Let’s say you’ve decided to treat yourself to lunch.
Sure, you can sit at your desk and make a mental resolution to visit that burrito spot around the corner. But unless you actually go out the door with your wallet in hand and purchase that delicious burrito, your decision is deficient.
Decisions trigger action. But there are many professing data-driven decision-making approaches that inform only what already happened. It’s useful information, but not always for directly motivating action, said Andrew Churchill, Vice President of Federal at Qlik, a data platform provider.
Agencies must transform passive decisions into action.
How One Agency Is Motivating Action
Take this large federal agency that Qlik worked with, for example. It had built too many dashboards. Executive leaders, in particular, were overwhelmed with information, and the data overload got in the way of decision-making.
To wade through the noise and get actionable insights, the agency turned to active intelligence. Active intelligence notifies you when your attention is needed, guides you toward analysis that supports a decision and then facilitates the action you will take as a result. In short, it offers continuous intelligence from real-time data to trigger action.
There are a couple things you need to make active intelligence succeed at your agency.
First, an iterative culture.
The U.S. Army Medical Command needed an analytics capability for the field. But it knew it had bad data coming from its source systems that would not be suitable for use on the first try.
So the team built a color-coded banner and engaged user input. A red banner meant medical professionals should not use the dashboard to make decisions, but they should certainly provide feedback. With that input, the team iterated and improved to reach the yellow banner status, which meant many errors were resolved, but users should take caution in using it for life-and-death decisions. Finally, the green banner meant the dashboard was considered the gold standard.
To create an iterative culture, leaders should articulate that solutions don’t have to be perfect to be useful.
“If you let great get in the way of good, good is going to take a long time to get to,” Churchill said.
Second, a scalable future.
The Defense Department’s (DoD) Advana program started as a simple system for fourth estate audits. The fourth estate is defense organizations that are non-military and non-intelligence.
Today, Advana has expanded to become the enterprise big data platform for the entire DoD, pulling in data from more than 450 systems.
This didn’t happen overnight, especially with legitimate concerns around security and governance. But over time, leaders saw the value of opening data access that outweighed the risk. This shift is becoming more normalized, and the platforms agencies choose should be able to scale as well.
“You can start small,” Churchill said. “But start with the end in mind.”
This article is an excerpt from GovLoop’s guide “Your Field Notes for Data-Driven Decision-Making in Government: Case Studies on Work Culture, Equity and More.”
