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Types of Analytical models for Organisations

Learn more about the types of analytical models for organisations.

Even though you are not expected to learn about them in detail, it is good to know about the main four analytical models organisations can always deploy in the process of data analysis:

  1. Descriptive analytics
  2. Diagnostic analytics
  3. Predictive analytics
  4. Prescriptive analytics

Descriptive Analytics

Descriptive analytics answer the question: What happened? This is the most common type of analytics found in business. It generally uses historical data from a single internal source to pinpoint when an event occurred.

For example:

  • How many sales did we make in the last week/day/hour?
  • Which customers required the most help from our customer service team?
  • How many people viewed our website?
  • Which product had the most defects?

Diagnostic Analytics

Diagnostic analytics helps us to answer the next question: Why did it happen?

To do this, analysts dive deeper into an organisation’s historical data, combining multiple sources in search of patterns, trends, and correlations.

For example:

  • Why have sales increased in a region that had no change in marketing? (Identify anomaly.)
  • What are the patterns outside existing data sets that can be used to identify correlations? (Drill the data down – data mining.)
  • Are the identified correlations actually related? (Advanced statistical analysis.)

Predictive Analytics

Predictive analytics sits at the intersection of classical statistical analysis and modern artificial intelligence (AI) techniques and attempts to answer the question: What will happen next?

As an organisation increases its analytical maturity and embarks on predictive analytics, it shifts focus from understanding historical events to creating insights about a current or future state.

For example:

  • What will be the effect of specific maintenance operations on aircraft reliability, fuel use, availability, and uptime? (Aerospace.)
  • What credit risk model should be developed using forecast and market trends? (Finance.)
  • When and where will the automated machines fail next? (Manufacturing.)
  • How will the additional product information or incentives increase the product sales? (Retail.)

Prescriptive Analytics

Prescriptive analytics is the most complex type of analytics. It answers the question: What will happen next, and why will it happen?

This process is both complex and resource-intensive but, when done well, can provide immense value to an organisation.

For example:

As the most complex form of analytics, prescriptive analytics not only pose technical challenges, but are also influenced by external factors such as government regulation, market risk, and existing organisational behaviour. If you are considering deploying prescriptive analytics, be sure you have a solid business case that identifies why machine-generated recommendations are appropriate and trustworthy for each decision to be made.

Consider this

With the different types of analytics, the more value they provide, the more complex they are to implement. Many organisations progress ‘up’ the levels of analytics, starting with descriptive analytics.
What are the benefits of progressing in this fashion?


1. Hare J. Use prescriptive analytics to reduce the risk of decisions [Internet]. Forbes; 2016 Mar 24. Available from:

2. Kuttappa S. Prescriptive analytics: The cure for a transforming healthcare industry [Internet]. IBM Big Data Hub; 2020 Apr 14. Available from:

3. Dent C., Burns D., Sherrard S. Do this, not that: Prescriptive analytics in sales and marketing [Internet]. Bain & Company; 2019 Aug 27. Available from:

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