The 4 Common Types of Business Data Analytics

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Business data analytics is the practice of using data and statistical analysis to gain meaningful insights from data to inform business decision-making and improve overall performance. These insights help to uncover meaningful patterns, trends, and correlations that can drive strategic actions and improvements within an organization.

For example, a retail business can use business analytics to optimize inventory management by analyzing sales data, forecasting demand, and avoiding stockouts. Similarly, it can be used for customer segmentation by analyzing data to identify unique customer preferences and personalize marketing campaigns. In the financial industry, analytics can detect fraud by analyzing transaction patterns. Overall, analytics enables data-driven decisions, enhances customer experiences, and gives a competitive edge in the data-driven business landscape.

Choosing the right types of data analytics for business can be confusing due to the wide range of options available. Different analytics techniques, such as descriptive, diagnostic, predictive, and prescriptive analytics, offer varying insights and capabilities. Additionally, the complexity of data analytics tools and technologies can add to the confusion. It is essential to carefully evaluate and select the most suitable analytics approach that aligns with the business goals and resources. In this blog, we will delve into four types of data analytics, exploring their applications and benefits and helping you identify which one is the best fit for your business.

Did You Know?

According to Allied Market Research, the global big data and business analytics market was valued at $198.08 billion in 2020 and is expected to grow at 13.5% CAGR to reach $684.12 billion by 2030.

Types of Business Analytics

1. Descriptive Analytics

To better understand descriptive analytics, let's consider a universal business requirement that involves analyzing sales data. A business can examine historical sales data to understand patterns and trends in its sales performance. By aggregating and visualizing this data, they can identify which products or offerings are top sellers, which regions or customer segments generate the most revenue, and how sales have evolved. This information can help a business identify areas of strength, make informed decisions about product lines or target markets, and allocate resources effectively.

From the above example, it is now clear that descriptive analytics is a branch of business analytics that focuses on analyzing historical data to gain insights into past events and understand what has happened in a business context. It involves summarizing and visualizing data to depict past performance and trends clearly.

A Case Study Example of Descriptive Analytics

Walmart, a multinational retail corporation, utilized descriptive analytics to optimize its inventory management processes. They gained insights into product popularity, seasonal variations, and stock replenishment requirements by analyzing historical sales data, customer demand patterns, and supplier performance. This data-driven approach allowed them to optimize inventory levels, reduce stockouts, and avoid excess inventory. As a result, Walmart achieved significant cost savings and improved customer satisfaction by ensuring product availability while minimizing carrying costs.

Benefits of Descriptive Analytics

Here are some benefits of descriptive analytics:

  • Descriptive analytics provides insights to make data-driven decisions efficiently.
  • It helps monitor and measure key metrics to assess performance accurately.
  • Descriptive analytics identifies bottlenecks and suggests optimization strategies for streamlined operations.
  • It helps identify potential risks and vulnerabilities, enabling proactive risk management and mitigation.
  • Leveraging descriptive analytics helps organizations gain insights that drive competitive advantage in their industry.
Gartner predicts that by the year 2025, 70% of organizations will move their focus from big data to small and wide data.

2. Diagnostic Analytics

To illustrate diagnostic analytics, let's consider an example: Suppose a retail business wants to understand why its sales have declined in the past quarter. They can access vast data, including sales figures, customer demographics, marketing campaigns, and inventory data. By applying diagnostic analytics, they can investigate the reasons behind the decline and uncover insights that may help them address the issue.

The above example helps us understand that Diagnostic analytics is a branch of data analytics that focuses on examining historical data and identifying the causes of specific events or outcomes. It involves analyzing data to understand why certain events occurred and identifying patterns, correlations, and trends that can explain the underlying reasons behind the observed results. Diagnostic analytics helps organizations gain insights into their past performance, identify areas for improvement, and make data-driven decisions to optimize future outcomes.

A Case Study Example of Diagnostic Analytics

A real-world business that has utilized diagnostic analytics is Netflix. Netflix uses this type of analytics to understand customer behavior and improve its content recommendation algorithm. By analyzing user viewing patterns, ratings, and preferences, Netflix can diagnose why certain content performs well or poorly. This helps them identify key factors contributing to viewership and engagement, such as genre preferences, actor popularity, or specific content attributes. With these insights, Netflix can make data-driven decisions to optimize its content library, personalize recommendations, and enhance the overall user experience.

Benefits of Diagnostic Analytics

Here are some benefits of diagnostic analytics:

  • Diagnostic analytics helps uncover the root causes of issues and challenges.
  • It enables targeted solutions by identifying specific areas for improvement.
  • It provides insights into the impact of different factors on performance.
  • By pinpointing areas of inefficiency, diagnostic analytics enables cost-saving initiatives.
  • Leveraging diagnostic analytics allows organizations to gain an edge by resolving issues swiftly and effectively.
  • It helps identify potential risks and take proactive measures to mitigate them.
According to Precedence Research, the Asia Pacific market is expected to grow at a CAGR of 23.5% over the period between 2022 and 2030.

3. Predictive Analytics

To understand predictive analytics, let's consider an example in the retail industry. Suppose a clothing retailer wants to determine which items will likely sell during a specific season to optimize its inventory management and marketing strategies. They collect historical data, including sales figures, customer demographics, weather conditions, and promotional activities for the past few years. The retailer can analyze this data using predictive analytics to identify patterns and correlations. They might discover that certain types of clothing tend to sell better during particular weather conditions or that specific demographics have a higher propensity to purchase certain items. 

With this understanding, we can explain that predictive analytics is the process of using historical data, statistical algorithms, and machine learning techniques to predict future events or outcomes. By analyzing patterns and trends in the available data, predictive analytics can help forecast future possibilities and assist in making informed decisions.

