The smarter we get, the harder it is to decide what to do

The Digital Oilfield and the Intelligent Grid: two initiatives in different areas—Energy and Utilities—that focus on improved monitoring and management of the entire production value chain. In other industries too, there’s a buzz about putting more intelligence into operations. By making devices smarter, using sophisticated communication technologies, and employing software tools to gather, integrate, and present information to decision-makers in an appropriate way, the thinking goes, you’ll empower them to manage their operations more effectively.

But we’ve forgotten something in all this buzz. Putting smart devices into the field generates an enormous amount of data that has to be processed and analyzed, and then somebody has to make decisions about what to do. Unfortunately, in most cases we’re not improving the decision making process itself.

Here’s what Deloitte said in 2006 about the Digital Oilfield: “The volume, frequency and complexity of data available drives a major need for organizational change. The tools, capabilities and skills required to analyse and act on the incoming data is significantly different from those expected of head-office staff in the past.” And in the October 2007 “In the Know” newsletter, Energy Insights writes about the Intelligent Grid: “Yet we find many utilities hesitating to invest in these sensors because they face …concerns about how to deal with the tidal wave of data [they] will bring ….”

The problem here is that while we’re embedding a lot of smarts into the front end of the decision process (in operations), we’re still relying on a human user to sift through all the data and analysis before actually make a decision on an appropriate course of action. That approach runs out of steam in a typical operational setting, since there’s too much data, and too much going on, for a user to keep up (think about playing checkers versus playing chess: it’s harder to play chess because there are a lot of different pieces and types of moves possible, so when you try to anticipate what your opponent might do at any given point in the game, there’s a lot going on in your head). While we’re getting smarter on the data side, we’re actually not doing much in terms of making more intelligent decisions.

We need to rethink the approach to decision making, and make that smarter too. We need to use software tools that take the complexity burden off the user, so that the process is speedier and optimized for success. This means automating parts of the actual decision process, and showing the user what the impact of their various decision alternatives might be. It also means balancing the power of software to process large quantities of decision data against the expertise and insight of knowledgeable users, so that decisions are made faster and are more accurately aligned with the real world.

We need to make it easier to decide what to do.