Where is the promised intelligence in BI?
In the data centric landscape of business intelligence, how could we aid decision making with the help of process discovery?
It’s a common practice to use the past information and to use it for future decision making by analysing it. However, it’s not as simple as it sounds, especially when the goal is to make faster, better and smarter decision. That’s even harder, when different people involved in decision-making and all of them want to see different aspect of the information cubes.
Daily business operations generate tons of different kind of raw information, which is difficult to analyze. To gain insights from such raw data this data needs to be processed and then to be presented to decision maker in a clear manner.
That’s what the business intelligence (BI) landscape does, generating insights from the raw data. Not surprisingly, just like anything else, the landscape of BI has evolved over time. In 1960’s it was coined as decision support systems, in 1980’s BI was combination of executive support system, online analytical process engine and decision support system and in 1990’s BI was an umbrella term.
Today, BI is huge space that involves process, technology, tools, methodologies, applications and set of skills. But the goal is still the same as it was a few decades ago, make better decisions and create value.
In today’s business intelligence landscape, everything in the BI is very data centric and end user often see value in the form of dashboards, scorecards and analysis. Targeted KPI are tracked in dashboards and reports but those KPIs do not show how actual process works. What if we are interested in the process related issues hidden behind the data? Therefore, there is lot more to add to BI to produce intelligence out of it.
Luckily, process mining can help; process mining bridges the gap between process modelling and data mining. In simple words, it’s a missing link between model based process analysis and data-centric analytical techniques. Process mining can play very vital role on the analytical side of the BI where traditional BI reporting does not provide in-depth analysis to uncover the root cause of the problem.
For instance, if your BI dashboard indicates loss in the sales then process mining approach allows you to dig right into the process and analyze the root causes (e.g., deviations in the process, bottlenecks etc.) of decrease in sales.
Process mining has huge potential to create the value that BI vendors are promising and also can aid the decision making by taking static data centric analysis to process centric analysis.
It might be too early for vendors to adapt process mining, as a necessary part of the BI landscape but that could be the key factor that vendors have to take into consideration in the near future.