Descriptive Analytics: Definition, Applications & Advantages


Descriptive analytics is a critical component of business intelligence, offering insights into past performance. This article explores the definition, advantages, disadvantages, and its place in the broader landscape of analytics. We also delve into the relationship between descriptive and predictive analytics and provide real-world examples. Discover how companies benefit from descriptive analytics and its role in informed decision-making.

Understanding descriptive analytics

Descriptive analytics, a fundamental aspect of business intelligence, involves the interpretation of historical data to gain a deeper understanding of changes within a business. It employs a variety of historical data to draw comparisons between different reporting periods, such as quarterly or annually, and even with other companies in the same industry.

Common financial metrics, like year-over-year pricing changes, month-over-month sales growth, the number of users, and total revenue per subscriber, all fall within the realm of descriptive analytics. These metrics paint a picture of what transpired in a business over a specific period.

How descriptive analytics works

Descriptive analytics takes raw data, thoroughly analyzes it, and derives conclusions that are easily comprehensible to managers, investors, and stakeholders. This data offers an accurate representation of past performance and facilitates comparisons with similar periods or industry peers.

For instance, consider a sales figure of $1 million. To evaluate its significance, you need context. Is it a 20% month-over-month decline or a 40% year-over-year increase? The broader context, including targeted growth, is essential to gauge a company’s sales performance.

Descriptive analytics is a foundational component of business intelligence. While it can be industry-specific, certain measures are widely accepted across the financial industry.

What does descriptive analytics tell you?

Descriptive analytics equips companies with valuable insights into their performance. It allows them to compare their performance with competitors, evaluate historical data like revenue growth, and set current financial trends and individual goals within the organization.

How is descriptive analytics used?

Descriptive analytics finds applications in various aspects of business operations, helping companies assess their performance and identify inefficiencies. Corporate management can utilize it to pinpoint areas for improvement, motivating teams to implement changes for ongoing success.

Two primary methods of data collection for descriptive analytics are data aggregation and data mining. Data is first gathered and then parsed into manageable information that can be used to comprehend a company’s standing.

For instance, return on invested capital (ROIC) is a form of descriptive analytics that evaluates net income, dividends, and total capital to generate a percentage for comparing a company’s performance to others.

Steps in descriptive analytics

Successful implementation of descriptive analytics in business strategy involves several key steps:

  • Identifying which metrics to analyze, such as quarterly revenue or annual operating profit.
  • Identifying and locating the data from internal and external sources.
  • Compiling and preparing the data, ensuring accuracy and formatting.
  • Data analysis through various tools.

Once these steps are completed, presenting the data to stakeholders using visual aids like charts, graphs, videos, and other tools helps provide valuable insights into the company’s direction.

The complexity of a company often determines the extent of descriptive analytics it employs to measure its performance.

Applications of descriptive analytics

Descriptive analytics finds applications in various industries and sectors. Let’s explore some comprehensive examples of how businesses utilize this analytical approach to make informed decisions:

Healthcare sector

In healthcare, descriptive analytics is used to examine patient data, track medical costs, and assess treatment outcomes. Hospitals and healthcare providers can analyze historical patient data to identify patterns, such as the prevalence of specific diseases in certain demographics. This information assists in resource allocation and healthcare planning.

Retail industry

Retailers harness descriptive analytics to evaluate sales data, customer buying behavior, and inventory management. For instance, a retail company can analyze historical sales figures to identify seasonal trends, helping them plan for inventory needs during peak seasons, reducing overstock or understock situations.

Financial services

Financial institutions rely on descriptive analytics to analyze transaction data, assess risk, and detect fraudulent activities. By examining past transaction patterns, banks and credit card companies can spot irregularities and take preventive measures. This ensures the security of financial assets and customer accounts.

Enhancing business strategy with descriptive analytics

Descriptive analytics plays a vital role in shaping business strategies. Here are some strategies that leverage the power of descriptive analytics:

Customer segmentation

Companies can divide their customer base into segments based on purchasing history, demographics, or other factors. Descriptive analytics helps identify which segments are the most profitable and which require targeted marketing efforts.

Operational efficiency

Businesses can use descriptive analytics to optimize operational processes. For example, manufacturing companies can analyze historical production data to identify bottlenecks in their production lines and make adjustments to improve efficiency.

The future of descriptive analytics

As technology and data collection methods evolve, so does the field of descriptive analytics. The future holds the promise of even more robust and insightful analysis. With the integration of machine learning and advanced data visualization tools, businesses can expect to gain deeper insights from their historical data.

Descriptive analytics is poised to continue its role as a fundamental component of business intelligence, aiding in understanding the past to shape a brighter future.

Advantages and disadvantages of descriptive analytics


Here is a list of the benefits and drawbacks to consider.

  • Breaks down information for easy understanding.
  • Allows companies to assess their performance against competitors.
  • Does not predict future performance.
  • Stakeholders may selectively analyze metrics.


