Establishing a roadmap for government data analytics

It takes less time than you think

By: Erin Stevens, Systems Integration Manager, SAS

Lately, there has been a great deal of emphasis placed on the government’s need to harness the power of “big data” and “analytics.” More data – and better analysis of that data – can assist with everything from early threat detection to entitlement and tax fraud identification.

The 2010 Government Performance and Results Modernization Act (GPRA) turned advancing government data analytics from a desire to a mandate. The recent rise Chief Data Officer (CDO) appointments further highlights that data is now viewed as a strategic asset in government. Not only can analytics make the nation more secure and streamline government agencies, it could also save taxpayers billions of dollars.

Technologies such as grid, cloud, in-memory processing and data visualization help organizations get more from their data.

For the uninitiated, the thought of adopting analytics for government data can seem like a daunting undertaking – especially within the federal IT sphere, where actions that are not expressly permitted are often forbidden. It isn’t easy to identify the appropriate analytics-based solution among all the variables, tools and technologies (that’s before you even consider an agency’s specific needs and compliance mandates).

Even when an agency is successful in solution identification, it has to overcome institutional inertia. This deeply ingrained attachment to “the way things have always been done” can be strong, even when traditional approaches no longer support the mission.

Establishing a strategy for government data analytics

Before an agency can harness the power of big data, it must first look inward to evaluate its current IT environment and craft a vision. Consulting stakeholders throughout the organization can decrease the chances of ignoring important considerations in that vision. In addition, getting other stakeholders involved will increase the odds of garnering buy-in. This input and buy-in can occur through structured discovery.

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Structured discovery brings together IT, business analysts and other related stakeholders to discuss current operations (strengths and challenges) and craft a unified vision. By tapping previously unengaged stakeholders, the agency gets a better understanding of its broader needs and uncovers clues about how analytics can further strategic goals.

To help align agency goals with market trends, it is often useful to engage third parties to assist with the internal review and future state planning. Third parties can help agencies discuss options for:

  • Self-service analytics: Move data exploration and visualization into the hands of end users via easy-to-use, drag-n-drop interfaces.
  • Faster processing: Reduce data movement and network congestion through in-database processing.
  • Centralized data storage and governance: Combine disparate data sources to produce a single version of the truth (and take advantage of lower cost storage mechanisms, like Hadoop).
  • Simplified maintenance: Move from PC-based software to enterprise platforms for centralized maintenance, upgrades and contracting with the potential for rolling upgrades and high availability.

These advancements focus on getting more insights out of an agency’s data faster while allowing IT to prioritize more high-level, strategic decisions.

Structured discovery allows agencies to consider their existing tools and processes and where they might benefit from infrastructure and software advancements. The goal is a predictive “analytics factory” that formalizes ongoing processes for analytic data preparation, model building, model management and deployment. The agency can then quickly identify questions, attain solutions and streamline improvements.

Through structure discovery the agency can craft an “analytics roadmap,” with assets such as:

  • Current state infrastructure, process and output documentation.
  • A common vision.
  • A change management and training plan to empower IT and end users in the future state environment.

There is no one-size-fits all for analytics solutions. Structure discovery, considering IT and the business users, can increase the odds of widespread buy-in – and for success upon implementation. “Big data” and “analytics” can be more than buzz words, they can allow our agencies to reduce costs while improving efficacy for a safer, healthier, more secure nation.


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