According to a Grand View Research report, the HR analytics market is expected to reach $3.28 billion in 2030, registering a CAGR of over 14.8% from 2023 to 2030. This data proves the growing importance and investment in HR technologies that enable organizations to optimize their human resources effectively, driving better business outcomes and enhancing overall workforce satisfaction and productivity.

This blog aims to shed light on the various facets of predictive analytics in Human Resources, from its benefits and applications to challenges and real-world examples.

Understanding Predictive Analytics in HR

Applying predictive HR analytics effectively transforms how organizations approach their workforce management. Despite the abundance of HR data available, only about 17% of organizations globally use this data to refine their HR operations, which is simply a missed opportunity for many.

The essence of predictive analytics in HR lies in utilizing existing data to create models that predict future outcomes, revolutionizing HR decision-making with the help of data-driven facts.

Simply put, predictive analysis in HR involves examining data across various human resource functions such as recruitment, performance evaluation, and employee engagement. This process is aimed at identifying patterns and using these insights to anticipate future outcomes, enabling more strategic HR decision-making.

Predictive analytics in HR management apps goes beyond mere forecasting, offering deep insights into employee interactions with the company, such as assessing promotion readiness and predicting potential wait times for advancement.

By utilizing predictive analytics, HR management solutions empower informed decision-making, resulting in a highly engaged and satisfied workforce. This strategic integration of AI and analytics positions HR as a vital catalyst for organizational success, fostering a proactive approach and establishing a positive and dynamic workplace environment.

Benefits and Applications of Predictive Analytics in HR

Predictive analytics is revolutionizing the way businesses understand their workforce. It provides deep insights into employee behavior and future trends. Here are the benefits and use cases of predictive analytics in HR.

Improved Hiring Decisions

Utilizing HR predictive analytics, organizations can significantly refine their hiring processes. This technique allows firms to analyze historical data, identifying patterns that lead to successful hires. It helps HR departments forecast the potential success of candidates, ensuring a better match between job requirements and applicant skills. This strategic approach reduces the risk of costly hiring mistakes.

By employing HR predictive analytics, companies also gain insight into the traits of ideal candidates, streamlining the recruitment process. Ultimately, this leads to a more effective and efficient hiring strategy, saving time and resources while improving overall team performance.

Enhanced Employee Retention

Predictive analytics enables companies to anticipate which employees might leave and why. This foresight allows for proactive measures to improve retention rates. Through analyzing patterns in employee behavior and engagement, HR can develop targeted strategies to address concerns.

This approach not only enhances job satisfaction but also saves costs associated with recruiting and training new hires. Ultimately, leveraging predictive analytics fosters a more stable and engaged workforce, contributing to the organization’s long-term success.

Optimized Talent Management

With predictive analytics, HR can forecast future leadership gaps and prepare accordingly. This strategic planning ensures a continuous flow of leadership within an organization. This proactive approach aids in identifying areas where specific skills may be lacking in the current workforce, thereby guiding the development of targeted training programs. Predictive analytics plays a crucial role in this process, enabling the identification of high-potential employees for leadership roles.

By analyzing performance data and career progression patterns, HR can tailor development programs to individual needs. This not only boosts employee morale but also aligns personal growth with organizational goals. Such precision in talent management significantly enhances operational efficiency and competitive advantage.

Increased Workforce Productivity

Predictive analytics empowers HR to identify factors that boost employee performance and engagement. By analyzing data on work habits and outcomes, organizations can pinpoint productivity drivers. This insight allows for the implementation of targeted interventions to enhance workforce efficiency. For example, predictive models can suggest the optimal mix of team skills or the best communication strategies.

Such analytics also help in recognizing and mitigating potential productivity blockers before they impact the team. Tailored training and development programs, powered by predictive analytics, ensure employees are well-equipped to meet current and future demands. Consequently, this leads to a more motivated and productive workforce, directly benefiting the company’s bottom line.

Cost Reduction

One of the major benefits of predictive analytics in HR operations is significantly lowering the recruitment and training expenses. By accurately forecasting staffing needs, organizations can avoid the costs of under or overstaffing. This streamlined hiring process pinpoints the right candidates faster and more efficiently.

Furthermore, HR predictive analytics also aids in reducing turnover rates by identifying factors that contribute to employee dissatisfaction. Addressing these issues early can improve retention, thus saving on the high costs associated with replacing staff. Moreover, tailored training programs, informed by predictive data, ensure resources are invested wisely, enhancing employee skills without unnecessary expenditure. This strategic approach to HR management fosters a more cost-effective and efficient workforce.

Accurate Cultural Fit Assessment

Cultural fit assessment is crucial for maintaining a cohesive work environment and serves as one of the valuable benefits of predictive analytics in HR. Predictive HR analytics plays a key role in this process by evaluating potential hires’ compatibility with the company culture. This approach goes beyond traditional recruitment methods, analyzing data from various sources to predict how well a candidate will integrate into the team.

By ensuring a good cultural fit, organizations can improve employee satisfaction, teamwork, and retention rates. Predictive analytics helps HR to make more informed decisions, aligning new hires with the values and behaviors that define the company. This strategic alignment contributes to a more productive and harmonious workplace.

The strategic implementation of predictive analytics in HR not only enhances operational efficiency but also fosters a more engaged and satisfied workforce. It represents a significant leap forward in the evolution of human resources management.

[Also Read: Big Data Analytics – What it is, why it matters and how to implement it in business.]

Future Workforce Trends Forecasting

Understanding and preparing for future workforce requirements is essential for sustained organizational growth. Through the analysis of extensive data sets, HR can predict shifts in employment needs and skill requirements. This foresight allows companies to proactively adjust their talent acquisition and development strategies. This predictive capability is one of the major HR analytics benefits, enabling organizations to stay ahead in a rapidly changing market.

By identifying emerging trends, companies can ensure they have the right talent in place to seize new opportunities. Such strategic planning minimizes gaps in capabilities and positions the organization to respond effectively to future challenges, maintaining a competitive edge in the industry.

Salary and Benefits Optimization

Salary and benefits optimization is crucial for attracting and retaining top talent. Utilizing predictive analytics for HR, companies can tailor compensation packages to meet the expectations of current and potential employees. This approach analyzes market trends, internal equity, and job performance data to ensure competitive and fair compensation strategies. By identifying what motivates employees, HR can allocate resources more effectively, enhancing job satisfaction and loyalty.

This not only helps in reducing turnover but also positions the company as a desirable employer. Predictive analytics thus plays a key role in creating a balanced and attractive salary and benefits framework.

[Also Read:  Role of Predictive Analytics in Closing Business Gaps. ]

Streamlined Diversity and Inclusion Analytics

Predictive analytics enhances diversity and inclusion initiatives by providing actionable insights derived from data analysis. The applications of predictive analytics in HR enable organizations to assess and improve their diversity initiatives. By analyzing workforce data, companies can identify areas of bias and develop strategies to address them. This approach helps in fostering an inclusive culture that values diversity at all levels.

Predictive analytics provides insights into the effectiveness of diversity programs and highlights opportunities for growth. It ensures that efforts towards inclusion are not just well-intentioned but are backed by data-driven decisions. Ultimately, this leads to a more diverse, inclusive, and productive organizational environment.

Reducing Biases in HR Decisions

Predictive analytics offers a data-driven approach to HR, significantly reducing subjective biases in decision-making. By relying on comprehensive data analysis, HR professionals can make more objective and fair decisions regarding hiring, promotions, and performance evaluations. The benefits of predictive analytics in HR are particularly evident in creating a more diverse and inclusive workplace environment.

This method allows for the identification and correction of any unintentional biases in recruitment and management practices. Furthermore, it ensures that talent recognition and development are based on measurable performance indicators and potential, promoting a culture of meritocracy. Ultimately, this leads to more equitable and just HR practices across the organization.

Examples of Companies Using Predictive Analytics in Their HR Operations

Businesses are rapidly harnessing the power of advanced data analysis to revolutionize their HR practices. This quiet shift towards data-driven decision-making has led to significant improvements in various HR functions. Here are the examples of predictive analytics in HR.

HP

HP has been a pioneer in leveraging data to address its employee turnover challenges. With a vast employee base, the company experienced turnover rates as high as 20% in certain sales divisions, leading to increased recruitment costs and productivity losses. In response, HP developed a “Flight Risk” score using predictive analytics in recruitment, aiming to forecast the likelihood of employees leaving.

This initiative enabled targeted interventions by providing managers with insights into the factors contributing to turnover, such as the impact of promotions without corresponding pay raises. As a result, HP’s strategic use of predictive analytics not only enhanced its retention strategies but also achieved substantial cost savings. The company ultimately conserved an estimated $300 million.

Xerox

Xerox Corp. revolutionized its hiring process by integrating HR predictive analytics to tackle high attrition rates in its call centers. The company discovered through analysis that traditional metrics like work experience had little effect on reducing turnover. Instead, personality traits such as curiosity significantly influence an employee’s likelihood to stay.

By shifting their recruitment focus to personality assessments over work history, Xerox saw a notable decrease in attrition. This strategic change, implemented after a successful half-year trial, resulted in a 20% reduction in turnover, demonstrating the power of predictive analytics to inform and refine HR processes effectively.

Google

Google dramatically transformed its hiring process with the aid of data analytics in human resources. Initially, candidates underwent up to 25 interview rounds, a method requiring extensive resources and time. However, a thorough analysis indicated that merely four interviews could predict a candidate’s suitability with 86% accuracy.

This insight led Google to significantly reduce the number of interviews, streamlining their recruitment process. As a result, the median time to hire was cut by 75%, dropping from 180 days to just 47. This strategic application of data analytics not only optimized Google’s hiring efficiency but also showcased the profound impact of analytical tools in refining HR practices.

Credit Suisse

Credit Suisse has significantly reduced employee turnover by employing predictive analytics for HR. The financial giant analyzed over 40 variables to distinguish between employees who stayed and those who left. Their refined predictive model focused on critical factors such as team size, managerial performance, promotions, and demographic details to predict departures within a year accurately.

Armed with this information, Credit Suisse equipped managers with the skills to engage and retain potential high-risk leavers effectively. This strategic application of predictive analytics has led to annual savings of approximately $70 million, showcasing the substantial financial benefits of data-driven HR practices in reducing turnover and retaining valuable talent.

These real-world applications showcase the transformative impact of predictive analytics on HR. By adopting such technologies, companies are effectively enhancing their human resource processes and gaining significant returns.

Challenges of Implementing Predictive Analytics in HR

Implementing predictive analytics in HR introduces a powerful tool for strategic decision-making. However, it also brings several hurdles that organizations must navigate. Here are the challenges of predictive analytics in HR.

Data Privacy Concerns

Data privacy concerns are paramount when deploying predictive HR analytics within an organization. With the collection and analysis of employee data, adhering to strict data protection regulations like GDPR, CCPA, etc., is essential. These regulations ensure that personal information is handled securely and used ethically. Organizations must navigate these legal frameworks carefully to maintain trust and confidentiality.

Implementing robust data privacy measures and transparent policies is crucial for mitigating risks associated with data breaches. By prioritizing employee privacy and regulatory compliance, companies can leverage predictive analytics effectively while safeguarding sensitive information and upholding their ethical obligations.

Bias in Data and Algorithms

Addressing bias in data and algorithms is crucial for effectively using predictive analytics for human resources. It’s essential to ensure algorithms are free from historical biases and inaccuracies. These biases can affect decision-making processes, leading to unfair outcomes in recruitment, promotions, and performance evaluations. To combat this, HR departments must implement measures to regularly audit and update their predictive models.

Ensuring diversity in the data used for training these models is also vital. By acknowledging and actively working to eliminate biases, organizations can make fairer and more accurate predictions, ultimately leading to a more equitable workplace.

Integrating Disparate Data Sources

Integrating disparate data sources poses a significant challenge in using predictive analytics for HR. Organizations often store data in various formats across multiple systems. Harmonizing this data to create a cohesive analytics framework requires sophisticated data integration techniques. Without seamless integration, the full potential of predictive analytics cannot be realized. This process not only demands advanced technological solutions but also a deep understanding of data structure and quality.