One of the primary benefits of employing descriptive analytics is its ability to disseminate information in a simple, understandable manner. Visual aids, such as charts and graphs, make complex ideas accessible. This analysis enables companies to compare their performance with industry peers effectively.


Descriptive analytics focuses on past data and events, making it unsuitable for predicting future performance. Additionally, stakeholders may selectively analyze metrics, leading to a biased view of a company’s performance.

Descriptive vs. predictive, prescriptive, and diagnostic analytics

Descriptive analytics provides vital information in an easy-to-grasp format. However, the analytical landscape extends beyond descriptive analytics to predictive, prescriptive, and diagnostic analytics.

Predictive analytics:

Predictive analytics aims to make forecasts about future performance by utilizing statistics and modeling. Current and past data help determine the likelihood of similar outcomes in the future. Companies employing predictive analytics can identify inefficiencies and optimize resource allocation.

Prescriptive analytics:

Prescriptive analytics leverages technology to analyze data and provide suggestions for achieving specific results. It considers past and present performance, available resources, and specific situations to develop recommendations for the future. Stakeholders use prescriptive analysis to make informed decisions, whether related to research and development, product offerings, or market entry.

Diagnostic analytics:

Diagnostic analytics involves the use of data to understand the relationship between variables and the reasons behind specific trends. Unlike other forms of analytics, diagnostic analytics doesn’t aim to predict future performance but focuses on identifying the root causes of events for future improvements.

How can companies benefit from descriptive analytics?

Descriptive analytics answers the question “What happened?” by analyzing historical data. Companies utilize it to understand changes and compare performance with other periods. This analysis reveals inefficiencies and drives informed decision-making for the future.

What is the relationship between descriptive and predictive analytics?

Descriptive analytics addresses past performance, while predictive analytics focuses on future outcomes. These two forms of analysis work together, with descriptive analytics helping identify areas for improvement and predictive analytics forecasting the impact of changes.

What is an example of descriptive analytics?

Companies often use descriptive analytics to analyze various metrics during specific reporting periods. These metrics can be both financial and non-financial. For instance, analyzing user engagement on social media can help companies understand the impact of ad campaigns on website traffic, sales, and referrals.

The Bottom Line

Descriptive analytics is a cornerstone of business intelligence, offering a clear view of past performance. It enables companies to compare their historical data, gain insights into industry trends, and identify areas for improvement. While it has its advantages, it’s important to remember its limitations, as it doesn’t predict future performance.

Companies that employ descriptive analytics alongside predictive, prescriptive, and diagnostic analytics have a comprehensive toolkit for data-driven decision-making. By analyzing the past, understanding the present, and predicting the future, businesses can optimize their strategies for continued success.

Frequently asked questions

What distinguishes descriptive analytics from other forms of analytics?

Descriptive analytics primarily focuses on understanding historical data and identifying trends. Unlike predictive and prescriptive analytics, it doesn’t aim to forecast future events or provide recommendations. Instead, it provides a clear picture of what has happened in the past, making it a valuable tool for historical analysis and benchmarking.

How does descriptive analytics benefit businesses in decision-making?

Descriptive analytics aids decision-makers by offering a comprehensive view of past performance. It allows them to assess the effectiveness of strategies, understand historical trends, and identify areas for improvement. With this information, companies can make data-driven decisions and allocate resources more efficiently.

Can descriptive analytics be used in combination with other analytics approaches?

Yes, descriptive analytics often works in conjunction with other analytics methods. For example, it provides the historical data required for predictive analytics to forecast future trends. By integrating descriptive analytics with predictive, prescriptive, and diagnostic analytics, businesses can create a holistic approach to data analysis, enabling better-informed decisions.

What are some common tools and software used for descriptive analytics?

Popular tools and software for descriptive analytics include Microsoft Excel, Tableau, Power BI, and Google Analytics. These platforms allow businesses to import, clean, and visualize data, making it easier to draw insights from historical data sets.

How is descriptive analytics evolving in the age of big data and artificial intelligence?

In the era of big data, descriptive analytics is evolving to handle larger and more complex data sets. Advanced machine learning algorithms and data visualization techniques are enhancing its capabilities. Descriptive analytics is becoming more accessible and powerful, allowing businesses to gain deeper insights and make better decisions based on their historical data.

Key Takeaways

  • Descriptive analytics focuses on interpreting historical data to understand past performance and trends.
  • It involves comparing data from different periods and benchmarking against industry peers.
  • Common metrics in descriptive analytics include year-over-year pricing changes and sales growth.
  • Descriptive analytics aids in identifying areas of strength and weakness within an organization.
  • It is often used in conjunction with other analytics approaches, like predictive and prescriptive analytics.
View Article Sources
  1. What Is Descriptive Analytics? 5 Examples – Harvard Business School
  2. descriptive analytics and performance of commercial banks – BW Academic Journal
  3. Descriptive and Analytic Studies – Centers for Disease Control and Prevention