Descriptive Analytics

What is Descriptive Analytics ?

Descriptive analytics is a fundamental component of data analysis that focuses on understanding and summarizing historical data to gain insights into what has happened in the past. It involves the examination of raw data to identify patterns, trends, and key characteristics. This form of analytics is primarily concerned with answering questions like "What happened?" and "What is the current state of affairs?"

One of the key aspects of descriptive analytics is its ability to provide a clear and concise overview of a dataset. This is typically done through the use of summary statistics, visualizations, and other techniques that help in distilling large volumes of data into manageable and interpretable information. For example, common techniques in descriptive analytics might include measures such as mean, median, mode, standard deviation, and frequency distributions.

Descriptive analytics is an essential first step in the broader data analytics process. By gaining a solid understanding of historical data, organizations can identify patterns that may inform future decision-making. It is often used to generate reports and dashboards that offer stakeholders a snapshot of performance, enabling them to make informed choices based on past trends.

Furthermore, descriptive analytics serves as a foundation for more advanced forms of analytics, such as predictive and prescriptive analytics. Predictive analytics leverages historical data to make educated guesses about future trends, while prescriptive analytics goes a step further by providing recommendations on actions to take based on those predictions.

Descriptive Analytics Definition

Various authors have offered their definitions of descriptive analytics:

1) Thomas H. Davenport and Jinho Kim:
"Descriptive analytics encompasses the set of techniques and tools for understanding what has happened in the past and why. It is by far the most common form of analytics."

2) Wayne W. Eckerson:
"Descriptive analytics involves gathering, summarizing, and interpreting data to make it understandable for business people. It comprises standard reporting, ad-hoc query, and drill-down capabilities, as well as alerts."

3) James D. McKeen and Heather A. Smith:
"Descriptive analytics is used to understand and describe what happened in the past, and its purpose is to identify trends and relationships that might not be apparent from raw data."

4) Barbara Haley Wixom and Paul A. Pavlou:
"Descriptive analytics examines data to understand the past. It is used to better understand, explain, and summarize the current state of a system, a process, or group of customers."

5) Randy Bartlett:
"Descriptive analytics is the process of statistically describing, summarizing, and understanding the main features of a dataset. It provides a summary of the historical data and often uses visualizations and simple summary statistics."

6) Joseph F. Hair Jr., et al.:
"Descriptive analytics refers to the examination of data to understand the characteristics of entities or the relationships between entities within a specific timeframe. Its focus is on 'what' happened and 'what is' rather than 'what will happen.'"

Descriptive Analytics Examples

Here are 10 examples of how descriptive analytics can be applied in different domains:

1) Retail Industry:
Sales Performance: Descriptive analytics can be used to analyze sales data to understand which products are the top sellers, the most profitable, and trends in customer purchasing behavior.

2) Finance and Banking:
Customer Segmentation: Banks can use descriptive analytics to categorize their customer base by demographics, spending habits, and financial behavior to tailor marketing strategies and services.

3) Healthcare:
Patient Demographics and Diagnoses: Hospitals can use descriptive analytics to examine patient data, identifying trends in diagnoses, demographics, and admission rates to improve resource allocation and healthcare planning.

4) Marketing:
Website Traffic and User Behavior: Descriptive analytics can provide insights into website traffic, user demographics, and behavior on the site, helping marketers understand which content is most engaging and effective.

5) Manufacturing:
Production Efficiency: Manufacturers can analyze production data to identify bottlenecks, optimize workflows, and improve overall operational efficiency.

6) Education:
Student Performance: Descriptive analytics can be used to track student grades, attendance, and participation, helping educators identify areas for improvement and implement targeted interventions.

7) Human Resources:
Employee Turnover: Descriptive analytics can be used to analyze historical data on employee turnover rates, reasons for departure, and trends over time to inform retention strategies.

8) E-commerce:
Shopping Cart Abandonment Rates: Online retailers can use descriptive analytics to track and analyze shopping cart abandonment rates, identifying potential issues in the checkout process.

9) Supply Chain Management:
Inventory Levels and Demand Patterns: Descriptive analytics can help companies manage their inventory levels by analyzing historical data on demand patterns, lead times, and supplier performance.

10) Customer Service:
Customer Satisfaction Scores: Descriptive analytics can be applied to customer feedback data to track satisfaction scores, identify trends in customer complaints, and improve service quality.

Objectives of Descriptive Analytics

  • Summarize historical data to provide a clear understanding of past events and trends.
  • Identify patterns, relationships, and key characteristics within the dataset.
  • Present data in a structured and comprehensible manner using summary statistics and visualizations.
  • Provide a baseline understanding of performance metrics and key performance indicators (KPIs).
  • Support informed decision-making by offering insights into what has happened in the past.
  • Enable stakeholders to monitor and assess the current state of affairs within the organization or system.
  • Serve as a foundational step for more advanced forms of analytics like predictive and prescriptive analytics.
  • Assist in reporting and communicating insights to relevant stakeholders effectively.
  • Aid in identifying areas for improvement and potential opportunities for optimization based on historical data.
  • Enhance data-driven decision-making processes by providing a solid foundation of historical insights.