Ensuring consistency and accuracy across these sources is paramount for drawing reliable insights. Overcoming this hurdle is crucial for HR departments aiming to leverage predictive analytics for strategic decision-making and workforce optimization.

Adherence to HMRC Compliance

When incorporating predictive analytics into HR operations in the UK, it is crucial to meticulously adhere to HMRC (Her Majesty’s Revenue and Customs) regulations, particularly concerning taxation, National Insurance contributions, and statutory payments. Failure to comply with HMRC standards can result in legal penalties and reputational damage for organizations.

Businesses seeking to incorporate predictive analytics into their HR management softwares may benefit from partnering with an app development company specializing in HMRC compliance. These firms can help businesses navigate the intricate landscape of HMRC regulations specific to HR operations and predictive analytics initiatives.

The partnership can further facilitate regular reviews and updates of predictive analytics processes to stay aligned with any changes in HMRC regulations. Taking a proactive approach to effectively utilize predictive analytics in optimizing their HR solutions can help them avoid potential legal and regulatory issues.

Skill Gaps in HR Analytics

Skill gaps in HR analytics present a significant hurdle for organizations. The lack of analytical expertise within HR teams limits the effective use of data-driven approaches. Addressing this issue is crucial for the successful implementation of predictive analytics for human resources. Companies must invest in training and development to equip their HR professionals with the necessary skills. Bridging these gaps will enable organizations to fully leverage predictive analytics, enhancing decision-making within HR functions.

Despite the hurdles, the benefits of integrating predictive analytics into HR practices are undeniable. Overcoming these challenges is essential for harnessing the full potential of this technology, leading to more informed and effective HR strategies.

Bring Predictive Insights to HR with Appinventiv

At Appinventiv, we are dedicated to transforming HR operations by integrating predictive analytics into core processes. Our innovative custom solutions are designed to enhance decision-making, streamline recruitment, and improve employee retention rates.

By leveraging advanced data analysis techniques, we help organizations unlock the future of HR analytics, enabling them to predict trends, understand workforce dynamics, and make strategic decisions with greater precision. Our expertise in delivering data analytics services allows HR departments to forecast potential challenges and opportunities, ensuring they are always one step ahead.

With Appinventiv, businesses can harness the power of data to optimize talent management, reduce turnover, and drive organizational success. Our commitment to innovation and excellence makes us the ideal partner for companies looking to bring predictive insights to their HR functions.

Our experts recently revolutionized the business operations of a global firm by integrating predictive analytics into their data analytics platform. By leveraging advanced technologies, we streamlined data storage and analysis on the cloud, eliminating unnecessary data clusters and ensuring real-time analysis.

Get in touch with us to explore how our HR management solutions, powered by predictive analytics, can transform your HR strategies and help you create a thriving workplace environment.

FAQs

Q. How is predictive analytics used in HR?

A. Predictive analytics in HR is used for a variety of purposes, including recruitment optimization, employee retention strategies, performance prediction, and workforce planning. It analyzes past data to forecast hiring needs, identify potential turnover, and predict future performance levels. This allows HR professionals to make data-driven decisions, enhancing efficiency and effectiveness across HR functions.

Q. How can HR benefit from predictive analytics?

A. HR benefits from predictive analytics through more informed decision-making, improved hiring processes, enhanced employee retention, and optimized talent management. It enables HR to anticipate future trends, adapt strategies accordingly, and align workforce planning with organizational goals. This results in a more engaged, productive, and stable workforce, contributing to the overall success of the organization.

Q. How can predictive analytics improve performance measurement?

A. Predictive analytics enhances performance measurement by forecasting future performance based on historical data. It identifies key performance indicators (KPIs) and trends, enabling managers to set realistic targets and implement strategies for improvement. By analyzing patterns in employee productivity, predictive analytics helps in recognizing areas for development, ensuring targeted interventions that boost overall performance.

As per the report of Fortune Business Insights, the global sports analytics market is on an upward trajectory, forecasted to reach from $3.78 billion in 2023 to $22.13 billion by 2030, showing an impressive 28.7% CAGR.

The stats above demonstrate that sports analytics is valuable, taking center stage in the sports industry and allowing businesses to enhance game strategies and performance.

Successful sports management requires a holistic and strategic understanding of diverse operations, both on and off the field, in various sporting contexts. Hence, businesses leverage extensive analytical techniques for individual and team sports to support business decisions.

With the assistance of sports analytics, athletes, coaches, and management officials improve the quality of play, revenue, and injury prevention.

Additionally, state-of-the-art technologies such as AI and machine learning create immense opportunities for the growth of the sports analytics market.

This blog post aims to provide extensive and valuable information consisting of business opportunities and challenges of implementing analytics.

So, let’s uncover key considerations for a seamless transition.

Business Opportunities of Data Analytics in the Sports Industry

Implementing data analytics in the sports industry creates a range of business opportunities, assisting in delivering valuable insights for teams, organizations, and stakeholders. Here are some of the key applications of data analytics in sports:

Performance Optimization

Performance optimization in sports through data analytics consists of extensive analysis of player as well as team metrics to boost overall effectiveness. Leveraging data-driven insights enables teams to tailor training programs to individual strengths and weaknesses. For example, holistic and strategic statistical analysis of player movements and fitness levels aids coaches in determining areas of improvement. Real-time monitoring is highly advantageous in providing feedback and allowing dynamic adjustments during training sessions.

Injury Preventions and Health Management

The performance and longevity of athletes are dependent on holistic health management as well as injury prevention. Leveraging data analytics in sports allows a dynamic stance in comprehending and reducing potential health risks.

Data analytics in the sports industry provide positive workload management and continuous surveillance, allowing coaches to create personalized training programs.

With the assistance of data-driven insights, it’s possible to prioritize athletes’ long-term well-being by boosting performance.

Recruitment and Scouting

Identifying and acquiring top-tier talent is one of the biggest concerns in the realm of the sports industry. Data analytics in sports makes the recruitment and scouting process more efficient, insightful, and targeted by monitoring systems and performance measurement. It also aids in comparing player performance across leagues and competitors. Making accurate decisions about player recruitment, and reducing reliance on subjective judgments and traditional scouting methods are the significant impacts of data analytics in sports.

Ticket Pricing and Revenue Optimization

Ticket pricing and revenue optimization are major concerns for sports businesses. Data analytics is the guiding force for this strategic endeavor, allowing a deep dive into intricate fan behavior and preferences information to set optimal ticket prices.

Data analytics in sports analyses age, location, and buying behavior to understand the target audience that caters to competitiveness and accessibility.

Historical data provided valuable insights, unveiling patterns associated with specific events, rivalries, etc., and anticipating demand fluctuations. By evaluating pricing tactics across the sports landscape, organizations can efficiently position themselves in the market by considering competitors.

Merchandising and Product Development

Today, merchandising and product development have become the need of the hour for businesses, even this is one of the most valuable paths forward. By evaluating past purchasing and sentiment analysis, industry experts can gain profound insights and leverage them to gain a competitive advantage.

It also provides broader market trends, enabling brands to align with the latest trends. By leveraging sports analytics, businesses can tailor merchandise offerings to align with seasoned major sporting events, cultivating profound loyalty within the passionate sports community.

Sponsorship and Marketing Opportunities

The adoption of sports analytics brings a quantifiable narrative, attracting sponsors, maximizing revenue potential, and much more.

Tangible metrics, including social media interaction viewership, provide an understanding of the exposure and engagement the brand can gain through strategic collaboration.

Analytics felicitates in assessing the effectiveness of past sponsorship and marketing endeavors.

Challenges During the Implementation of Analytics in Sports

The emergence of analytics in sports not only comes with advantages but also presents a series of challenges. Let’s delve into the primary challenges the sports industry encounters during the process.

Data Quality & Availability

The quality and availability of the data is an essential aspect of creating effective sports analytics for businesses to meet their expectations. Businesses face challenges due to inaccurate, incomplete, and irrelevant data collection. This problem arises owing to data entry errors. , discrepancies in recording player actions, and many more. Technical glitches or incompleted recordings can severely affect the accuracy of conclusions.

In order to gain successful data quality and availability, it is vital to invest in state-of-the-art technology, meticulous data management practice, and a team of experts.

Resistance to Change

The traditional methodology often creates challenges as coaches, players, and staff may be hesitant owing to the trust they gained from their experience. Their deeply rooted conventional approaches restrict them from shifting towards data-driven decision-making.

By displaying data-centric success stories, extensive education and training can assist coaches and players in comprehending the significance of sports analytics.

Cost Consideration

Implementing data analytics in sports requires substantial upfront costs. Technology infrastructure, one of the most crucial pillars, demands cutting-edge analytics tools, databases, and computing resources, allowing businesses to handle the intricate demands of sports-centric data. Other factors also create challenges, such as strategic budgeting, data security, and talent acquisition & training.

Communication and Collaboration

Building effective communication and collaboration between data analysts and sports professionals is paramount. Data analysts and sports experts have time constraints and workload issues, which are lacking in regular communications and collaboration, ultimately delaying the sharing of insights and execution of recommendations.

Lack of data literacy is also a notable concern as sports professionals do not have the same level of interpretation skills as data analysts, which creates challenges for the team in grasping the nuances of analytics findings.

Exploring Analytics Applications Across Sports

Let’s comprehend the strategic and extensive role of data science in the sports industry that different companies execute to gain advantages.

Football is a game where data analytics is leveraged to comprehend player tendencies, evaluate player movements, and monitor physical metrics to avoid injuries. Analyzing an opponent’s performance and exploring flaws through sports analytics enable players to predict goals and choose the players for gameplan.

In cricket, data analytics has been adopted to make better decisions by identifying improvement areas and creating individual training programs.

Evaluating pitch types and analyzing batting averages allows baseball players to improve their overall game strategy.

With the help of data analytics, F1 Racing has exceptionally strategized its approach. The data-centric system helps in comprehending fuel efficiency, evaluating aerodynamics, and tire management.

The importance of sports analytics is evident in its capacity to revolutionize the game approach across a diverse spectrum of sporting disciplines.

Final Words

In this blog post, we discovered that the data-driven approach has become fundamental for the sports industry, revolutionizing team strategies, fan engagement, and predicting the result of an upcoming sports event.

We are a leading data science & analytical solutions provider company, generating extensive, actionable insights for business scalability.

Our data analytics services assist businesses in generating crucial insights through collecting, processing, evaluating, and interpreting data. Our robust tools, skill analysts, and well-defined strategy act as catalysts for business growth by identifying trends and creating opportunities to elevate.

FAQs

Q. What is sports analytics?

A. Data analytics in sports industry  illustrate the collection, analysis, and interpretation of data related to different aspects of sports, used to extract significant insights to improve decision-making, game performance, and player development.

Q. What are programming languages used in sports analytics?

A. Different businesses use different programming languages per their sports analytics project requirements. But SQL, C++, Java, and MATLAB are some of the prominent programming languages widely used for sports analytics.

Q. What algorithms are used in sports analytics?

A. Sports analytics employs linear regression, clustering algorithms, reinforcement learning, and neural networks to gain a competitive advantage.

Q. What are Market trends of data analytics in sports?

A. Increasing the use of cloud-based solutions, leveraging AI, developing new data sources, and player-tracking technology are some market trends of data analytics in sports that businesses leverage to meet their expectations.

While the outcome of the driving experience is that of contentment, the way vehicles reach that stage is a complex one. An extensive amount of intelligence is required for a car to drive on its own while keeping track of the speed, dodging traffic, and updating humans on the vehicle’s needs. An intelligence that involves several technologies and pieces of machinery coming together.

Achieving the level of automation and time-sensitive intelligence requires accessing and translating massive sets of data into action and insights, thus paving the road for big data in the automotive industry.

In this article, we are going to dive into the origin and role of automotive analytics solutions which goes beyond making the self-driving experience smooth and autonomous. But before we dive into the role of big data for autonomous driving, let us first answer a few questions that you will get asked when you have to detail the technology’s involvement in space.

• What amount of data is generated through a self-driving vehicle?

It is estimated that an autonomous vehicle generates over 4,000 GB data every day divided into separate sections like –

• Camera – 20-40 KB per second
• Radar – 10-100 KB per second
• Sonar – 10-100 KB per second
• GPS – 50 KB per second
• LIDAR – 10-70 KB per second.
• Is the current fleet of autonomous vehicles truly driverless?

