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Understanding the Difference between Descriptive, Diagnostic, Predictive, and Prescriptive Analytics

In this age of digital content, the volume of data that enterprises generate, process, and analyze, is humongous. Data Analytics is being used by enterprises across various industry verticals and sizes to extract meaning and insights out of the raw data. The influx of data has led businesses to use Business Intelligence tools that delve deep into them and uncover relevant patterns.

Modern Data Analytics is broadly classified as:

  • Descriptive Analytics: What is happening in your business?
  • Diagnostic Analytics: Why is it happening to your business?
  • Predictive Analytics: What is likely to happen in the future of your business?
  • Prescriptive Analytics: Determine the best course of action to eliminate future issues.

Let’s understand the differences between these four categories of Data Analytics and how they help organizations meet business objectives.

Descriptive Analytics

As the title indicates, Descriptive Analytics gives answers to “what is happening in the business?” It is primarily centered around past events or all that has happened during the past operative months. The analytics goes on to understand the overall performance at an aggregate level.

It can be understood from the perspective of review and analysis as it starts with all available data to build reports, and move on to build analytical models. The process usually begins by identifying KPIs as benchmarks for performance in a business area such as operations, sales, or finance. Data sets that make maximum impacts are identified, collected from appropriate sources, and readied for formulating models.

Experts use different methods to identify patterns and measure performance such as tracking, clustering, statistics summarizing, and regression analysis. Finally, data visualization is carried out for a quick understanding of all that is collected and analyzed.

Diagnostic Analytics

This analytics uses historical data to answer the question, “why did it happen?” Typically, for any business, there are multiple factors that contribute to any trend in a business. Diagnostic Analytics reveals the full spectrum to give the complete picture, indicating factors that have maximum impact. This type of analytics delves deeper than Descriptive Analytics to bring out correlations. Capabilities of Diagnostic Analytics lead experts to carry out data mining and identify potential sources of anomalies.

At times, it becomes necessary to compare and use external data for a complete and informed Diagnostic Analytics. The overall objective is to identify outliers, isolate patterns, and uncover relationships. Identifying behaviors of profitable customer segments can reveal why they are spending on a particular product segment. Similarly, comparing high-performance platforms generating revenue from advertisement with those performing poorly will bring out the reasons.

Predictive Analytics

Again, as the title indicates, this form of analytics is all about predicting “what is going to happen?” – but based on historical data.

Predictive Analytics uses Descriptive Analytics and Diagnostic Analytics as its aim is to predict trends and patterns in businesses. It starts with identifying problems that need to be resolved. Experts begin by defining what is to come and state what is going to be achieved by it. Cleansing data, reviewing data quality, and finally determining modeling objectives are at the crux of this analytics.

The process brings together data mining methodologies, forecasting methods, and predictive models apart from analytical techniques. It analyzes current data, assesses risks and opportunities, and maps relationships to make precise predictions about future operational happenings.

For example, in a manufacturing setup, by collecting data from machinery operations and their digital twins, analytics can predict which parts are most likely to need repair and maintenance and when. This type of predictive maintenance analysis helps improve operational efficacies without letting businesses lose productive time.

Prescriptive Analytics

This is the final Analytical model that pertains to guide models to help take specific actions. Prescriptive Analytics is the effective merging of Descriptive Analytics, Diagnostic, and Predictive Analytics that together drive decision-making. It uses advanced tools and technologies like Machine Learning and algorithms that, in congruence with business rules, make sophisticated analytics manageable. The best Predictive Analytics requires both historical internal data and external information due to the nature of the algorithms it’s based on.

Prescriptive Analytics calls for quantifying call-to-action and underlying the criteria for doing it. It is characterized by techniques such as simulation, graph analysis, complex event processing, Machine Learning, Neural Networks, Heuristic Deep Learning, and Recommendation Engines. Prescriptive Analytics can help businesses cut through the clutter of immediate uncertainties of changing conditions. The analytics results can help operations meet business goals and increase efficiency by limiting risks.

Ways to improve sales for target verticals, and knowing what to promote next are good examples of how Predictive Analytics help optimize operations. It has more to do with trial-and-error but is based on internal and external historical data that gives it concrete scientific backing. Dwindling sales can be handled with decisions to cut prices, expand marketing, or by discontinuing the product with the help of Predictive Analytics which has already explored past data and built a prescriptive model.


Forward-thinking organizations are making smart decisions using all these analytical models that cover the full ambit of business operations. These analytical models need to work in sequence for any business to derive the full benefits of data analytics.

At Ascentt, we make use of the latest algorithms to formulate models that utilize historical data from internal and external sources. As we analyze and extract value from operational information, we help businesses streamline their business operations with the best analytical results. Beginning with Descriptive Analytics to identify operational bases, we move to Diagnostic Analytics to ask about the current scenario and find out why things are happening the way they are. Next, we move on to Predictive Analytics using the prior databases to point out potential strengths and weaknesses. The final rounding-off takes place with Prescriptive Analytics suggesting the best course of action to augment business and revenue growth.

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