How Descriptive and Predictive Analytics Drive Decision Making

Data Analysis

Mar 21, 2024 | By Ananya Chakraborty

How Descriptive and Predictive Analytics Drive Decision Making

In the fast-paced world of data analytics, every decision we make, from strategic business plans to personal choices, is in fact harnessed via the mighty data. But have you ever paused to think about what is guiding these decisions? Enter Descriptive and Predictive Analytics, our personal time machines in the realm of data. 

In today’s blog post, we will dig deeper into the world of data analytics and learn more about: 

  • Role of descriptive & predictive analytics in decision making
  • Descriptive vs. Predictive Analytics: The key differences that set them apart
  • Applications & Use Cases

Importance of Descriptive and Predictive Analytics in Data Science

Imagine owning a time machine that allows you to look into the past and future per your requirements. That's the power these analytical techniques (descriptive and predictive analytics) hold. Predictive Analytics uses past data to forecast what's on the horizon. At the same time, our retrospective storyteller, Descriptive Analytics, paints a picture of past events, helping us understand the 'what'  of our data story.

Descriptive Analytics: Looking Back to Understand the Present

Descriptive analytics can help you figure out the "what" part of the data in data analytics. With the help of historical data, descriptive analytics can help you uncover what happened in the past.

In this type of analytics, we start by analyzing the historical data to understand any trends, patterns, and anomalies to make effective decisions. 

Benefits of Descriptive Analytics

  1. Understanding the Past: It provides a comprehensive view of past behaviors and trends, enabling us to learn from history.
  2. Identifying Patterns: It helps in recognizing patterns and trends that might be overlooked otherwise.
  3. Informing Future Decisions: The insights derived from Descriptive Analytics can guide future decisions, laying a solid foundation for Predictive and Prescriptive Analytics.
  4. Enhancing Sector-Specific Outcomes: Whether it's improving business performance, healthcare outcomes, or educational strategies, Descriptive Analytics plays a crucial role in driving sector-specific improvements.

Steps Involved in Applying Descriptive Analytics

  1. Data Collection: The first step in descriptive analytics is gathering the data you want to analyze. This could be sales data, customer data, operational data, etc.
  2. Data Cleaning: Once the data is collected, it needs to be cleaned. This involves handling missing values, removing duplicates, and correcting inconsistencies.
  3. Data Analysis: We analyze the data using various statistical methods after the data cleaning process. This could involve calculating averages, percentages, and ratios or more complex statistical analysis.
  4. Data Visualization: The results of the analysis are often visualized using charts, graphs, and other visual tools. This makes it easier to understand and interpret the results.
  5. Data Interpretation: The final step is interpreting the results and drawing conclusions. This could involve identifying trends, spotting anomalies, or making comparisons.

Limitations of Descriptive Analytics

  • It only tells you what has happened, not why it happened or what will happen in the future.
  • The insights are dependent on the quality and accuracy of the data being analyzed. 

Practical Use Case: Spotify 

Spotify, a popular music streaming service, implements descriptive analytics to understand the listening habits of its regular users. By analyzing historical data, Spotify identifies patterns and trends, such as which albums or artists are most popular, what time of day people listen to music, and how listening habits vary by different regions. This information is invaluable for Spotify as it helps them tailor their services to the preferences of their users, enhancing user experience and engagement of the music platform.

Predictive Analytics: Anticipating the Future

Predictive analytics is like a crystal ball in the realm of data analytics. It uses statistical models and machine learning algorithms to predict future outcomes based on historical data. 

Benefits of Predictive Analytics

  1. Forecasting Future Outcomes: It enables businesses to anticipate future events, trends, and customer behaviors.
  2. Risk Assessment: It aids in identifying potential risks and challenges that a business might face in the future.
  3. Improving Efficiency: Predictive analytics can help businesses streamline operations, manage resources better, and reduce costs.
  4. Sector-Specific Advancements: From optimizing logistics in supply chain management to predicting patient outcomes in healthcare, predictive analytics has a wide range of practical applications across various sectors.

