In my previous blog post I talked about how the rapid and varied growth of data calls for states to consider an enterprise analytics program, in the form of a Center of Analytics. This entry, first posted as an article on Government Executive's Route Fifty, gives the most important success factors for an enterprise analytics program.

Enterprise can mean a lot of things – full scope across all government agencies for a state or county, one agency with several divisions that need to share data more effectively, or a group of similar business organizations with shared interest in data and outcomes, like criminal justice or health services.

During my time in North Carolina state government, I had the good fortune to work with a dedicated team of resources to establish the Government Data Analytics Center. The GDAC grew from a focus in one business area to an enterprise analytics program supporting a variety of business needs across the state. While the challenges were significant, the GDAC learned to build collaboration and engagement with agencies across NC government through strong leadership, governance and best practices.

In speaking with other government organizations who are implementing enterprise data management and analytics, there are several key components that are helping these efforts achieve success.

Active and Engaged Leadership

Government organizations typically follow a philosophy of data ownership and protectionism rather than one of data stewardship. Creating a culture of data sharing and analytics requires the active and visionary support of business and IT leaders.

Leadership can evangelize the value of having access to quality, consistent and reliable data to help answer government’s most complex business problems, building collaborative relationships across agency boundaries. Through strong leaders, a Center of Analytics can find ways to share data for business needs while addressing the essential need for data privacy and adherence to regulation.

A strong leader will also help seeking funding, set priorities and ensure that business units are actively engaged in the process of creating their analytic solutions.

Strong Governance Approach

Data sharing is generally the first objection raised when discussing the concept of enterprise analytics. Government organizations often recognize the benefits of comprehensive, data-driven analytics, only to respond with “that sounds great, but we cannot share our data.” At times the reasons for not being able to share data are vague references to regulations or law, based on opinion that has become ingrained as fact, or based on the idea that “it has never been done.”

A strong approach to data governance can facilitate better understanding of the rules, regulations and law that impact data sharing. Governance establishes data sharing and usage agreements, defines requirements for security access and user authorization controls and may determine audit and reporting requirements.

In addition, governance ensures that data used in the enterprise program is standardized, clearly defined and understand, quality, consistent and reliable.

Start Small, Show Value

Enterprise programs often get a bad rap – taking so long to develop and produce results that the business needs may have changed before the program is implemented. So when considering the idea of starting an enterprise analytics program, avoid the “big bang” approach.

Help organizations see value before investing tons of time and resources. Start with a smaller business focus, perhaps building from a pilot into a larger, full-scale implementation. Understand the burning issue – is it a legislative issue, federal compliance mandate, governor’s strategic priority, or a key social issue? Opt for areas of analysis that can produce quick wins and demonstrate measureable outcomes - a quick win for the business can mean faster buy-in and adoption and a collaborative partnership for future opportunities.

With each analytic solution, envision how the data and analytic functionality might support the next business problem. Develop with the enterprise in mind and a focus on re-use, repeat and grow.

Build Strong Teams

And perhaps most importantly, focus on the people involved in your enterprise analytics program. Build key skill sets and resources that understand the power of data management and analytics. Engage strong business users who can set key goals and objectives, provide necessary business requirements and help design the analytic solution. Develop strong analysis and technical resources who ensure that the solution provides quality data and targeted analysis to support business needs. Where skill sets are limited, seek external organizations who can partner with government to provide the necessary capabilities.

Successful enterprise analytics demands strong leadership and governance, a talented workforce and realistic near and long-term goals. The process breaks down established data barriers and fosters a data culture, opening up new frontiers of evidence-based services and governing.

Do you have other ideas for getting a project like this off the ground? Please share them, and stay tuned for my next post in this series, which will give tips on how to support implementation and adoption of analytics.