In its current state, big data analytics in the automotive industry is completely involved in only till Level 2 of driving automation. A majority of cars that are operative in the self-driving space work in a way that the vehicle performs acceleration and steering while humans monitor all the tasks and take over control whenever they deem necessary.

There will be more than 30 million autonomous vehicles on the road by 2040

The Technicalities of Automotive Data Analytics

Big data and machine learning in autonomous driving runs on sensors built in the cars. The information that comes in from the multiple in-car sensors are processed and analyzed in microseconds, enabling not just a safe movement from point A to point B but also passing information about the road conditions, communicating with other vehicles, and informing the owners about vehicle issues.

In addition to these sensors, there is one other crucial component in the autonomous driving domain: the automotive data analytics software that helps store and analyze the data sets. The software, being connected to a network, passes information from the sensors to the cloud in a way that the response time to those conditions is instantaneous, especially after the introduction of 5G in automotive.

An AI-based self-driving car should have sensors, automotive analytics solutions, and a connection to a cloud server. Next, the car should know its location, for which it makes use of GPS. Together, the data that comes from the internal sensors, such as compasses and speedometers, defines the direction and speed.

Once a vehicle knows its location, it gets easy to know what is around it by using lidar and radar to localize itself inside this map. Here, elements like markers, signs, and other obstacles are taken into consideration.

Using the collected data, the driverless car builds strategies for different situations that may occur on the road. Additionally, data sharing between autonomous vehicles aids in avoiding traffic jams, reacting to emergency situations, and taking weather conditions into account.

To sum up, big data in the automotive industry can be used in the following ways –

• Look and sense – get information; plan and respond on the basis of the collected data
• Map the surroundings in detail
• Identify speed, range, and distance through lidar and cameras
• Communicate with other cars to share information.

Now that we have looked into the core of analytics in the automotive industry, let us look into some role of big data in autonomous vehicles through the use cases.

Role of Data Analytics in the Automotive Industry

Big data analytics in the automotive industry has grown to unimaginable levels. Right from powering self-driving cars to building intelligent traffic systems, AI in its different forms has changed how we travel and interact with vehicles.  Now while the role of big data in autonomous vehicles can be seen in the manufacturing, price setting, and customer experience space, for this article, we will be looking into the contributions of autonomous driving through data analytics.

Sensing and Perception

Self-driving cars use several sensors like radar, lidar, camera, etc., to collect data about their surroundings. The data then gets processed and analyzed through big data algorithms for creating a detailed environment map to identify objects like traffic lights, other vehicles, and road signs.

Decision Making

Autonomous cars use data analytics in the automotive industry to make real-time decisions on the basis of data that they gather from the in-car sensors. For example, if the car detects another vehicle getting too close, it will use big data to choose the best course of action, either slowing down or stopping.

Predictive Modeling

The industry has been using big data and machine learning to predict others’ behavior in autonomous driving. The technology combination helps the vehicle anticipate movements and problems that might happen with the car and then take timely action to avoid them.

Natural Language Processing

Another use case of automotive industry data analytics can be seen in the cars being equipped with voice recognition technology which enables the passengers to communicate with the car through their natural language. The technology, in turn, helps the car understand and respond to the user’s spoken commands.

The reasons behind growing instances of big data applications in the automotive industry are evident. But at the same time, we cannot negate the fact that automotive analytics solutions are not getting incorporated beyond Level 2. Let us look at some challenges that need an industry-wide solution.

Challenges of autonomous driving through data analytics

The expectations from big data in the automotive industry are growing exponentially, especially as the automotive industry is planning to make Level 4 and 5 mainstream in the coming years. However, there are a number of complications that are yet to be addressed. Let us peek into them.

1. Varied data sets – For predictive analytics in the automotive industry to work, the blend of supervised and unsupervised datasets should be proper and repetitive. However, when driving, there are multiple instances where accidents happen because of nobody’s fault. Additionally, numerous events are extremely rare in nature. So the challenge is to create patterns from several of these isolated events.
2. Data storage – A recent report by Western Digital found that the storage capacity per vehicle could reach 11 terabytes by 2030. To accommodate this massive amount of data, it is critical for businesses to bring data storage and processing from the cloud to the vehicle itself via satellite transmission.
3. Security concerns – Since data-driven automotive vehicles collect data from the public where the expectations from privacy is limited, the users are less likely to be in control of their data, as they won’t be able to opt out of data collection.

Owing to these industry-level challenges in the adoption of big data for autonomous driving, the market expectation is that the self-driving space will reach its maturity in Level 2 before the exploration work starts on Level 3 and above.

Standing at today, there’s a need for automotive data analytics services that would help automated vehicles with this roadmap. At Appinventiv, we specialize in working with automotive analytics solutions that excel in collecting the massive amount of data and routing them to the system which needs them. Moreover, our Data Analytics Solutions providers aggregate and enrich the mass of data by organizing them in a digestible format for the vehicle to use.

FAQs

Q. How is big data being used in automotive analytics?

A. The role of big data in automotive data analytics can be seen in multiple facets. Right from making self-driving experience organic to designing future-ready vehicles, and finalizing the price range, the technology is fast becoming pivotal for the space’s existence.

Q. What are the benefits of data for reliable autonomous driving experiences?

A. The benefits of automotive analytics driven by a massive amount of datasets can be witnessed through – improved sensing and perception, faster decision making, predictive modeling, and natural language processing.

Q. What are the challenges in implementing big data analytics for autonomous driving?

A. The limitations that surround implementation of advanced analytics in automotive industry are majorly driven by industry-grade challenges such as presence of multiple isolated events, security concerns, and absence of a data storage mechanism that can store and process terabytes of data.

The retail sector faces difficulties such as a lack of data security and confidentiality. The inability of the companies to implement the insights acquired from analytics into their business as well as the lack of a skilled team to conclude are further issues. 

However, data analytics can assist in improving customer retention and boosting brand recognition by providing customer satisfaction with the right skills and precise inference from retailers. Data analytics investment may be fairly assumed to remain a crucial component of the retail industry as technologies come to the fore.

In comparison to the last two decades, the retail industry is progressing significantly.

Physical stores are losing business to online retailers as buyers choose online shopping more often. The retail sector had previously restricted its concentration to marketing and customer service. Now, the emphasis is on gathering data, analyzing it, and improving the marketing strategy using the insights. The need to be able to move quickly based on data-driven insights has never been stronger.

Retailers may design new go-to-market plans that are more successful at engaging customers by using data science and analytics solutions to convert their data into actionable insights. Data analytics use in the retail industry can boost brand awareness and strengthen customer loyalty by ensuring client satisfaction. 

Data analytics for retail businesses

Traditional brick-and-mortar retailers have been radically overhauled by data analytics, which has swept the industry off its feet. To assess consumer needs, enhance supply chain administration, and boost profits, it has introduced a new perspective. Additionally, it seeks to optimize revenues by maximizing brand strategy, discount coupons, and ensuring that excess inventory loss is kept to a bare minimum. 

Furthermore, data analytics aids in evaluating and comprehending each store’s sales trends and identifying its consumer’s purchase behavior. Businesses will be able to fill their stores with favorite products and promote goods and services thanks to this pattern recognition. Businesses can also retain clients by offering them incentives or promos. 

Nowadays, a lot of businesses provide membership plans wherein a customer’s transactions are all connected to a single profile, whether they were made in-store or online. This helps companies to thoroughly understand each consumer and effectively address sales.

How advanced data analytics is transforming the retail industry?

The modern accelerator that has propelled business leaders to their advantageous position is data analytics. The retail analytics market is anticipated to grow at a CAGR of 19.1% from 2020 to 2027, reaching USD 23.8 billion. 

Now, one thing is for certain – data analytics use in the retail industry has a promising future. Additionally, there is a significant role of data analytics in the retail industry. 

Acquisition of data

In the retail sector, rewards cards are among the most common methods for collecting big data. Financial transactions, network connections, customer log-ins, and other techniques are now used to acquire it as well. As more information is collected, retail companies can utilize actionable insight to analyze the past influx and outflow of consumer expenditure to anticipate potential purchases and provide customized suggestions.

Spending forecasting

Based on your previous searches and transactions, companies like Amazon makes recommendations for you based on customer information. Their recommendation algorithm examines more than 150 million profiles and generates 35% of their sales. The online firm has made significant revenues as a result of this.

Tailoring the consumer experience

Data science and advanced analytics in the retail present an opportunity to enhance customer relationships. To keep its customers happy, companies like Walmart monitor transaction details. 

Demand prediction in retail

To forecast future developments in the retail market, several algorithms now take into account social media and web browsing habits in addition to data analytics. The atmosphere is possibly one of the most fascinating sample points for sales forecasts.

With the help of the Weather Report, companies like Pantene modified product suggestions for customers by considering climate patterns. To properly deploy their resources during the various seasons of the year, retailers use commercial prediction and retail estimates.

Analyzing customer experiences

The trajectory of a consumer is not continuous. From research to purchase, the cycle across channels is criss-cross. The only way to understand the customer experience and enhance users’ experiences is via the deployment of big data. Retailers who use analytics solutions can get responses to inquiries like: Where do shoppers look for product pages? Where do you miss them, precisely? What would be the best strategies to approach them and encourage them to buy?

Why should your retail business invest in advanced data analytics?

Today, data analytics use in the retail industry offers not only specific customer insights but also data on the company’s operations and processes with opportunities for improvement.

Here are the top reasons why retail companies should scale their investments in advanced data analytics.

Personalized customer interactions

Businesses can differentiate themselves from their rivals by personalizing their services.

Retail businesses may monitor data at every stage of the purchasing process with the aid of data analytics. Additionally, they track the consumer’s prior transactions. Customized conversations targeted to the customer using this data are more effective than standard marketing techniques.

Price optimization

The growth and fall of demand can be predicted to a great extent using pattern recognition. Businesses have discovered through predictive research that when a product’s pricing is gradually lowered from the point at which demand declines, demand increases yet again.

Appinventiv’s comprehensive data science solutions have increased customer operational effectiveness by 30%.

Enhanced client experience

Data analytics seeks to provide each customer with individualized service, from product recommendations to transactions. As a result, customers stay with the company longer.

Data analytics also improves customer satisfaction by evaluating the things that consumers buy in tandem and making suggestions to them so that they buy a combo of products at a discounted price.

Cross-selling sales are generated by data analytics algorithms, which aid merchants in increasing their revenue and hence enhance user satisfaction.

Market trend forecasting

The majority of brands offer festive or end-of-season deals because the data support their profitability. To analyze the market’s attitudes, marketers employ sentiment analysis. Even the top-selling products can be predicted using data collected by sophisticated algorithms for machine learning.

User loyalty

Data analytics can be used to find customers who aren’t engaging with your business but who can become long-term consumers or regular customers in the future.

This makes it easier for the retailer to offer special rewards and deals to attract and retain customers.

Increased ROI

Businesses can uncover opportunities with a high ROI through data analytics investments. To assess how customers respond to marketing campaigns and determine their propensity to make purchases, predictive analysis can be used.

Inventory control and demand forecasting

Retail businesses that use data analytics can better understand the needs of their clients and emphasize product categories with strong demand. Data-driven conclusions help businesses estimate demand and maintain inventories appropriately.

Successful retail spaces

Investment in data analytics helps the business identify the locations where customers spend the vast majority of their attention.

Additionally, analytics offers data on demographics, people’s standard of living, and market conditions. This is quite helpful in deciding where to place their retail business so they can attract the most clients.

Strategic and data-driven decision-making 

Businesses rely on data to make wise decisions about their products and clients by employing a single, reliable source of information.

Top 5 features to look for when selecting a data analytics tool

In this section, we are discussing the key features that you must look for when selecting a data analytics tool for your business. Getting an idea of the features you need will help you choose the right tool as per your business needs and requirements. So, let’s get started.

Regular and individualized competitor tracking

Control over the data’s origin is essential. Controlling who and what you see in the market is crucial since it serves as the foundation for your expertise. A set of data must contain at least of three main sources to be evaluated, with 5 being the ideal number. These sources should include your opponents and other retailers who carry the same kinds of items and advertising strategies that you do through your offline or online business.