A Case Study Example of  Predictive Analytics

The e-commerce giant, Amazon extensively employs predictive analytics to enhance its customer experience. Amazon's algorithms predict customer preferences and offer personalized product recommendations by analyzing vast customer data, including browsing history, purchase patterns, and demographics. They also employ predictive analytics in inventory management, optimizing their stock levels based on anticipated demand. This approach enables Amazon to deliver a tailored shopping experience, increase customer satisfaction, and drive sales by presenting customers with products they are likely to purchase.

Benefits of Predictive Analytics

Here are some benefits of predictive analytics:

  • Predictive analytics helps detect emerging trends and patterns before they become evident.
  • It provides insights into future outcomes, allowing for better planning and resource allocation.
  • Predictive analytics enables proactive decision-making based on anticipated future events.
  • It assists in Anticipating customer needs and preferences and leads to improved customer experiences.
  • Leveraging predictive analytics provides a competitive edge by staying ahead of market trends and customer demands.

4. Prescriptive Analytics

To provide a clear understanding, let's consider an example related to supply chain management. Imagine a firm that manufactures and distributes various products to retailers. They have historical sales data, information on production capacity, inventory levels, transportation costs, and other relevant factors. The firm can analyze this data using prescriptive analytics and generate recommendations on the best actions to optimize its supply chain. For instance, the analytics model may identify excess inventory for a particular product in a specific region while there is high demand in another area. Based on this insight, the model could recommend redistributing the inventory from the surplus region to the region with higher demand.

This example helps us understand prescriptive analytics is a branch of analytics that uses historical data, mathematical algorithms, and machine-learning techniques to provide recommendations and actions for future decisions. It goes beyond descriptive and predictive analytics by predicting future outcomes and suggesting the best action to achieve desired outcomes.

A Case Study Example of  Prescriptive Analytics

UPS, a global package delivery business, utilized prescriptive analytics to optimize delivery routes, significantly improving efficiency. UPS created an advanced algorithm to recommend the most efficient routes for their drivers by analyzing historical data, weather conditions, and real-time traffic updates. This reduced fuel consumption, minimized delivery times, and improved customer satisfaction. Implementing prescriptive analytics resulted in a 15% reduction in overall delivery costs, enhancing UPS's competitive advantage in the industry.

Benefits of Prescriptive Analytics

Here are some benefits of prescriptive analytics:

  • Prescriptive analytics provides actionable insights for making optimized decisions.
  • It helps in accurate planning and forecasting of future outcomes.
  • It supports automated decision-making processes for faster and more efficient operations.
  • It allows organizations to simulate different scenarios to assess their potential impact.
  • Prescriptive analytics facilitates ongoing optimization and improvement of processes.
  • Leveraging prescriptive analytics enables organizations to stay ahead by making data-driven, optimized decisions.

Determining the Analytics Needs of Your Business

Assessing the Business Needs

Analyzing the business requirements is crucial for making informed decisions and achieving long-term success. By conducting a thorough assessment, you can identify areas where analytics can provide valuable insights and drive impactful decision-making. This process helps determine the analytics needs unique to your business, enabling you to align your resources and strategies accordingly for optimal outcomes.

Examples of needs to consider when assessing your business include the following:

  • Sales augmentation: Analyzing customer purchasing patterns and identifying upsell or cross-sell opportunities to increase revenue.
  • Customer retention: Understanding customer behavior, preferences, and satisfaction levels to develop targeted retention strategies and reduce churn.
  • Market expansion: Evaluating market trends, customer demographics, and competitive landscape to identify new market opportunities and support expansion efforts.
  • Operational efficiency: Analyzing internal processes, supply chain management, and resource allocation to optimize efficiency, reduce costs, and improve overall performance.
  • Customer segmentation: Understanding the different segments within your customer base by analyzing demographic data, purchasing behavior, and psychographic factors. 
  • Marketing effectiveness: Assessing the effectiveness of marketing campaigns, advertising strategies, and customer engagement efforts.

Evaluating the Type(s) of Analytics Align With Your Business Needs

To determine which analytics type aligns with your business needs, consider your goals, available data, and decision-making requirements. Assess whether you require insights into the past, want to understand causal relationships, need predictions for future planning, or seek prescriptive recommendations. Evaluating these factors helps you identify the most suitable analytics type to drive effective decision-making and achieve your business objectives. Therefore, by understanding the nature of your data and the desired outcomes, you can determine whether descriptive, diagnostic, predictive, or prescriptive analytics will best suit your needs.


Analytics plays a vital role in the success and growth of businesses today and will continue to do so in the future. The evolution of analytics is expected to bring even more sophisticated and advanced techniques, allowing businesses to gain a competitive edge.

As technology advances and more sophisticated analytics tools become available, businesses can harness the power of machine learning and artificial intelligence to extract meaningful insights from their data, fueling continuous growth and success. With the increasing availability of IoT devices and sensors, businesses can leverage real-time data streams and analytics to monitor operations, identify anomalies, and make timely interventions to enhance customer experiences and operational performance. To harness the full potential of analytics, businesses must adapt and invest in data-driven technologies, talent, and infrastructure.

However, businesses are finding it difficult to implement analytics in-house for a lot of reasons. They prefer hiring a third-party analytics partner to assist them in analytics operations. 

At Phygital Insights, we have delivered analytics solutions to businesses from several industries. We understood their needs and offered the right analytics solutions to put them on the right path. Contact us today to learn how we can boost your success rate by analyzing your data.

Article by

John is a seasoned data analytics professional with a profound passion for data science. He has a wealth of knowledge in the data science domain and rich practical experience in dealing with complex datasets. He is interested in writing thought-provoking articles, participating in insightful talks, and collaborating within the data science community. John commonly writes on emerging data analytics trends, methodologies, technologies, and strategies.


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