Types of Descriptive Analytics

Descriptive analytics encompasses several techniques and methods for summarizing and understanding historical data. Here are some common types of descriptive analytics:

1) Summary Statistics:
This includes measures like mean, median, mode, standard deviation, range, and percentiles. These statistics provide a concise summary of the central tendency and variability of a dataset.

2) Frequency Distributions:
This type involves organizing data into intervals or categories and counting the number of observations within each category. It provides a visual representation of how data is distributed.

3) Data Visualization:
This involves creating graphical representations of data to help identify patterns, trends, and outliers. Common visualization tools include bar charts, line graphs, histograms, scatter plots, and pie charts.

4) Heat Maps:
Heat maps use colors to represent data values in a matrix or table format. They are particularly useful for visualizing data trends and patterns across multiple variables.

5) Histograms:
Histograms are graphical representations of the distribution of a dataset. They display the frequency of data points within predefined intervals or "bins."

6) Box-and-Whisker Plots (Boxplots):
Boxplots provide a visual summary of the distribution, highlighting the median, interquartile range, and potential outliers in a dataset.

7) Time Series Analysis:
This technique involves analyzing data collected at different points in time to identify trends, patterns, and seasonal variations.

8) Correlation Analysis:
Correlation measures the strength and direction of a linear relationship between two or more variables. It helps understand how changes in one variable may be associated with changes in another.

9) Cohort Analysis:
Cohort analysis groups data into smaller, meaningful segments (cohorts) based on shared characteristics. It is commonly used in marketing and customer analytics.

10) Geospatial Analysis:
This type involves analyzing data that is associated with specific geographic locations. It's used in fields like logistics, urban planning, and environmental studies.

11) Customer Segmentation:
Customer segmentation involves dividing a customer base into distinct groups based on common characteristics or behaviors. This helps in tailoring marketing efforts and services.

12) Data Mining Techniques:
Techniques like clustering, which groups similar data points together, and association rule mining, which identifies patterns in data, fall under descriptive analytics.

13) Data Cleaning and Data Preprocessing:
Before conducting any analysis, it's essential to clean and preprocess data to remove duplicates, handle missing values, and transform data into a usable format.

Descriptive Analytics Tools

There are a variety of tools available for conducting descriptive analytics, ranging from spreadsheet software to specialized data visualization and statistical packages. Here are some commonly used descriptive analytics tools:

1) Microsoft Excel:
Excel is a widely used spreadsheet tool that offers basic data analysis capabilities, including functions for calculating summary statistics, creating charts, and performing simple data manipulations.

2) Google Sheets:
Google Sheets is a web-based spreadsheet tool similar to Microsoft Excel. It provides collaborative features and basic data analysis functionalities.

3) Tableau:
Tableau is a powerful data visualization tool that allows users to create interactive and dynamic visualizations from various data sources. It is known for its user-friendly interface and extensive visualization capabilities.

4) Power BI:
Microsoft Power BI is a business analytics tool that enables users to create reports and dashboards from a wide range of data sources. It provides powerful data modeling and visualization features.

5) QlikView/Qlik Sense:
QlikView and Qlik Sense are business intelligence platforms that allow users to create interactive dashboards and reports. They offer powerful data discovery and visualization capabilities.

6) Google Data Studio:
Google Data Studio is a free tool that allows users to create interactive and shareable dashboards and reports. It integrates seamlessly with other Google products.

7) SAS (Statistical Analysis System):
SAS is a comprehensive software suite for advanced analytics, including descriptive, predictive, and prescriptive analytics. It provides a wide range of statistical and data manipulation tools.

8) R:
R is a programming language and open-source software environment for statistical computing and graphics. It offers a vast collection of packages for data analysis, visualization, and statistical modeling.

9) Python:
Python, with libraries like Pandas, Matplotlib, and Seaborn, is a versatile programming language widely used for data analysis and visualization. It offers a rich ecosystem for data manipulation and exploration.

10) SPSS (Statistical Package for the Social Sciences):
SPSS is a software package used for statistical analysis. It provides a user-friendly interface for data manipulation, visualization, and advanced statistical modeling.

11) Stata:
Stata is a statistical software package that provides a range of tools for data manipulation, analysis, and visualization. It is commonly used in academic research and social sciences.

12) SAP BusinessObjects:
SAP BusinessObjects is a business intelligence platform that offers tools for reporting, ad-hoc analysis, and data visualization.

MATLAB is a programming environment that is widely used for numerical computing and data analysis. It offers powerful tools for data manipulation and visualization.