The capacity to dig deeply into the data

Taking a broad view of the marketplace while also being able to delve into minor specifics leads to the most successful strategies in prosperous sectors. All players can make critical choices at the optimal time when they have data readily available at all market stages, encompassing supply chain KPIs, merchandise and guide selections, and store pricing and discounting. The retailer can make smart decisions by taking cognizance of where items are placed and if they are selling through or not.

Ranking of adversaries in real-time

Pricing adaptability can be extremely difficult in a market environment where individuals in the post-pandemic are price-sensitive. Today, it is becoming easier for buyers to compare rates and browse around for the greatest deals, both in person and online.

Thus, real-time pricing strategy modifications can be made by you and your team by comparing your rates against those of your rivals. This relieves you of the burden of a labor-intensive and time-consuming manual investigation into the pricing of your rivals, a process that frequently reduces productivity and eventually makes your attempts at revaluing worthless.

Cross-sell recommendations and on-site suggestions

The predictive algorithm recommends products that may be of relevance to consumers while they explore an online marketplace, searching for and picking products to purchase depending on their browsing habits and market dynamics in the analytics solution. 

This can comprise the latest releases, lines of products that aren’t performing successfully, and products that consumers are browsing or adding to their shopping carts. To optimize the effectiveness of product up-selling and cross-selling, the algorithm might use a variety of strategies such as product grouping and discounting.

Notifications and action prompts

To save resources and effort, all while maximizing pricing improvements, these capabilities simplify the process of proactively establishing retail prices and stock assortments among subcategories in real time. It all ultimately boils down to being among the first to comprehend and reap the benefits of a situation as it unfolds when making the most appropriate judgment based on forecasting data and retail analytics. Given the number of regulated branches and product items, physically updating the selection of goods spanning multiple platforms in a time-sensitive fashion can be nearly impossible.

The aforementioned qualities, in addition to a highly flexible and user-friendly interface, should be carefully taken into account when selecting a retail data analytics solution. Developing a smart retail marketing and sales strategy and optimization of supply chains all rely on the efficient use of real-time data analytics. That analysis yields valuable interpretations, recommendations, and mechanization, which will eventually have the greatest positive impact on your bottom line.

How can Appinventiv help your retail business with data science and analytics solutions?

Retailers are always looking for opportunities to gain an advantage over their rivals, including more effective and efficient customer experiences, improved methods of contacting consumers, and opportunities to predict customer needs. 

Appinventiv assists your business in a variety of ways with its data science and analysis solutions. Success hinges on getting the most utility possible out of data, and Appinventiv does just that with its wide range of data science and analytics solutions:

Data Analysis Consulting: The expert analytics consulting services provided by Appinventiv help turn data into relevant insights, ensure organizational performance, and provide you with a market edge.

Database Management: Whether your database engine is on-site or in the cloud, our experts can assist you in getting your data where it needs to be. We evaluate your user’s requirements to create a unique data warehouse,

Analyzing Supply Chains: With the aid of our supply chain optimization solutions, you may boost revenues and lower expenses.

Through in-depth market analysis, we assist you in comprehending the needs of your customers and the ambitions of your opponents.

For instance, we provided a robust ERP solution to the world’s largest furniture retailer, IKEA, by deploying location-wise kiosk solutions for stores located at different locations, with every store having its own individual server. The solution provided is now being expanded to other stores in UAE and is considered the biggest source of ROI.

Innovation and challenges are the two drivers that push our experts to provide solutions that are unique to every client and their requirements. We pride ourselves in providing solutions that are customized as per the client’s needs. So, get in touch with us today!

FAQs

Q. How are data analytics applied in retail businesses?

A. Advanced analytics in retail enables enterprises to develop customer suggestions based on their past purchase history, contributing to a more tailored buying experience and better service to customers. In addition to participating in trend forecasting and strategic decision-making based on market research, these enormous datasets also aid in trend detection.

Q. What advantages do retail data analytics offer?

A. The following are the top 5 advantages of retail data analytics:

• Insights into customer behavior
• Enhancing retention
• Administering the basics
• Optimizing in-store management 
• Boosting return on the investment

Q. What are the must-have features of a retail analytics tool?

A. The must-have features of the retail analytics tool are:

• Capabilities for assessing client behavior data
• Cross-selling and on-site recommendations
• Alerts and activity events
• Real-time reference for competitor pricing
• Retail forecasting and analytics

Q. What are the advantages and pitfalls of data analytics?

A. Investment in data analytics offers both advantages and disadvantages.

Advantages

• Enhanced decision-making
• Improved client experience
• Price optimization

Disadvantages

• Data Protection
• Lack of communication with teams
• Poor data quality

As an essential part of the entire business data center and warehouse, the data support infrastructure consists of power, cooling, security, monitoring and measurement systems, all designed to provide assistance in maintaining core business data operations.

According to Grand View Research, the global market for data center support infrastructure was valued at $2.1 billion in 2021 and is expected to reach $7.79 billion in 2030, witnessing a CAGR of 15.9% from 2022 to 2030. The growth of modern data infrastructure is fueled by the rapidly rising volumes of data that need to be stored and managed in an efficient manner.

Besides, there is a continuous demand for cloud data storage, online mobile cloud computing services, data visualization and big data analytics leading to sustainable market growth of data infrastructure solutions.

According to Gartner, by the year 2027, half of the essential applications used by enterprises won’t be hosted on centralized public cloud platforms.Instead, these critical applications will be found in private clouds, on-premises data centers, edge computing environments, or decentralized cloud infrastructures. 

This shift implies that having a robust and adaptable data infrastructure is crucial for effectively managing data assets and supporting business processes in a changing technological landscape.

 So what are we waiting for? It’s time we take a detailed look at ;

• What is an integrated data infrastructure?
• Why does your business need a modern data infrastructure?
• Data infrastructure types for your business
• Top five data infrastructure trends for 2024 and beyond, shaping the future of your business

What is An Integrated Data Infrastructure for Business?

A data infrastructure consists of data assets and can be seen as an ecosystem of processes, technologies and systems to collect, store, maintain and distribute data for business purposes.

In short the accurate data infrastructure definition would be:

“Data Infrastructure consists of Data Assets, both structured and unstructured, supported by Process, People and Technology”.

Here are some of the primary characteristics of modern data infrastructure:

• An integrated Data infrastructure involves- technologies and standards to curate and provide access to all business data resources
• Guidance and policies to inform the use and management of data assets and its systems
• Data sets, identifiers and registers to analyze customer data ecosystem and business data ecosystem individually

While many organizations use a software-defined infrastructure (SDI) to support the dynamic IT needs in businesses, SDI has its own backlogs. One of the most popular data infrastructure trends is to implement ADI, also known as, Artificial Intelligence defined Data infrastructure.

ADI is an intelligent upgrade of Software based data infrastructures stacked up with modern AI/ML algorithms to backup your business data in every possible manner.

We will talk about more data infrastructure trends in detail, however, for now, let’s take an overview on why your business is in immense need of data infrastructure management solutions.

Why is Data Infrastructure Important For Your Business?

A strong data analytics infrastructure strategy ensures enhanced productivity and efficiency, makes collaboration with resources easier and allows easy access of information remotely, provided that you have properly implemented data infrastructure systems in your workflow.

Some of the primary benefits of using data infrastructure solutions are:

1. Improved data sharing and data security

Data infrastructure helps businesses share data quickly, effectively and securely across various organizations. You get quick solutions to database query, moreover, the end users are able to speed up the work process.

For instance, with an integrated data infrastructure, your sales team would get resourceable data insights where they can prospect sales accurately in the provided period.

2. Effective data integration

The need for a modern data ecosystem is significant since it helps promote a more accurate picture of your operations and data management. Data infrastructure determines how one segment of your business is affected by other segments.

Therefore, you can integrate various data standardization options such as APIs and integration, data processing, networks and more to enrich your data sets.

3. Consistent and reliable data

Data inconsistency is by far the most common challenge to every business vertical. Data infrastructure with the help of certain data quality tools makes sure that the accurate view of data is shared throughout the organization.

4. Data complied with privacy regulations

Data infrastructure ecosystem provides a better framework for the enforcement of privacy and security policies. By orchestrating data in a unified manner, you can centrally manage data privacy and security with lower risk of regulatory violations.

5. Better decision making

Decisions made on data are good as information used. Data infrastructure systems help provide data quality initiatives and better data management with reduced operational costs.This ultimately leads to better organizational decision making.

This is probably why your business needs a reliable data infrastructure. If you are convinced to deploy a data infrastructure ecosystem to workflow, the common deployment options are on-premise deployment, cloud deployment and hybrid deployment.

As for the banking and supply chain industry, their workflow is moving further to cloud native and serverless infrastructure for data hosting and reduced workload.

However, it’s best to know what type of data infrastructure your business needs before you deploy the ecosystem across your enterprise.

Types of Data Infrastructure For Businesses To Implement

Data infrastructure can be classified into various categories based on the purpose and objective of the data used for your business. Following are some of the most common types of modern data management infrastructure systems used by enterprises to scale their everyday work processes:

Data access – Data access is an interface to move, copy, retrieve and modify data from IT systems to the requested access query. It is basically a medium where users get the required data in an authenticated and systematic method.

Data Ingestion –  Data ingestion is the infrastructure where data is transported from one or multiple resources to a location where the data can be used for analysis and manipulation. The system is nothing but routing the data into the right direction.

API integration – As mentioned above, APIs are commonly used data infrastructure systems that process requests for seamless distribution of data through multiple devices. APIs communicate and interact with the backend systems along with our programs, devices and applications.

Data storage and processing – Data storage refers to the physical or virtual storage of data through various equipment and software.

An interface to control the data for effective management is called data processing. The process involves transforming raw data into insights. The collective process of both is called data integration.

Databases – Databases are a systematic and organized collection of data that can be accessed only through authentication. Databases, when in enormous amounts, are managed by large database management systems.

Networks – Networks infrastructure acts as a connection between company’s systems, mainframes, servers, network devices, peripherals etc. to share data efficiently.

Data security – Data security infrastructure involves systems, software, hardware and applications to protect data from unauthorized access. The ecosystem includes data encryption, hashing, tokenization for comprehensive protection across all digital platforms.

You may also want to read;

How to prevent data breach on your app and

Six proven ways to avoid data leakage in your Android app

Data centers – Data centers have the infrastructure or a dedicated space for storage of business applications and data. For instance, router, storage systems, firewalls, application delivery controllers etc.

Data visualization – data visualization is the representation of data in graphical form including dashboards, charts and maps for easy communication of insights.

Data migration – Data migration is performed with the cloud platforms where the internet based business data is stored and processed.

All these data processes are used by businesses of every scale and size to maintain and optimize their data silos. If you are still not convinced on how these data infrastructure systems can help you out; here’s an infographic depicting data infrastructure examples and use cases by tech giants.

Apart from these tech giants, data analytics is also a hit in the telecom industry. Discover how Appinventiv helped a leading telecom company build a customer centric data infrastructure platform increasing the data quality by 85%.

This is all we needed to know about data infrastructure before moving to the data infrastructure trends. Speaking of data trends, businesses are becoming smarter with integrating and managing enterprise data that is accessible, authenticated and governed.

For better data infrastructure management, organizations are placing emphasis on their backed systems in order to gain more customer facing assets and work better. Therefore, if you are looking to move beyond your business data and sustain your business process, here are top five data trends you can follow.

Top Five Business Data Infrastructure Trends To Follow in 2024 and Beyond

Data infrastructure trends are all about migrating to efficient data solutions, cleansing data, making better use of data assets and removing extensive data silos. Now that all industries and verticals are leveraging modern data processes, here are a few data infrastructure trends accelerating and shaping businesses:

1. Data fabric

Data fabric is the foundation for distributed enterprise. With the constant evolution of digital channels and online sales, there is a complex and diverse ecosystem of devices and applications. Businesses are now creating data fabric to drive enterprise wide data and analytics and automate all data processes.

Data fabric allows businesses to choose the most appropriate approach to manage their data which ultimately results in reduced time of delivery. Many studies have stated that “data fabric technology takes data virtualization to the next level by automating data management functions using AI and analytics.

