Data is everywhere, especially in the public sector. Government collects and uses data to influence financial decision-making, shape policy initiatives and influence the way government achieves goals. Agencies even generate their own data at a pretty remarkable rate.
This so-called big data creates a blessing and a curse. Leveraged effectively, big data offers the blessing of new insights to improve government functions. But given its massive volume and often informal structure, it also brings the curse of limited resources to store and maintain data – much less understand it.
We each have sprawling data sets at our disposal, but often the problem that presents itself is how to properly analyze and leverage data at scale to make an impact. This is not for a lack of effort. The President’s Management Agenda even highlights data and accountability as one of its three main drivers and outlines a federal data strategy. So why aren’t we using data to its fullest potential?
As the Government Accountability Office (GAO) found in Sept. 2018, agencies’ self-reported utilization of performance data remained stagnant since their last survey in 2013. When asked by GAO to rate their use of performance data on a five-point scale, the average was 3.39, down 0.02 in five years and 0.07 in the past decade.
Data analytics is essential to government’s missions, but unfortunately becoming fully data-driven doesn’t happen overnight. GovLoop wants to help.
Starting this month, we’re launching a brand new community of practice for analysts in government. We’re eager to share advice, resources, and best practices for putting data to better use in our roles.
We’re even more excited to hear from YOU! Our goal is to use insights and personal experiences, plus ask questions of one another and network, all for the common goal of improving how we use data in government.
No matter how you use data in your job, we all have room for improvement. With more and more data being created every minute, what are you waiting for? Join here and let’s work together to improve how we use data analytics.
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