Beyond Business Intelligence
Recent financial news in the enterprise software sector is bubbling with stories about behemoths like IBM, Oracle, and SAP gobbling up Business Intelligence (BI) vendors like Cognos, Hyperion, and Business Objects, respectively.
This isn’t a surprise, given the need to add new sources of revenue to meet shareholder growth expectations. For the customer and the acquiring vendor, it makes perfect sense to add BI technology that extracts incremental value from previous investments in data-generating technology stacks.
But is BI really the top layer on the technology stack? Does it provide everything users need to make decisions in complex, rapidly changing conditions?
First, let’s look at what BI does.
Wikipedia says: “In modern businesses, increasing standards, automation, and technologies have led to vast amounts of data becoming available ... Business intelligence has now become the art of sifting through large amounts of data, extracting pertinent information, and turning that information into knowledge from which actions can be taken ...”
But, this misses the punch line. In other words, what action do you actually take after sifting through large amounts of data and extracting “pertinent” information? Making good decisions requires more than analysis.
BI does not suggest possible courses of action, given a complex decision-making environment. In this case, software should help you decide what action to take when you are focused on allocating resources to meet an objective, subject to a series of constraints. Also, the software should be interactive so the user can include subtle factors such as intuition, preference, and ad hoc priorities into the decision making process.
How do we assess the potential impact of our decisions when there are hundreds, or even millions of feasible decision scenarios to evaluate in a compressed timeframe? This challenge is most acute in decision making scenarios like scheduling, planning, and optimization. And, the return on investment for technologies that can assist with this type of decision support is greatest in asset-intensive industries where operating conditions change frequently and often unpredictably. I predict further innovation in the area of decision support technologies for asset-intensive industries that use techniques from operations research and artificial intelligence to build on the value that BI provides.
Characteristically, the way forward is complicated owing to overlapping terms and concepts. For example, decision support solutions fall into a large number of evolving categories such as “manufacturing operations management”, “enterprise manufacturing intelligence”, “production operations management”, “integrated manufacturing execution”, and “event driven, closed loop performance management”. All of these categories offer support for complex decision making and all will need more than BI to do the job.