2. Data mesh architecture

Data mesh architecture has become more enticing than ever. With the organization growing in size and complexity, usually central data teams are asked to deal with a wide array of functions. This makes it difficult to understand the data requirements of other cross functional teams, hence the businesses are unable to provide the right set of data products.

Data mesh is a decentralized architecture for data analytics that removes the bottlenecks and takes closer data decisions. In the upcoming years, if your business involves distributed data environments, data mesh is what you need for unified infrastructure to share your data products.

3. Decision Intelligence

Decision intelligence approach makes an inroad for business wide decision support. Businesses acquire vast amounts of data and leverage the information to drive successful business outcomes. With the decision intelligence approach, the data insights, regular dashboards, BI platforms are augmented with AI/ML and analytics capabilities to help support higher decision actions.

4. Monolithic architecture

Businesses have already embraced composable data and analytics architecture to empower data consumers. Although monolithic architecture is already a thing of the past, the process is about to be acquired by businesses in even small footprints. If your data is spread across cloud platforms, physical and regional servers and data center boundaries, it is practically impossible to have a central location to consolidate all data silos.

With composable data architecture, you have a leverage with low code or no code infrastructure providing you desired data management tech stack and making your business less dependent on IT.

5. Small and wide data analytics

As we know, modern technologies are transforming the way businesses operate. However, to be successful and relevant, businesses also need traditional analytics, BI and cloud and possible data tools. With wide data analytics, the infrastructure combines structured, unstructured and semi-structured data from various sources for analytical purposes and gives pace to the dataflow across the organization.

Small and wide data analytics is a new concept yet to be adopted, however, within months we will be able to see enterprises gaining better traction with this concept.

We believe we are still in the very early stages of a revolution in modern data analytics infrastructure. Just as AI and cloud computing changed the way we work, harnessing the modern data infrastructure trends can transform every business vertical and industry with its integrated and centralized approach. All you need is data infrastructure management services to guide you through the journey.

How Can Appinventiv Help You With Modern Data Infrastructure Solutions?

Appinventiv is a leading data management company specializing in data analytics solutions for businesses of every scale and size. Our rich pool services include analytics consulting, data warehousing, Big data, Data visualization, Business Intelligence and Supply chain analytics etc.

Our experts are known to follow an agile methodology and result oriented approach, offering end-to-end data solutions to your business.

Talk to our data experts to build a robust data infrastructure for your business now.

Data-driven cultures are often data centric, with data being used to characterize every aspect of the organization. In such cultures, data is seen as a valuable asset, and data-driven decision-making is seen as the best way to achieve success. This approach can help companies to be more efficient and effective in their data-driven processes and can lead to better outcomes.

In this blog post, we will discuss what is data-driven culture, and the benefits and importance of data-driven culture in an organization. We will also look at how businesses can create a data-driven culture and the challenges they may face. 

What is data-driven culture?

A data-driven culture is an approach or mindset that organizations have where they rely on data as an aid in decision making and strategy planning. Data is collected from different sources like internal systems and external ones, analyzed, and then used to help drive business decisions. Companies with this kind of culture are always looking at ways to improve their profitability, efficiency, or productivity by using data.

Netflix is a good example of a company that has benefited from taking a data-driven approach. The online streaming giant uses data to educate everything from what content to produce to how to personalize the user experience. This has helped them become one of the most successful companies in the world.

A data-driven environment is also much more efficient than a non data-driven one, as it gives space for faster decision making and improved customer service. Also, as per the latest research, data powered enterprises outperform their competitors in business growth.

What are the benefits of becoming a data-driven business?

In today’s world, it’s not enough to be data-driven. The key is to become a data-driven company, which means that all employees are empowered to use data analytics to make decisions.

There are many benefits to having a data-driven culture in your organization. These include:

Improved efficiency

Employees who know what their numbers are and where they stand on various metrics like web traffic, customer feedback, call center conversations, and more can make better decisions about how they should spend their time, leading to less wastage of time and effort.

Better communication

Having one place where everyone can see the data helps with collaboration between departments because there’s less confusion about what’s happening in other areas of the company.

Employee satisfaction

Employees are happier when they have access to the information they need and feel like they can do their jobs effectively. Employees feel empowered when they have this type of access because they know what’s going on at all times.

A data-driven culture will help your business succeed as it provides an easy way for people within an organization or team to access their metrics and make better decisions based on those numbers. 

Better customer service

A data-driven culture ultimately helps in delivering better customer services, which further leads to enhanced business growth for the organization.

Amazon and Google are the two major tech giants that have greatly benefited from a data-driven culture. 

Amazon, in particular, has used data to its advantage in a number of ways. For instance, the company has used data to improve its customer service and recommendation engine. Additionally, Amazon has also used data to help it become one of the leading eCommerce platforms in the world.

Similarly, Google has used data to improve its search engine and advertising products. Additionally, the company has also used data to develop products, such as Google Maps and Gmail. 

By now, it must be clear how data-driven culture can help organizations tackle tough challenges head-on, and stay ahead of the curve in the rapidly changing business world. A data-driven approach fosters innovation, creativity, and productivity by enabling organizations to make decisions. 

So if you want your organization to thrive in the years to come, make sure to invest in a data-driven culture. How? Let’s hop on to the next section.

How to create a data-driven culture in your organization?

A data-driven culture is one where data is used to inform and guide decision-making at all levels of the organization. This approach can have a transformational effect on businesses, helping them to optimize operations, improve customer satisfaction, and drive growth.

When it comes to being data-driven, there are a few key things that you need to put in place. At the core of it all is a systematic process for gathering, cleaning, and storing data. You need to have processes like data discovery, data mining, data preparation, data integration, warehouse management, reporting and more in place to identify which data is important and track changes over time.

There are a number of factors that contribute to a data-driven environment. So, let’s delve deeper into the blog post.

Have a clear vision

Ensure to have a data-driven approach in your mind as it is important to have a clear and shared vision for what it looks like. This means that all levels of management are utilizing data to make informed decisions. By having a clear vision, you can shape the culture and the way your employees operate the business.

This not only leads to better decision-making but also helps the organization to achieve its goals more effectively. As a result, you will be able to create an environment that is conducive to innovation and creativity. Once the vision is in place, it should be communicated regularly and effectively to everyone in the organization.

Hire data visionaries

A data-driven culture requires people who are passionate about data and its potential to transform the organization. These individuals, known as data visionaries, are rare and highly sought-after. They are the ones who see the potential in data and are able to turn it into tangible results.

Data visionaries are not only experts in their field but are also able to articulate the value of data to those who are not. They understand how data can be used to improve data-driven decisions and drive business results. 

If you want to create a data-driven culture in your organization, it’s important to hire data visionaries. These individuals will be instrumental in helping you create a culture that is open to change and willing to experiment with new approaches.

Keep data accessible

Data should be easily accessible to those who need it. This means having the right tools and infrastructure in place to allow for quick and easy data retrieval. It also means having data analysis tools that are user-friendly can be used by the team.

Data should be easily accessible to all employees, regardless of their role or level within the organization. This can be achieved through a variety of means, such as data visualization tools, data reporting tools, and data analytics.

Maintain proper data

Ensure that the data you’re using is clean and accurate. This may seem like a no-brainer, but it’s important to remember that data quality is the foundation of all data-driven decision-making and data-driven solutions that a company offers. This means making sure that there are no errors or discrepancies in your data set. To do this, you need to have a process in place for regularly checking your data.

Data integration

In order to make data-driven decisions, you need to have access to data from different sources. This data can come from internal sources, such as sales records, customer service logs, or external sources, such as market research reports. 

The key is to ensure that all of this data is easy to access and integrate. Having a central place where all of your data is stored will make it much easier to use for data-driven solutions. In a data-driven culture, everyone from the top down is aware of the data and uses it to drive their decisions. This ensures that everyone in the organization is acting based on the best information available. 

Encourage a data-driven mindset

A data-driven culture is one in which individuals are encouraged and empowered to use data effectively to make informed decisions. By adopting this mindset, individuals can work together to create a better understanding of their business, customers, and operations. In turn, this can help them becoming a data-driven business more quickly and efficiently. 

Furthermore, a data-driven culture can also lead to a more open and collaborative environment, which is beneficial for both employee productivity and innovation. So, if you want to flourish your 

company as the best data-driven organization, start by encouraging a data-driven mindset among all your employees.

Train employees in data literacy

If you want to create a data-driven culture, it’s important to ensure that all employees know how to work with the data. This means having a basic understanding of how to read and interpret data.

There are a number of ways to do this, but one of the most effective is to offer training and development opportunities. This can include workshops, webinars, or even online courses.

In addition, you can create a data dictionary that defines the terms used in your data set. This will help ensure that everyone is using the same terminology when discussing data.

Use data visualization tools

Data visualization can be a great way to help you understand and communicate data-driven insights. By visualizing data, you can make complex data sets easier to understand and identify patterns and trends. There are a number of different data visualization tools available, so it’s important to choose the one that best suits your needs. You can also get a custom data visualization platform developed as per your business needs from a reliable data analytics service company.  

Cloud-based data analytics tools can be a great way to get started with data-driven decision-making. These tools typically offer a pay-as-you-go model, which makes them more affordable for small businesses. In addition, cloud-based tools are often easier to use and require less technical expertise.

Implement business intelligence solutions

Business intelligence solutions can help you turn data into actionable data-driven insights. These solutions typically include a varied range of features, such as reporting, management, and data analytics to help your business.

By using business intelligence solutions, you can make better decisions about where to allocate resources and how to grow your business. In addition, it can help you track Key Performance Indicators (KPIs) and identify areas of improvement for your business.

Follow standardized processes

The final piece of the puzzle is to have standardized processes in place. This means having a clear and repeatable process for data collection, analysis, tracking, and decision-making. As your organization grows, you can replicate the processes that have been proven to work. This will help you avoid the common pitfalls that organizations face when they try to scale their workflows.

Role of a CEO in building a data-driven culture

While all the factors given above are critical, achieving data-driven culture is not possible without the key initiatives of the CEO of the company.  

The CEO can take the company’s culture to an entirely different level by keeping the company’s values, vision, and innovation intact. Listed below are five steps CEOs can take to foster a data-driven environment:

• Be passionate about the subject matter yourself
• Create a safe environment where employees feel comfortable sharing their findings with management
• Map out your company’s goals and identify the steps needed to achieve them
• Encourage your team to ask questions
• Hire the right people for your business

Challenges faced by organizations in becoming a data-driven culture

There are innumerable benefits to becoming data-driven, but not every organization is ready or willing to make the shift. Some of the common challenges faced by organizations include:

Making a transitional shift

One of the major challenges is making the shift from a traditional, siloed approach to data to a more collaborative and centralized approach. This can be difficult because it requires changing long-standing processes and culture.

Dealing with data 

Not to be missed, data is spread all across different departments. This data needs to be collected, cleansed, and standardized before it can be used to make decisions. This can be a time-consuming and costly process.

Hiring employees

Getting employees to buy into the data-driven culture can be difficult because it requires a change in mindset and a willingness to embrace new ways of working.

Reliable data

Another challenge is the data itself. Not all data is created equal. Some data is more reliable than others. This can prove it difficult to trust the data and make decisions based on it.

Tools and technologies

We can’t deny the use of tools and technologies needed to support a data-driven culture. These can be costly and require specialized skills to use.

Decision making

The major challenge is changing the way decisions are made. In data-driven organization, decisions are based on data, not gut instinct. This can be difficult for organizations that are used to making decisions based on tradition or experience.

Maintaining data-driven culture

The last challenge is maintaining the data-driven culture. Once you have achieved it, it is important to maintain it. This can be difficult because it requires ongoing commitment from everyone in the company from the lower position to the higher position including CEO, CTO, and more.

These are just a few of the challenges that organizations face when trying to become data-driven. Overcoming these challenges is essential for organizations that want to make better use of their data and make more informed decisions.

How can Appinventiv help you become a data-driven organization?

If you’re looking to create a data-driven culture in your organization, Appinventiv can help. We specialize in building apps and software with digital solutions that can help drive data-driven processes and decision-making.

Our data analytics services cover a whole suite of solutions including Analytics Consulting, Data Warehousing, Business Intelligence, Data Visualization, and more. 

For instance, we recently developed a full-fledged data analytics platform for a US-based telecom company. Our efforts led to 85% increase in data quality and accessibility and 26% reduction in maintenance costs for the company.   

