Descriptive vs Predictive vs Prescriptive vs Diagnostic Analytics: A Quick Guide

Descriptive vs Predictive vs Prescriptive vs Diagnostic Analytics

Data analytics has changed dramatically with the advent of cloud computing. The Snowflake Data Cloud as well as modern cloud data platforms such as Amazon Redshift make it possible to load large data sets and prepare them for analysis in a matter of seconds. Data-driven companies can now access more data and dig deeper into the analysis.

These capabilities allow organizations of any size to use all four types of analytics today to answer any questions.

  • Descriptive Analysis: This tells you what has happened in the past
  • Diagnostic Analytics: This will help you understand the reason something occurred in the past
  • Predictive analytics: Which predicts What’s the most likely future outcome?
  • Prescriptive Analytics: recommends actions you could take to influence those likely outcomes
    Let’s look at predictive, diagnostic, prescriptive, and diagnostic analytics, and how they relate.

What is Descriptive Analytics?

Descriptive analysis is often the first step in business intelligence. It uses data mining and data aggregation to organize historical data and produce visualizations like line graphs and bar charts. Descriptive analytics provides a clear picture of the past as does statistical modeling. However, it stops there. It doesn’t offer any advice or interpretations.

What does descriptive analytics show?

It is useful to find answers to simple questions about the past. This type of analytics involves identifying KPIs that are benchmarks for performance in particular business areas (sales, finance, and operations). The next step is to determine the data sets that will be used in your analysis, and then gather and prepare them.

To see patterns and measure performance, you’ll use a variety of methods, including pattern tracking, clustering, and summary statistics. To make data more understandable and quick, you will create visualizations. This type of analysis can be done by anyone without any programming or SQL skills thanks to Sigma.

Examples of descriptive analytics

Descriptive Analytics is a tool that can be used to benefit decision-makers in every department of a company, including finance and operations. Here are some examples:

  • The sales team which customer segments made the most sales in the last year by visiting’s the sales team.
  • The marketing team can determine which social media platforms provided the highest return on advertising investments last quarter.
  • The finance team can monitor month-over-month and year-over-year revenue growth or decrease.
  • Operations allow you to track the demand for SKUs in different geographic locations over the past year.

Also read: Analytical Report – What Is It and How to Write It

What is Diagnostic Analytics?

Once you have the facts, you will want to understand why. Diagnostic analytics is where you come in. Your business intelligence will be actionable if you understand why a trend is occurring or what caused the problem. It prevents your team from making inaccurate guesses, particularly related to confusing correlation and causality.

What does diagnostic analytics show?

There is usually more than one factor that contributes to a particular trend or event. The full scope of causes can be revealed by diagnostic analytics, so you can see the whole picture. It can help you identify the most important factors and pinpoint them. Diagnostic analytics uses the same techniques as descriptive analytics but will use drill-down and correlations. To fully support your analysis, you may need to add data from outside sources. This is easy with Sigma, especially when linked to Snowflake’s powerful capabilities.

Note: Diagnostic analytics, also known as root cause analysis, is used to determine the source of business problems and provide solutions that will prevent them from occurring in the future.

Examples of diagnostic analytics

Every team can benefit from diagnostic analytics. These are just a few examples.

  • The sales team is able to identify common characteristics and behavior of profitable customer segments, which may help explain why they spend more.
  • Marketing team to identify reasons for performance differences, the marketing team will look at the unique characteristics of social media ads that perform well compared to those that do not.
  • The finance team can help to determine correlations by comparing key initiatives with month-overmonth and year-over-year revenue growth.
  • Operations can examine regional weather patterns to determine if they are contributing to the demand for certain SKUs across geographical locations.

What is Predictive Analytics?

Understanding the past will help you understand how it happened and why, can you predict what is likely to happen in the future? Based on this information. Predictive analytics is a way to take the investigation one step further. It uses statistics, computational modeling, and machine learning, in order to determine the likelihood of different outcomes.

What does predictive analytics show?

Predictive analytics forecasts future outcomes and predicts when they will occur. It assists organizations in better planning, goal setting, and avoiding unnecessary risk. This allows teams to predict future performance more accurately based on past performance as well as all factors that affect it.

What-if analytics, which is the process of changing values to determine how they will impact the outcome, is one of the most important forms of predictive analytics. Business teams can make faster decisions when they are able to conduct fast, iterative analysis in order to evaluate different options. This capability was built into Sigma.

Examples of predictive analytics

Because it gives decision-makers more confidence about the future, predictive analytics is particularly powerful for teams. Here are some examples:

  • A sales team can determine the potential revenue of a customer segment.
  • A marketing team can forecast how much revenue they will generate from a future campaign.
  • The finance team can make more precise projections for next year.
  • The operations group is better equipped to predict the demand for different products in different areas at certain points during the year.

Also read: Unlimited Guide Data Analytics for Beginners

What is Prescriptive Analytics?

Prescriptive analytics is where the action is. This type of analytics shows teams what to do based on the predictions. It’s the most complex type, which is why less than 3% of companies are using it in their business.

Although AI in the prescriptive analysis is making headlines right now, this technology still has a lot to learn in order to produce actionable, relevant insights. To use AI at scale, you need to run thousands of queries in order to find statistical anomalies. However, randomly identified anomalies are not always indicative of business opportunities.

Uncover meaningful business insights, at least until AI technology improves will still require human involvement. This means that data must be analyzed in the context of company goals, market trends, business processes, and company objectives.

What does prescriptive analytics show?

Prescriptive analytics predicts when and where an event or trend will occur. It helps you determine which actions are most likely to lead to the best outcomes. It helps teams solve problems, increase performance, and seize on opportunities.

Examples of prescriptive analytics

Although prescriptive analysis requires a lot of data, it is still practical for everyday use. Take this example:

  • A sales team How to improve the sales process for each target vertical by the sales team
  • A marketing team Helping the Marketing Team decide which product to promote in the next quarter.
  • The finance team Ways that the finance team can optimize their risk management.
  • operations Help the operations group to determine how to optimize warehouses.


Business intelligence is now possible thanks to the cloud, Big Data storage, and analysis. Cloud data analytics tools allow business decision-makers to gain the insight they need to improve their performance and make better decisions. Sigma allows even non-technical users to conduct rigorous analyses and answer key follow-up questions. This will allow them to uncover the reasons behind trends and predict future outcomes.

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