Tips for Deploying Analytics at the Federal Level

Even though the federal government is one of the biggest—if not the biggest—producers of data, the public sector seems to be falling behind implementing big data programs, according to a 2016 report published by Delft University of Technology academics.

While federal agencies have done a good job of collecting data, they’ve had less success in actually deriving insights from that data via analytics systems.

It almost seems like big data analytics is The Promised Land that’s forever over the next hill for federal agencies. However, rather than focus on the lack of an analytics capability at the federal level, or the reasons why federal agencies have fallen behind their private-sector counterparts, I’d like to offer some prescriptions for what can be done.

While growing up, I was taught the old adage that “failure to plan is planning to fail.” That’s as true with regard to analytics as anything. Before even considering the idea of implementing an analytics program, agency heads need to ensure they have buy-in from key stakeholders. Simply put, without adequate buy-in, nothing else can be accomplished. In order for real progress to be made, agency leaders and IT professionals need to come together to define realistic, measurable, objectives for an analytics system and what it will cost.

Such a conversation should include a discussion of both direct costs, such as the purchase price of software and the time it will take your engineers to install and maintain it, as well as indirect costs, such as the training public-facing employees will require in order to correctly use the system. While this is a time-consuming process that will undoubtedly delay implementation, failure to do so is a virtual guarantee of further problems and disappointments later down the road.

Once objectives and costs are defined, policies must be implemented to ensure relevant data is collected and stored in such a way that is useful for analysis. This step is where federal agencies (and private businesses) sometimes stumble on the block of so-called “data siloing,” where different pieces of a technology stack don’t talk to one another. The solution is good data governance.

My colleague Mark Hensley haswritten about the importance of data governance.