Decision Intelligence vs. Business Intelligence

With Decision Intelligence you are ready to act whilst Business Intelligence users are still pondering over statistics.

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October 10, 2022
minutes read
Decision Intelligence
Business Intelligence

Looking at how a company handles its data helps to explain the relationship between Business Intelligence (BI) and Decision Intelligence (DI). The evolution of technology and changing approaches to data analytics have not replaced each other, rather they build on each other. Taking a company’s revenue as an example, this relationship can be illustrated as follows:

  • Descriptive Analytics describes how sales have developed in the past.
  • Diagnostic Analytics analyzes why sales have developed positively or negatively.
  • Predictive Analytics predicts how sales will develop. - Prescriptive Analytics recommends how to ensure that sales grow at an optimal rate

Business Intelligence works using Descriptive Analytics and Diagnostic Analytics and Decision Intelligence additionally uses Predictive and Prescriptive Analytics. Whilst BI analyzes structured data to describe and diagnose business processes, DI accesses all data, including unstructured data, to create forecasts and make recommendations for action or executes them automatically.

The limits of Business Intelligence

It is evident that BI has reached its limits due to the exponentially growing volumes of different forms of data. Eckerson Group data analysts define five key limitations of Business Intelligence:

  1. BI works with historical data instead of real-time data.
  2. BI only shows summary trends and patterns and so can miss subtle nuances.
  3. BI requires the user to sift through a variety of different types of data to find relevant information.
  4. BI is not able to predict future events or prescribe solutions to those events.
  5. BI is not automated.

The result of the above is data congestion that impairs or even hinders decision-making. The huge amount of data is too much to be managed by humans and its complexity exceeds the capabilities of self-service users. Companies would have to hire armies of data analysts to manage this and even then it is very doubtful that it would fix the problem in the long run.

DI provides competitive advantages

The solution to this problem is DI. DI uses Artificial Intelligence and machine learning to monitor data 24/7. The system displays any changes in metrics that occur, performs root cause analyses, offers courses of action and autonomously executes prescribed responses via automated processes.

The use of DI in any enterprise spurs business success and provides companies with competitive advantages by:

  • using complex amounts of data;
  • keeping processes flexible;
  • shortening decision-making processes;
  • making correct and value-free decisions;
  • making decisions measurable.

Of course DI doesn’t mean leaving every decision to the machines, but combining human experience and intuition with automation takes decision-making to a whole new level.


Those who use Business Intelligence to assess their key figures must draw any conclusions themselves. DI, on the other hand, takes data analysis from mere description to forecast and recommendation. This allows the user to react to current developments in real time and choose between data-based decision alternatives.

Four Types of Analytics Capability Gartner
Source: Gartner (2014); Four Types of Analytics Capability 
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