Steps Involved in Predictive Analytics

  1. Data Collection: Predictive analytics also starts with the data collection process. However, in this case, we need historical data that we can use to train predictive models.
  2. Data Preprocessing: The collected data is then preprocessed. This involves cleaning the data, handling missing values, and transforming variables if necessary.
  3. Feature Selection: Not all variables in your dataset can make predictions. Feature selection involves identifying the most relevant variables that are included in the predictive model.
  4. Model Building: Once the features are selected, a predictive model is built using statistical or machine learning techniques. This involves training the model on a subset of the data.
  5. Model Validation: The predictive model is then validated on a different subset of the data (aka the validation set). This helps us test how well the model will likely perform on unknown data.
  6. Prediction: Once the model has been validated and fine-tuned, it can be used to make predictions on new data.
  7. Model Updation: Predictive models often need to be updated over time as new data becomes available and underlying patterns and trends change. This involves repeating the model-building, validation, and prediction steps.

Limitations of Predictive Analytics

  • The accuracy of predictions can be influenced by the data quality, the choice of the model, and the assumptions made during the analysis.
  • The predictions are probabilistic, meaning there's always a degree of uncertainty involved.

Practical Use Case: BigBasket

BigBasket, a leading online grocery store in India uses predictive analytics to understand the future shopping needs of its customers. By analyzing past purchase history and browsing behavior, BigBasket can predict what products a customer might be interested in buying in the future. Concepts like Smart Basket aka Order Again analyze our past purchases to understand what we need today. This allows BigBasket to personalize product recommendations, improving the shopping experience for customers and increasing sales for the company. Moreover, BigBasket can also forecast demand for different products, helping them manage inventory more effectively.

Descriptive vs. Predictive Analytics in Data-Driven Decision-making

While Descriptive and Predictive Analytics play crucial roles in interpreting data, they offer different insights and serve other purposes.

So, when should you use each type of analytics for making informed decisions? 

Descriptive Analytics is useful when you need to understand past behaviors and analyze how they might influence future outcomes. It's the first step in data-driven decision-making, helping you understand where you are now.

Predictive Analytics is useful when you want to forecast future outcomes. It helps you understand where you're going, allowing you to take proactive measures and make informed decisions about the future.

The Analytics Pipeline: A Cyclical Journey, Not a Linear Path

Data analytics often discusses the analytics pipeline as a linear path from descriptive to predictive to prescriptive analytics. However, in practice, it's often more of a cyclical journey. You might start with descriptive analytics to understand your data, then move on to predictive analytics to forecast future trends, and finally use prescriptive analytics to make informed decisions. But along the way, you might cycle back to descriptive analytics as you uncover new insights and refine your models.

For instance, a streaming service like Netflix uses descriptive analytics to understand viewers' past behavior, such as the genres and types of shows they watch. Then, they use predictive analytics to forecast what those viewers might want to watch in the future. Based on these predictions, they recommend shows (prescriptive analytics) that viewers might enjoy. But the process doesn't stop there. Netflix continually refines its recommendations based on viewers' feedback and new viewing data, cycling back to descriptive analytics. This process isn't linear; it's cyclical. You continually cycle back, adjusting your path based on new information.


In conclusion, Descriptive and Predictive Analytics are powerful tools that help us navigate the complex landscape of data science. They provide us with insights into the past and future, empowering us to make informed, data-driven decisions. Whether you're a business owner, a healthcare professional, or an educator, these analytics can provide valuable insights to guide your decisions.

Key Takeaways

  • Descriptive Analytics provides a clear understanding of past behaviors and trends, serving as the foundation for data-driven decision-making.
  • Predictive Analytics forecasts future outcomes, enabling us to anticipate trends and make proactive decisions.
  • The data science journey is cyclical, often involving a cycle between descriptive and predictive analytics.
  • Effective decision-making involves leveraging both types of analytics and understanding their strengths and limitations.

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