Business Intelligence Is Rapidly Evolving Into Decision Intelligence

For many decades now, Business Intelligence (BI) tools have been used to build enterprise reports and dashboards. We have all seen managers staring intently at these displays, drilling into information about why a traffic light is red and basing their business decisions on the insight created by these tools.

There are now many different BI tools available. Microsoft Power BI, Qlik and Tableau are all recent examples, but you can also look back in time to Cognos and BusinessObjects. These tools have been refined and improved and a generation of managers has consistently used them for decision support since the era of a PC on every desk.

But a very interesting post on the technology news site TechTarget recently explored the limitations of Business Intelligence and how we need to help it evolve. The big problem is the flood of information. Most BI platforms offer insight on everything taking place inside a business. Every possible Key Performance Indicator (KPI) is measured and reported in real-time leaving the manager with a sea of data, but not necessarily any insight or suggested actions.

Wayne Eckerson, founder and principal consultant of Eckerson Group, talked about this on a webinar recently and highlighted several key challenges, including:

• The input is entirely historical. You have a sea of data, but it’s always reporting what happened in the past so the insight into what may happen in future has to come from the intuition and experience of the manager.
• It’s usually manual. The manager has the charts and reports, but often has to sift through them looking for trends or anomalies.
• There is no prediction of future outcomes and very little automation.

This is a challenge. It can’t be resolved by hiring more analysts. If the problem is that the data looks backwards and is overwhelming then creating more reports and dashboards isn’t going to help.

Business Intelligence needs to evolve into Decision Intelligence (DI).

Companies using BI platforms need to be able to apply Artificial Intelligence (AI) and Machine Learning (ML) to the flow of data so input can be matched to events – the system needs to learn which variables inside the business will change outcomes.

The TechTarget analysis suggests that managers with a DI approach can increase their productivity by 10-100 times. That’s a bold claim, but it comes from empowering each analyst or manager that is reviewing the BI data. By adding a DI layer, millions of datapoints can be analyzed and compared to historical outcomes. The system can make predictions and warnings about potentially negative outcomes. It can also directly suggest required actions to improve the situation.

This is not a replacement for BI, it’s more like an evolution. A good analogy is the modern customer contact center. Typically a customer calls a company and explains their problem then the agent has to figure out a solution. This may involve searching manuals or other resources – the customer is usually placed on hold as this search takes place. Now it is common to apply an AI ‘agent’ that listens to the conversation and locates the required information in real-time as the customer and agent are speaking – so there is no need to break the conversation and search for an answer because it will be presented on the screen of the agent as the most likely solution to the problem.

This is how BI is evolving. Instead of just offering the capability to monitor a business, it can evolve and start indicating which decisions may lead to different outcomes. It allows the real-time predictions of what may change if specific decisions are applied right now.

Managers monitoring a large dashboard of activity will welcome this evolution to DI. It is like giving them a 24/7 digital assistant that can check and monitor millions of different decision outcomes in real-time.

I recently wrote in this blog about the Business Intelligence (BI) opportunities for retail companies. Retail brands that use BI to get closer to what their customers want can reduce cart abandonment rates and offer discounts or special offers designed for the individual customer. Read more about BI in this blog.