Reach out to us today in order to learn more about how we can help you create a data-driven culture in your organization.

Final Note

It is clear that there are many benefits to having a data-driven culture in your organization. However, building this type of culture can be a challenging task. The CEO has a critical role in setting the tone for data-driven decision making and ensuring that the necessary resources are made available to employees. 

By following the steps we have outlined and hiring a reliable data service company, you can create a data-driven culture in your organization that will help you make better decisions and drive growth. 

The approach of data analytics has evolved over the years from Descriptive to Diagnostic to Predictive to Prescriptive analytics. The next exemplar shift is toward cognitive analytics advancing to high-performance computing by combining artificial intelligence and natural language processing, machine learning, and deep learning techniques with data analytics.

As stated in a report, the Cognitive computing market size was valued at $8.87 billion in 2018 and is projected to reach $87.39 billion by 2026, at a CAGR of 31.6% from 2019 to 2026. The business that is shifting their focus toward cognitive analytics are Healthcare, BFSI, Retail, Government and Defense, IT & Telecom, and more.

To understand how can businesses tap into cognitive computing systems, let us first understand what is cognitive analytics

What is Cognitive Analytics?

Cognitive analytics imitates human brains to do certain jobs that enable them to draw inferences and insights from the existing data patterns. This helps businesses to arrive at critical business decisions and conclusions based on existing data.

Combination of technologies like Semantics, algorithms of artificial intelligence, machine learning, deep learning, and natural language processing results in cognitive analytics. Cognitive analytics thus becomes more effective from the interactions with data and humans. By searching through the entire data present in the knowledge base, cognitive analytics arrive at real-time solutions.

It all boils down to getting the right information, in the right hands at the right time. Organizations are using cognitive analytics to tap unstructured data sources such as images, emails, text documents, and social posts for finding out real-time answers to arrive at conclusions.

Now that we have understood what is cognitive computing, it’s time to look into the ways cognitive analytics is benefiting businesses. 

Business Outcomes from Cognitive Analytics

Cognitive computing is emerging as the technology that provides organizations an edge to gain business advantage. Organizations that adopted the technology at an early stage have benefited the most from the positive results of their investments.

Studies and surveys have shown that cognitive computing is proving to be the key differentiator with improved productivity and efficiency to rapid business growth. 65% of the early adopters of this technology find it essential for an organization’s strategy and success. 58% of the early adopters find it crucial and a must-have for an organization’s digital transformation and to remain competitive.

Listed below are some of the cases where the early adopters of the cognitive technology are seizing the opportunity:

Customer Acquisition

Organizations are taking a strategic algorithmic approach to sales and marketing by using cognitive data. The primary attribute of the cognitive approach is to sift through huge amounts of data that will help in arriving at meaningful insights. The process does not stop here. With human-like intelligence, cognitive analytics can predict and recommend solutions from trends and patterns. 

With cognitive analytics, companies can refine product pricing based on purchase records, and market trends, thus increasing the possibility of customer acquisition and leading to revenue growth.

To further optimize sales and marketing, the inferences can be drawn from the growing volume that is critical to targeting the audiences. The cognitive analytics approach helps in speeding up the process of data analytics to get relevant insights. This then helps in augmenting the decision-making to reach, engage and maintain valuable customer relationships.

Customer Engagement

Cognitive analytics can greatly help companies to improve business-to-customer data analytics that helps in identifying the consumer’s needs and wants and serving them better. Intelligent analytics benefits the consumer and company alike.

The early adopters in the eCommerce industry are able to personalize the customer experience with the help of cognitive inferences. The technology has also helped the companies in increasing customer engagement and is rapidly responding to market and customer needs.

By enhancing customer engagement and experience with the cognitive approach, more satisfied customers show increased engagement and loyalty which are critical for sustainable growth.

Enhanced Customer Service

By automating routine customer care operations, the agents can be better utilized for high-value interactions. Organizations offering customer service through various channels can highly benefit from cognitive computing as it improves operational efficiency.

Cognitive analytics and artificial intelligence combined resolve most of the pain areas of the poor customer service experience like putting the call on hold for a long period of time, repeating the same information to different agents yet the issue not getting resolved, long IVR options to reach to a live agent, and many more.

Cognitive assistants are emotionally intelligent and simulate live agents. By recognizing patterns, mining data, and learning from experience, they offer personalized interaction through natural language processing.

Augment Productivity and Efficiency

Organizations can harness the power of cognitive analytics to overcome resource bottlenecks while gaining valuable predictive insights, thus augmenting productivity and efficiency.

Irrespective of the industry your organization belongs to, the mantra to take the lead and stay competitive in this digital era is to find out the shortest path to the best results. By combining the technologies of machine learning, parallel processing, and sophisticated analytics, one can find answers to a lot of questions and provide recommendations to derive predictive insights.

Data present in the humongous amounts in diverse formats can be churned that not only improves productivity and efficiency but helps in decision making and planning.

Risk Management

As we have been discussing that cognitive computing can ingest large amounts of data to deliver meaningful insights and patterns providing relevant recommendations much faster than humans do. The financial services industry completely relies on data that has to be integrated with regulatory compliances.

Cognitive computing can churn the data in any form, whether structured or unstructured, from diverse sources, thus providing the capability to mitigate risk while adding value, better customer experience, and improved security and compliance.

Specifically, in the data-driven financial services sector, cognitive analytics is a boon that can aggregate insights from various reports, documents, and financial and medical histories improving compliance while reducing risks.

Examples and Applications of Cognitive Computing

Cognitive computing is used in the tasks that are required to resolve large amounts of data in meaningful assessments. For example, in computer science, cognitive computing and big data analytics identify trends and patterns and understand human language to interact with customers.

Some of the industries that are the early adopters of the technology are healthcare, financial services, manufacturing, and retail. However, exploiting the benefits of cognitive analytics can boost the growth of the organization in any sector.

Mentioned below are some of the use cases of cognitive computing explaining how different industries approach the transformation.

Healthcare

Both doctors and patients can benefit alike. Cognitive computing can manage unstructured data from various sources such as patient reports, medical histories, diagnoses, conditions, and more to make recommendations to medical practitioners. This helps doctors in making better treatment decisions and providing better patient care.

Cognitive image interpretations can detect the minutest of the details that human radiologists might miss or might not comprehend. Machines are anyways much more capable than humans of analyzing different types of images and critical patterns that can provide better insight, thus better treatment and patient care through cognitive analysis.

Patients/individuals can benefit from AI machines and cognitive technology by enhancing their health in the form of devices that will capture the information and will provide recommendations from customized exercises to diet plans, behaving like a health and fitness coach.

The gist is that AI and cognitive technology will empower the healthcare industry to provide better treatment, better decision-making for practitioners, greater cost-effectiveness, patient empowerment, and better health, and fitness.

[Also Read: How data analytics in healthcare is reducing costs]

Retail

By analyzing the basic information of the consumer and the details of the product that the customer is looking for, cognitive analytics can provide personalized suggestions that meet the specified criteria.

AI with cognitive technology gathers information from various sources like social media sentiments, past user preferences, customer reviews, and even the geographical location for the weather of the customer’s location. 

With the unstructured details gathered in different formats, the data is churned and processed to create buyers’ persona based on demographics. AI and cognitive computing make sense of this data based on which the retailer can engage customers exactly on the criteria of their needs and preferences. 

Banking and Finance

Banks have been using chatbots to interact with customers to resolve their queries. Cognitive analysis can analyze customers’ questions, sentiments, etc. to create a database of its own. 

This helps both banks and customers in loan management by analyzing loan needs based on their financial transactions, needs, queries, and much more. The category and type of loan can also be suggested with this technique. Creating new products based on this data helps in increasing the bank’s portfolio.

Financial advisors can use this data collated from cognitive insights to manage customer portfolios, offering them suggestions for the right kind of loans and investments. The right advice and the right returns will only lead to better customer satisfaction and engagement.

The cognitive analysis is also beneficial for analyzing the data for decision making and alarming for the risks of potential frauds. Several insurance companies too have begun to take the advantage of cognitive analytics

Cognitive Analytics Examples

Some examples of cognitive analytics which are in use today include Microsoft’s Cortana, Apple’s Siri, and IBM’s Watson. Other examples of cognitive analytics are:

Royal Bank of Canada uses AI and ML to scan clients’ transaction histories and usage patterns to provide them with more personalized solutions. Cognitive analytics has helped the bank and the clients with better customer service, and personalized financial products by simplifying complex security mechanisms.

The Lark app leverages AI and health IoT devices to cognitively gather data and provides personalized health advice for treatments and fitness.

USAA financial services company has been the early adopter of cognitive analytics that checks the policy application for approvals and disapprovals.

With cognitive computing, researchers at the University of California, Los Angeles were able to identify people with diabetes changes by mining the patient records.

Cognitive Analytics with Appinventiv

Solutions offered by Appinventiv cater to diverse industries that are designed and formulated with a focus on fulfilling ever-changing marketing needs.

Appinventiv takes pride in providing data analytics services that have enabled organizations to identify the gaps and successfully deploy digital solutions across different levels of the organization.

For instance, Appinventiv has provided successful data analytics and solutions to a US-based telecom giant. By offering a customer-centric product solution, we created an ecosystem that could process high volumes of data to classify it according to customer behavior and preferences.

We have worked with some best innovative brands and ideas and are eager to transform your business idea into reality. Speak to our expert.

Wrap Up!

Cognitive analytics is the next big thing that will transform your business with robust and agile solutions. Cognitive analytics being the extension of our intelligence and skills, has the potential to strengthen overall business capabilities. It will not be an exaggeration to say that cognitive analytics is becoming a must-have for businesses.

FAQs

Q. How does cognitive computing improve the business?

Cognitive computing helps in improving the business outcomes by:

• Accurate data analysis
• Leaner and more efficient business processes
• Improved customer service and interaction

Q. What are the differences between cognitive analytics and AI?

A. Following are the major differences between the two technologies:

• AI uses ML, NLP, neural networks, and deep learning whereas cognitive analytics uses all of these and sentiment analysis.
• AI has the capability to find patterns in big data to learn and reveal information and deliver solutions to complex problems whereas cognitive analytics mimics human thoughts in finding solutions to complex problems and decision making.
• The purpose of AI is to automate processes whereas cognitive analytics enhances human capabilities.

Q. What are the must-have attributes of cognitive analytics?

A. With self-learning technologies that use data mining, pattern recognition, and NLP, cognitive analytics mimics human intelligence. To achieve this, it must have the following attributes:

• It should be adaptive to the dynamic data in real-time and should be able to adjust as the environment and data changes.
• It should be interactive with other processors, devices, and cloud platforms.
• It should be iterative and stateful.
• It should be contextual and able to understand, identify and mine contextual data from structured and unstructured information.

With the growing adoption of emerging technologies such as Machine Learning, Artificial Intelligence, and Intelligent Automation, businesses across industries have started experimenting with multiple big data analytics applications. 

In a survey conducted by NewVantage Partners in late 2021, it was found that 91.7% of IT and business executives said they’re growing their big data and AI investments, while 92.1% of them reported that they are getting massive business outcomes from these technologies. Now, to be a part of these statistics, it is essential to apply the best big data analytics techniques.

In this article, we will look into the basics of getting started with big data analytics technologies in your business. However, prior to that, we would also look into what is big data analytics and why it is important for your business.

What is big data analytics?

Big data analytics is the process of finding patterns, trends, and relations between a large amount of raw data, with the intent of making data-backed business decisions. With the constant influx of data coming in from multiple touchpoints like smart devices, web usage, and customer interactions etc., big data engineers are able to use the information and convert it into key business insights. 

Types of big data analytics

Big data can be divided into three primary categories. The three types are crucial in not just understanding how big data works but also in getting started with the incorporation of the technology in the business. Here are those three types of big data analytics. 

Structured data – These data sets are organized with defined dimensions according to the set parameters. These are tangible numbers which makes it easy for an architecture to sort and collect data. 

Unstructured data – Any data that comes with an unfamiliar model or structure gets categorized as the unstructured dataset. It is difficult to process and then analyze this data type as it lacks specificity in structure and form. 

