Operational BI - Part 1

The genesis of BI has always been the need to seek for the BIBLE of decision making. But BI over a decade has transformed itself from a night watchman to more of a 24/7 call-center representative. It has become real-time. What made this change? Why was the mutation to real-time necessary? What are the challenges in data integration? And finally, how can Operational BI (O-BI) be coupled with the Enterprise Analytical Reporting framework? I will be assessing each of the questions posed in greater detail and arrive at a design pattern for modeling a Operational BI Solution.

Let us drive the need for implementation of an operational BI solution with an example.

A store manager at a retail outlet manages various aspects of retailing - visual merchandising, customer experience, resource scheduling, loss prevention, product management (ordering, receiving, pricing, inventory). Let me explain each one of these facets of the retailing business briefly.

• Visual Merchandising: Promotion of the sale of good through visual appeal in the stores (source: Wikipedia).

• Customer Experience: Reduced customer wait-time in the check-out counters.

• Resource Scheduling: Monitoring the efficiency of the employee schedule for improved load balance of employee work-hours.

• Loss prevention: Real-time monitoring of 'shrinkage' because of shoplifting, employee embezzlement, credit card fraud, system errors and many more.

• Product Management: Real-time monitoring of product inventory.

Given this background, I would proceed on to connect all these process areas with a business case that would put a Store Manager in trouble and how an Operational BI solution can save his day.

Let us assume that the Store Manager has access to a reporting solution which refreshes once in a day. He notices that the daily sales has dropped as compared to the previous day. He drills further down to investigate the cause of the decline. He finds out that the drop can be traced to one particular hour in the day. A deeper look into the problem highlighted the issue of an increased average customer wait-time per hour causing a poor conversion rate. The wait time finally was attributed to reduced work-force in that hour because of an increased lunch break taken by the employees (since they turned up very early to work).

This problem could have been easily rectified if the store manager had access to data earlier than what he had. Had he had real-time access, he would have noticed the dip in sales for that hour immediately and would have taken corrective action, thereby not affecting the sales during that hour. With a decent business case established for a real-time BI system, let's analyse what an operational BI is and how does it facilitate to solve the problem.

The architecture of Operational BI and the challenges associated with it will be posted in the next article.