Optimal Decision-Making: Turning Data into Actionable Information

Optimal Decision-Making: Turning Data into Actionable Information
By Claire Schwartz

In my last post, I wrote about collecting data to provide information for decision-making. There is no question that data is where we begin, but facts are not enough. What we really need is information—a meaningful interpretation and presentation of the data that gives us insight into a condition or situation.

For example, when I manage a project, I collect data about task performance, such as “Task A” started on January 5 and finished on January 12, it took 27 hours of effort and we spent $3000 on travel.

Interesting facts, but they really don’t tell me enough to evaluate the task’s performance, its impact on the project, or help me make decisions about the remaining work or cost.

It turns out that “Task A” was scheduled to complete on January 14, making it ahead of schedule. Unfortunately, it isn’t on the critical path, so there’s no impact to the project schedule. We had planned to use 25 hours to complete the work, but even though we were two hours over our plan for the task, we’ve been running significantly below our labor estimates on the work performed to date, and we’re already about halfway to completion. Now for the bad news: the total travel budget for the project is $5000 and we still have two more trips planned. What was supposed to be a one-day trip, costing $1000 for three team members, turned into a three-day trip, costing $3000 because the team got stranded in a Chicago blizzard.

So what makes the second paragraph more useful than the first? In general, the second paragraph tells a complete story that informs, highlights what’s important and clearly identifies the actions or decisions needed. If we further dissect that paragraph, we can see a few specific attributes that make it more meaningful and useful: content, context, contrast and consequence.

• Content: In the first paragraph, we know the facts, but the second paragraph takes the time to combine the facts into a story. By adding a little more detail, or content, the reader has a better grasp of “the five W’s”: who, what, where, when and why. Our desire to be brief and direct often results in repeated question-answer cycles, leaving the decision-maker to ferret out the relevant and important information before taking an appropriate action, delaying the process.

• Context: While the first paragraph describes the amount of money that was spent on travel, it does nothing to explain the circumstances or context behind the expenditure. By providing the information about the blizzard, the decision-maker better understands why the expenditure was high and in a better position to make an informed decision regarding future travel expenditures.

• Contrast: In the first paragraph, we know what date the task finished, but in the second paragraph, we know that it finished late. By including data from the project plan, the second paragraph is able to compare what was supposed to happen with what actually happened. This provides the decision-maker with a much better sense of the problem and apply an appropriate decision.

• Consequence: In addition to understanding that a variance exists, we also need to be clear about the impact or consequence of the difference. Sure, the first task took a couple more hours than we had originally planned, but in the overall scheme of things, it really doesn’t require action. The budget variance on the other hand is significant since it pretty much blows our travel budget out of the water.

Turning data into meaningful information to drive a decision isn’t rocket science, but it does require some thought. Next time you put together a report, ask yourself: does this tell the full story? If not, it’s probably just data.

In my next post, I’ll write about presentation in the context of decision-making, including some thoughts on dashboards.

Claire Schwartz is a Senior Business Process Consultant with Daptiv (www.daptiv.com) and has over 30 years experience in designing and implementing PPM processes and tools. She has worked with a wide variety of organizations and disciplines including IT, new product development, supply chain management, education, transportation, and financial services. Claire has developed and taught numerous courses in project and project portfolio management and written on a variety of topics including project management maturity, project and portfolio management methodology and implementation approaches, and choosing and implementing project management software packages. She is a graduate of the University of Michigan and holds a Project Management Professional (PMP) certification from the Project Management Institute (PMI) and has earned a Stanford Certified Project Manager (SCPM) credential from the Stanford Center for Professional Development.