Semi-structured data – This data type is a mix of both structured and unstructured data types. It is one that has not been classified into any specific repository but consists of important tags or information that differentiates elements within the dataset. 

The key elements of big data 

The technology revolves around multiple functions and components ranging from:

Big data architecture – The traditional data warehouse gets incorporated in big data architecture for storing the structured data. Moreover, businesses use data lakes that store multiple data sets in the native formats and are usually built on big data analytics technologies like Hadoop, Spark, and NoSQL databases. 

Big data analytics – The big data architectures are usually brought into use for analytics applications that can vary from reporting to advanced analytics like finding anomalies. Machine learning, specifically, has been benefiting from the presence of big data for finding patterns that dictate the business chart. 

Big data integration – Several integration techniques are adopted in big data for preparing the information for analysis stages like data discovery, cleansing, and validation. The element is very crucial when it comes to defining the success of a big data project. 

[Also Read: What is data integration and why it matters to your business?]

Big data governance – An efficient data governance is needed for ensuring that the collection of data is consistent and complies with the data standards and privacy regulations. In order to achieve this, data quality management practices are incorporated at every individual step of big data integration. 

Now that we have settled the basics of what is big data, let us look at the importance of big data analytics for your business. 

The advantages of big data analytics

The importance of big data analytics stands for both established firms and startups. For the former, it helps make sense of the massive amount of datasets, for the latter, it helps through promoting agility and the ability to take quick data-centric decisions.

Understanding customers 

Big data analytics applications help with giving businesses a complete picture of their customers. What makes them act, what type of products they buy and when, how they interact with businesses, and why do they choose a certain company/product over others. 

Through the right set of big data analytics techniques, businesses are able to gather data from internal data, social media, public data, and browser logs – a combination that gives an inside out view into the customers’ expectations. 

Finding trends 

Identifying and tracking patterns and behaviors is another set of benefits of big data analytics. It tells businesses where their growth is headed, what is the demand of their product and how it is changing over time. The technology removes “instinct” from prediction about trends. 

Big data tools are engineered to collate trends from social media and traditional media sets, customer behavioral patterns. It, in turn, informs businesses of where they should focus their energy on a proactive level both in terms of targeted advertisements and customer retention. 

You may also want to read, The top 5 trends shaping the future of data infrastructure in 2022 and beyond

Monitoring competition 

Traditionally, understanding competition moves has been limited to activities like reading business news, pretending to be a customer to get insights into processes, etc. Today, however, you get every information you need about the competition without even leaving the desk. 

Google Trends can show the popularity of a brand, social media can tell about what people are thinking about the product, and rating and review websites can show where the brand is lagging. All this is made available through simple big data analytics techniques. 

Better operations 

Big data technology is in demand for optimizing business processes and operations. By being incorporated in every data-heavy business operations like production line, customer ordering systems, etc. the technology is being used to define efficiencies, finding anomalies, and highlighting when the process needs improvement. 

These benefits of big data analytics are being used heavily across sectors like retail, supply chain, telecom, healthcare, and other similar industries. 

Tweaking business models 

Big data analytics applications have been used by companies to update their current products while coming up with new products and business lines. With a massive set of market data at their disposal, businesses are able to define what their customers are looking for and which businesses are catering to their needs. This information, in turn, is being used to define new products and business models. 

The advantages of big data analytics we covered are only the surface level explanations of how companies use big data. There are a number of other ways the technology is being used in the global startup and established business ecosystem. However, for a business that is new in the domain, it is important to understand that these benefits can only happen when you have rightly integrated big data analytics into your business. 

[Also Read: How to use Cognitive Analytics to achieve better business outcomes]

How to get started with big data analytics in your business?

An important part of getting all the many benefits that big data technology promises is knowing how to get started with its incorporation in your business model. 

At Appinventiv, we have seen this process happening whenever a client comes to us seeking help with creating a big data project. 

Big data projects come into existence when a business executive gets convinced that they are missing out of big data benefits. This conviction leads to CMO and CIO teams to work together where they specify and make a scope of the insights that have to be pursued and make analytics architecture around them. Next, the business launches a small IT project where the CIO team engineers a complex system where they capture the raw data and convert them into structured information. 

Now, when the analytics professionals start using the data for which the big data model was created in the first place, they start noticing problems in the system and the cycle of iterations begins. 

We have witnessed this situation happening multiple times in our data analytics services journey. 

For instance, when we created a data analytics platform for a leading telecom company in the US, our first step was to store and analyze their data on the cloud using Apache technologies such as Spark and Hadoop. To make data insights accessible, we deployed different BI solutions through interactive dashboards. The results?

Our efforts led to 100% processing of customer consumption data and 85% increase in data quality and accessibility. 

Coming back to getting started with big data analytics in business, what we advise to our clients is firstly, starting very small. It can be as small as identifying the products their customers are viewing and leaving the page from – an information that can be used for sending follow-up email or push notification.

Secondly, we suggest capturing data in a one-off model instead of creating a process for capturing and analyzing data. If you start with capturing the specific data needed to perform the test, you will find the data volume easy to manage. At this stage, we include an analytics engineer. They can highlight what data they need and which should be ignored for a particular big data model. 

Once you have mastered these two stages, you can move to the stage of a much broader adoption of big data in business. Incorporating big data through these small, targeted steps can be the least expensive, fastest way to go. It would give business a clarity if a major investment in big data would prove beneficial or how it would pay off in the long-run. It is unquestionable why big data is important, but something which is benefiting others might not benefit you the same way. By running these small-scaled prototypes, you will be able to decide if at all there is a need for big data in your business. 

Thirdly, it is important to know the challenges associated with incorporation of big data. Knowing what those are beforehand, would help in taking calculated decisions around the size of the project. Here are some of those challenges – 

• Managing data – as the business grows, so does the size of the data which has to be collected. 
• Maintaining data quality – businesses end up spending a lot of their time scrubbing data and making them insights ready. 
• Keeping data secure – privacy concerns are an inevitable part of big data, especially since the large amount of business data they contain. This is where strict compliance checks come into the picture. 

While this highlights the most safe-sided approach of big data incorporation in business, knowing how big data analytics works is equally important. It would give you a framework of how to get started. Let us look into that next as we conclude the article. 

How does big data analytics work?

Big data analytics is majorly a four stage process that is used by businesses to get informed data as the outcome. Here’s what those stages are:

Data collection – Here the data (structured, unstructured, and semi-structured) is collected from multiple sources and stored in data warehouses where the BI tools can analyze them. Usually, the highly diverse unstructured data are assigned their individual metadata and are stored in the data lakes. 

Data processing – Once the data is gathered and stored, they are processed to get results on queries. To achieve this, two processing options are available – batch processing, where large datasets are looked at over time and stream processing where small batches of data are processed. 

Cleaning of data – Data, irrespective of the size, requires scrubbing for bettering the data quality and getting better results. At this stage, the data are formatted in the same context and all the irrelevant, duplicate data are eliminated from the system. 

Data analysis – This is the stage where the collected data are converted into insights. Some of the most popular data analytics methods include – data mining, predictive analytics, and deep learning. 

Wrapping up!

So here is everything you need to know about getting started with big data analytics. While the approach of integration can vary, the end result should be the same – big data should solve business problems and not make them complex. 

If you are looking to add in big data technology in your business and are looking for support to get started, get in touch with our data analysis team. We will sit you to identify the areas where big data can be most beneficial and work on its incorporation. 

Big data in manufacturing is one such solution. And there are signs that validate it, one being that the global big data in manufacturing domain is projected to reach $9.11 billion by 2026. The use case of the technology has been playing a big role in shaping this market growth. In this article, we are going to explore the role of big data analytics in manufacturing and how it is making the entire domain smarter and more efficient. 

What is big data?

The technology can be defined as high velocity, high volume, and high variety data sets that help with processing information which enhances insights, helps with decision making, and automates processes. 

Another way to define big data can be that it is an Industry 5.0 technology which consists of a diverse and complex set of data which is gathered through multiple resources and requires an advanced processing approach like cloud computing or machine learning to provide key business insights.

The technology is majorly made of three key elements – 

Variety – There is a wide range of data available to businesses but they can be categorized into unstructured, semi-structured, and structured data. 

Velocity – It refers to the rate at which the data gets received. Usually, the data is stored in memory but there are real-time processing mechanisms active in businesses as well. 

Volume – The technology processes a big amount of information which comprises a range of structured, semi-structured, and unstructured data. 

Now that we have looked at what big data stands for, it is time to look into how the data is generated by the manufacturing industry. 

How is big data analytics for manufacturing generated?

In addition to the general sources of generating data like loyalty programs, online marketing analysis, and social media monitoring, the industry is using a range of software to collect the information. 

The software sets ranging from CRP, MES, and CMMS, etc. are integrated with the machines to generate big data in the manufacturing space. 

The data sets which these software and machines generate then can be used to form patterns, identify the problem areas, and come up with data-backed solutions. 

Now to generate this massive amount of data, the industry requires a robust set of intuitive technology stack. At Appinventiv, we make use of some of the best in class industrial data analysis tools:

What is the role of big data analysis for manufacturing?

The benefits of big data in manufacturing ranges from several preventive level advantages to aiding predictive decisions. Let us look into the different ways that highlight the importance of data analytics in the manufacturing industry.

1. Greater competitive edge

The manufacturing industry has been the center of technological innovations. Whether it is mobile connectivity, industrial IoT or next-gen hardware, the data that is generated through all the different mediums helps raise competitiveness to the next level. The data leads to greater insights in the market trends, better understanding of the customer needs, and forecasts into the future trends. In short, it provides everything that gives manufacturing houses a massive competitive edge.

2. Less downtime

Downtime of hardware can be a real productivity hazard in the manufacturing domain. It doesn’t just hamper employees’ time but also requires a lot of maintenance and troubleshooting. Now, the solution that the industry has found for the issue is using industrial data analysis to perform preventive and predictive maintenance on their hardware. It helps the manufacturers keep a track of hardware’s quality assessment by analyzing their efficiency and working on a daily basis.

3. Greater CX

Manufacturing houses are now using advanced sensors to provide big-data powered alerts to the field technicians concerning the maintenance requirements, they are using RFID tags to monitor the condition of units, and making use of data-driven reports that offer accurate suggestions for improving the customer services.

4. Supply chain management 

Big data analytics in manufacturing gives manufacturers the ability to track down the location of the products. This capability of tracking down the product location using technologies like radio frequency transmission devices, and barcode scanners, solves the issue of products getting lost or becoming difficult to trace. What this means for customers is that businesses are able to give them a more realistic delivery timeline. 

5. Production management 

One of the key productivity signs of a manufacturing house is determining what the market needs are and what volume of goods they need to create. 

Back in the day when big data in manufacturing did not exist, businesses relied on human estimates that led to goods either getting produced in excess or shortage. Big data helps with giving businesses important predictive insights that helps them make the choice better. 

6. Agile response to fluctuation in market demand 

The incorporation of real time manufacturing analytics specifically in the CRM system can help manufacturing houses forecast the future in real-time. The analysis of CRM data can showcase the difference in the order and consumption patterns that can be used for driving the adjustment in production. Moreover, the big data-driven intelligence gathered from the CRM can help with knowing what the customers are asking for and then preparing the production in a cycle in a way that the time to respond minimizes. 

7. Speeding up the assembly 

With big data analytics in manufacturing, businesses have gotten the capabilities of segmenting their production and identifying the units that get manufactured faster. This helps the manufacturing houses know where they have to focus their efforts to get maximum production. It would also help them identify the areas they are most efficient in, along with the ones they need to work on. 

8. Identification of hidden risk in process  

The analysis of data around the equipments’ past failures enables the manufacturers to forecast its lifecycle and set up the correct predictive maintenance schedules, which are either usage based or time based. All this, in turn, helps detect the gaps, lower the wastage and downtime, and help businesses create a recovery plan in case an unexpected failure occurs. 

Moreover, big data when combined with AI enables the manufacturers to automate the processes so that they self-optimize without the involvement of a human intervention.

9. Product customization made feasible 

Historically, manufacturing units have focused on producing at scale and left the customization to enterprises serving the concentrated market. Data analysis for manufacturing makes customization possible at the manufacturing stage by predicting its demand and then giving the manufacturers lead time to produce customized products at a great scale. 