Steps in Descriptive Analytics

Descriptive analytics involves a series of steps to summarize and understand historical data effectively. Here are the key steps in conducting descriptive analytics:

1) Define the Objective:
Clearly outline the purpose of the descriptive analysis. Determine what specific insights or information you are seeking from the historical data.

2) Data Collection:
Gather the relevant historical data from various sources. Ensure the data is accurate, complete, and representative of the time period under consideration.

3) Data Cleaning and Preprocessing:
This step involves cleaning the data to address issues like duplicates, missing values, outliers, and inconsistencies. Additionally, data may be transformed or aggregated to make it suitable for analysis.

4) Data Exploration:
Begin exploring the dataset to get a sense of its structure, variables, and general patterns. This may involve generating summary statistics, histograms, and initial visualizations.

5) Summary Statistics:
Calculate basic summary statistics such as mean, median, mode, standard deviation, and range. These measures help provide a high-level overview of the dataset.

6) Frequency Distributions:
Create frequency distributions or histograms to understand how data is distributed across different categories or intervals.

7) Data Visualization:
Utilize visualizations like bar charts, line graphs, scatter plots, and heat maps to represent the data in a meaningful way. Visualizations can highlight patterns and trends that may not be immediately apparent in raw data.

8) Correlation Analysis:
Analyze the relationships between variables to understand how changes in one variable may be associated with changes in another. This helps identify potential patterns or dependencies.

9) Time Series Analysis:
If applicable, examine data collected over time to identify trends, seasonal patterns, or cyclical variations.

10) Cohort Analysis:
Segment the data into smaller, meaningful groups (cohorts) based on shared characteristics. Analyze trends or behaviors within these segments.

11) Geospatial Analysis:
If the data is associated with specific geographic locations, conduct spatial analyses to uncover insights related to location-based patterns.

12) Customer Segmentation:
If relevant, categorize the data into distinct customer segments based on shared characteristics. This can provide insights into customer behavior and preferences.

13) Documentation and Reporting:
Document the findings, insights, and visualizations generated during the descriptive analysis. Create reports or dashboards to present the results in a clear and understandable format.

14) Interpretation and Insight Generation:
Analyze the results to draw meaningful conclusions and insights from the descriptive analysis. Consider how these insights can be used to inform decision-making or drive improvements.

15) Iterative Process:
Descriptive analytics is often an iterative process. As more data becomes available or specific questions arise, the analysis may need to be revisited and refined.

Advantages of Descriptive Analytics

  1. Historical Insight: Provides a clear understanding of past events and trends.
  2. Simplicity: Relatively easy to implement and understand, making it accessible to a wide range of users.
  3. Decision Support: Helps in making informed decisions based on a solid understanding of past data.
  4. Data Visualization: Utilizes graphs, charts, and other visual aids to convey information effectively.
  5. Performance Monitoring: Allows for the tracking of key performance indicators (KPIs) and performance trends.
  6. Baseline Establishment: Serves as a foundation for more advanced analytics techniques like predictive and prescriptive analytics.

Disadvantages of Descriptive Analytics

  1. Limited Predictive Power: Focuses on historical data and may not provide insights into future trends or outcomes.
  2. Lack of Context: Doesn't always offer explanations for why certain trends occurred, requiring additional analysis.
  3. Inflexibility: Limited in its ability to adapt to changing circumstances or predict unexpected events.
  4. Dependency on Data Quality: Relies heavily on the accuracy and completeness of the underlying data.
  5. Potential Oversimplification: May not capture the full complexity of certain situations or phenomena.
  6. Not Action-Oriented: While it informs decisions, it doesn't necessarily provide specific recommendations for action.

Descriptive Analytics vs Predictive Analytics

Descriptive Analytics and Predictive Analytics are two distinct stages in the data analysis process, each serving different purposes. Here are the key differences between them:


Descriptive Analytics

Predictive Analytics


Descriptive analytics focuses on summarizing historical data to provide insights into what has happened in the past.

Predictive analytics aims to forecast future events or outcomes based on patterns and trends identified in historical data.


Deals with historical data and provides a snapshot of past events and trends.

Looks ahead and attempts to make educated guesses about future trends and events.

Methods Used

Utilizes summary statistics, data visualizations, frequency distributions, and other techniques to distill large volumes of data into manageable and interpretable information.

Employs statistical modeling, machine learning algorithms, regression analysis, and time series forecasting to make predictions about future outcomes.


Generating reports on sales performance, visualizing website traffic, creating dashboards of customer demographics.

Predicting customer churn, forecasting sales for the next quarter, estimating future website traffic based on historical data.

Level of Insight

Offers an understanding of "what is."

Focuses on "what will be."


Often forms the foundational step in the data analytics process, providing context and understanding of past events.

Builds upon the insights gained from descriptive analytics, using historical data to make informed forecasts about future events.

Decision-Making Support

Aids in understanding historical trends and patterns, providing a basis for decision-making.

Assists in making forward-looking decisions by providing forecasts and predictions.