Using big data, manufacturers are able to streamline their manufacturing process by eliminating wastage and predicting the demand. This streamlining helps them with time they require to do mass personalization of the products. 

10. Improvement of yield and throughput 

Big data technology helps manufacturers find hidden patterns in the processes, enabling them to pursue their continuous improvement initiatives with better certainty. The result of this can be seen in a rise in the throughput and yield.

11. Price optimization 

The price point of a product can be decided with the help of big data. The technology can collect and analyze the data from multiple stakeholders like customers, suppliers, etc. to determine the best price point that suits both customers and businesses. 

12. Image recognition

A manufacturing house can find a range of image recognition specific use cases for big data. Let’s see an example. Suppose you require a specific spare part but you don’t know what it’s called or how much it costs. A big data powered image recognition software can help businesses capture the image and give the details back to the manufacturers.

Now that we have looked into the wide set of reasons as to why big data is important in manufacturing, let us look at some real-world cases of where businesses have adopted the technology for an evident rise in production efficiency.

What are some of the top real-world big data in manufacturing use cases?

The manufacturing industry has made it evident that there are a number of benefits that big data offers to the domain. But how are those benefits actually being leveraged in the real world? Let us find out through some real-world examples of companies. 

Now that we have looked into the real-world use cases of big data in the manufacturing domain, let us look into the ways the technology can be adopted in the industry. 

How to incorporate big data in the manufacturing space?

While every project is different, there are some steps which are common in every project that calls for the adoption of big data in manufacturing. 

1. Establish the business KPIs 

The beginning of a big data project should start with knowing what is expected from its inclusion. You will only be able to validate the profit and feasibility of the technology in your manufacturing business when you know the key performance indicators to measure them against. 

2. Analyze the issues in manufacturing

The next step would be getting details around your current manufacturing requirements and needs. Only when you know how your manufacturing unit is performing today, will you be able to find scope for big data inclusion. An analysis of your current status will also help you build a strong quality improvement process.

3. Perform a cost-benefit analysis of the project 

Once you have set the KPIs for the technology and analyzed the issues in business, the next step would be to know the cost of the project. When estimating this price point, account for all development, integration, and maintenance of the project. Once done, measure this cost against the possible perks that the manufacturing unit can expect. 

4. Incorporate big data in the manufacturing process

Once you have identified the processes which you will be incorporating big data in and have analyzed the cost, benefit analysis, the next step would be partnering with a reliable big data company. They will help you with a seamless integration of the technology in the manufacturing houses.  

Final thoughts

Big data in manufacturing, as we have covered throughout the article, is the secret behind manufacturers getting high production efficiency, better prediction of anomalies, and getting competitive advantage. However, applying it in the traditional systems is not easy or enough. In order to truly benefit from the technology, big data needs to be integrated with technologies like IoT and AI. 

What manufacturers truly need to gain advantage from the technology is the support of a data analytics service powerhouse like Appinventiv. If you are looking to modernize your manufacturing house, reach out to us. 

When implemented correctly, data analytics in healthcare can reduce treatment costs, offer a comprehensive insight into patients and conditions that affect or have the potential to affect them, and improve the quality of life in general. 

Healthcare data analytics combines historical and real-time data to predict trends, reveal actionable insights, achieve medical advances, and drive long-term growth. 

As per a report by Allied Market Research, the global healthcare analytics market size is projected to reach $96.90 billion by 2030 from $23.51 billion in 2020, at a CAGR of 15.3%. Various factors such as the need for laboratory automation and efficiency and the rising prevalence of chronic diseases have led to a surge in the adoption of big data in the healthcare industry. 

Apart from the aforementioned factors, healthcare organizations are now turning to big data analytics to cut down overspending on poor stock management, patient care, and staff deployment. 

In this article, we are majorly going to focus on the ways data analytics is helping medical institutions and professionals assist with patient care and reduce costs.

But before moving forward, let’s dive into the main types of healthcare analytics and how they can drive a change in the healthcare industry. 

What are the types of data analytics in healthcare?

There are four types of healthcare analytics that can be applied based on the goals and needs of healthcare professionals and institutions. Each type is crucial for making the most of healthcare data, depending on the situation.

Descriptive analytics: Descriptive analytics provides a historical view of data that allows healthcare providers and management to determine if current practices are efficient and make recommendations, if necessary. 

Predictive analytics: Predictive analytics uses modeling and forecasting to determine what is likely to happen next. It helps healthcare providers to calculate risk scores for each patient and identify which patients may require additional attention. However, predictive analytics does not indicate preventing adverse events such as hospitalization.  

Diagnostic analytics: Diagnostic analytics helps understand why something happened so that actions can be taken to address the problem. It is useful in figuring out what events and factors led to a specific outcome. Just like descriptive analytics, diagnostic analytics also involves an investigation of historical data.

Prescriptive analytics: This type of analytics allows us to understand what actions are needed to change the prediction. It uses advanced algorithms to help determine the effects of specific actions and provide solutions by applying historical data to get a specific result. It is the most useful and powerful type of analytics, providing guiding recommendations to healthcare professionals.

Top ways to reduce costs in healthcare using data analytics 

Applying data analytics and visualization in the right way can lead to an increase in patient access to services, resulting in lower costs, more revenue, and improved patient satisfaction. In this section, we will delve into different ways to reduce costs in the healthcare sector using data analytics tools and solutions.

Electronic health records (EHRs): One of the major benefits of data analytics in healthcare is digitizing medical records that can result in substantial savings. EHRs generate a lot of data from the plethora of clinical information they contain. The data collected in EHRs comprises administrative and diagnostic patient information updated in real-time for each encounter. In particular, EHR provides information on procedures, demographics, length of stay, and fees. Additionally, EHRs improve the quality of care since they can trigger warnings and reminders for diagnostics.

They also enhance performance by streamlining routine tasks, reducing errors, and speeding data access/entry, thus significantly cutting costs in healthcare.

Kaiser Permanente, a leading healthcare company in the US, has implemented an integrated system that shares data across all their facilities and makes it easier to use EHRs. This improved outcomes in cardiovascular disease while saving the healthcare company $1 billion from reduced office visits and lab tests.

Thus, it means a shared system of digitized patient records can save hospitals and healthcare centers substantial sums of money.

[Also Read: How to build an EHR system? Features and cost breakdown]

Forecasts operating room demands: Operating rooms are expensive to build, operate, and staff. So, it’s in every hospital’s best interest to optimize operating room use without compromising patients’ health. To achieve this goal, a number of healthcare providers and administrators are utilizing data analytics to understand the relationships between the numerous operating room variables that tend to ruin effective scheduling. These variables include surgeon availability, operating hours, and equipment functionality and availability.

With so many factors to consider, creating a surgical schedule isn’t easy. Fortunately, data analytics can help streamline operating room management. Hospitals are now awash in staff, patients, and facilities data. So, the best way is to mine the right data, study patterns of operating room utilization, and use predictive modeling to match the most appropriate human and support resources with the right operating rooms.

For instance, hospitals like UCHealth in Colorado have switched to mobile apps that use cloud computing and predictive analytics to optimize operating room utilization and eliminate schedule conflicts. Since the switchover, UCHealth saw an increase of 4% or $15 million in revenue from surgeries per year.

Optimizes staffing: In many healthcare facilities, on-the-fly scheduling is done without taking into account other factors, which can sometimes lead to a shortage in staff that will ultimately affect patient care. 

Accurate staffing is extremely important because half of a hospital’s budget is spent on labor costs. Data analytics is now helping administration and managers better cope with staffing challenges at least 30 days in advance. Data analytics for enterprise ventures uses intelligence to forecast and analyze historical staffing numbers, local weather trends, seasonal infections, holidays, and more, available for each organization. This leads to reduced labor costs and efficient, cost-effective shift management.

Prevents 30-day hospital readmissions: Unnecessary readmissions are rampant in a US healthcare system. They also place an unnecessary cost burden on hospitals that have few resources to spare. Reducing readmissions promises to lower costs for hospitals. Data analytics tools can be used to identify patients with specific symptoms and diseases that lead to their readmission. This helps healthcare providers take additional measures to prevent the patient from returning within the 30-day window.

Analytics tools can also be used to develop a heat map for each patient who has stayed out of the hospital for the past 30-day period. Healthcare professionals can look at easy-to-understand visual representations of the data to identify where exactly in this 30-day window the patient is most at risk. This information helps them plan further actions. 

For instance, data scientists at NYU Langone Medical Center have developed a predictive analytics algorithm based on a wide range of clinical factors. It helps identify patients who are likely to spend less than two nights in hospital. The tool also helps physicians know when to place a patient on observation.

Forestalls no-show appointments: When patients do not arrive for scheduled appointments, the unexpected gaps in a practitioner’s daily calendars can have financial ramifications and throw off the workflow. Using data analytics to identify patients who are likely to skip appointments without advance notice can significantly lower revenue loss, allow medical professionals to offer free slots to other patients, and enhance customer experience. A Duke University study found that data analytics could capture 4,800 patient no-shows a year for higher accuracy.

Data analytics tools can also be utilized to predict when patients may appear at facilities such as urgent care centers or emergency departments, which do not have fixed schedules. This, in turn, could help improve staffing levels and minimize wait times. 

Enhances supply chain cost management: As for all businesses, hospitals rely on the supply chain for efficient operations. If the chain experiences disconnection, patient care, and treatment suffer, and hospitals lose revenue. 

Analytical tools maintain efficiency and track supply chain metrics, resulting in cost savings of up to $10 million yearly. These tools also help automate requisitions, purchase orders, invoices, and other processes to reduce documentation errors. 

Prevents fraud and enhances security: Hospitals are no stranger to data breaches and fraud claims. In June 2020, the Department of Health and Human Services in the US witnessed an increase in data breaches as cybercriminals took advantage of the distractions from the Covid-19 pandemic. Such cybercrimes are costly to hospitals, resulting in an average revenue loss of nearly $3 million. To combat data breaches, hospitals are using data analytics to identify pattern changes in network traffic and other suspicious online behaviors. 

Data analytics can also allow organizations to track incorrect or fraudulent payments. Analytics streamline the claims process for more expedient reimbursements and track incorrect payments.

[Also Read: How to Ensure Cybersecurity in the Age of IoT]

Reduces medical errors: Medical errors arising from surgical, diagnostic, medication, and other failures affect approximately 400,000 patients a year and cost hospitals billions of dollars in lost revenue. Many errors occur from staff negligence or insufficient information. Big data can help reduce these medical errors by analyzing the patient’s medical records with all treatments prescribed and flagging anything that seems out of place.  

Now that we have seen how data analytics is used in healthcare for reducing costs and the benefits of data analytics in healthcare, let’s see what the future of data analytics looks like in the healthcare industry. 

The future of data analytics in healthcare 

Data analytics is already playing a massive role in transforming the healthcare sector by providing quality care and reducing overall costs for both patients and medical institutions. It is set to have a substantial impact in the coming years as well. Big data analytics in healthcare will increasingly be used to help predict the probability of future scenarios in order to make informed and better decisions. 

Technological and organizational advancements will make increasing quantities of data available that will be suitable to develop and deploy data analytical tools and systems. Processing power will increase in the future, and more off-the-shelf analytical tools will be available, which will make this development easier and commercially realizable. 

Also, in the coming years, there will be a significant increase in the use of data analytics tools to predict the compliance and engagement of patients, which is particularly important in successfully managing long-term conditions.

You may also want to read, The top 5 trends shaping the future of data infrastructure in 2022 and beyond

How Appinventiv can help with data analytics

As we have seen, the impact of data analytics in healthcare is tremendous. Data analytics plays a vital role in reducing expenses for hospitals and enhancing the overall quality of care that patients receive. This is the reason why there has been a significant surge in the adoption of big data analytics in the healthcare sector. 

At Appinventiv, our excellent team of professionals will help you implement data analytics solutions for your healthcare business while taking care of all your data challenges at the same time. These tools will lead to greater user retention, enhance productivity, and reduce medical errors. 

So, in case you want to implement data analytics solutions in your business or learn the importance of data analytics in healthcare, you can discuss with our IT healthcare consulting experts. Our team will offer you the best-in-class data analytics services, with all your needs covered!