Driving Government With Data

Governments were among the first users of computing technology in the mid-20th century. By the 1960s, governments had full IT departments with centralized data centers that worked with large data entry organizations. From this early start to today, governments have maintained an intimate relationship with data.

However, more can be done to successfully collect and use data to achieve agency missions. A 2013 IBM study showed that only 50 percent of managers made even half their decisions using data and analytics, revealing there is much room for improvement.

An IBM Analytics white paper, “Data-driven government: Challenges and a path forward,” highlights the importance of using data to drive progress in government. By explaining the challenges that governments face in using data, the paper also offers potential solutions and guidance for moving forward with data-driven decision-making.

Analytics can evolve to meet agency needs and provide them with more efficient ways of measuring and performing mission-critical functions. For example, analytics can help agencies measure the effective and efficiency of service delivery channels; evaluate where maximum benefit could be obtained from the next investment; and detect internal fraud and errors in procurement and expense reporting. Data can help agencies improve services based on user insights, reduce costs, and improve public perception.

However, in order to achieve these benefits, agencies need to address several challenges.

  • The privacy responsibility: Some consider privacy to be the “proverbial elephant in the room;” however, in general, agencies are concerned with maintaining individual privacy. Agencies should consider the type of data they use as well as changing citizen privacy expectations.
  • Sources of data: The amount of data produced both inside and outside of government is growing at a rapid pace, and agencies must figure out ways to determine what is useful and collect the relevant information in an efficient, cost-effective way.
  • Readiness for use: Even when agencies understand what data they need and have access to it, they still may have trouble incorporating it into their analysis. Considering the variety of sources agencies can pull from, standardizing data and training analytic tools are key steps in the process. 
  • Managing data: Many government agencies have stories about having to manage multiple warehouses full of data. In addition, much of the storage is occupied with redundant data, as many agency divisions aim to claim access to their own information. Working on interagency and intra-agency collaboration can eliminate redundant data.
  • Using the data: In order to gain actual benefit from data, agencies need to extract actionable information from it. The analytic tools must be up-to-date and able to perform the tasks needed, such as integrating data from several sources, producing visualization, and identifying relationships. Agencies must decide whether they will rely on in-house tools or commercial solutions.

While significant, these challenges are not insurmountable. An important first step in this strategy building is full consideration of the role data and analytics should play in the agency’s future. This includes asking questions such as who owns the data strategy currently, what is your data management process like, and do you need a chief data officer. These are high-level discussions that are foundational to developing and implementing a data strategy. From there, agencies should decide on an ideal end state and then establish the analytics infrastructure needed to achieve the desired goal.

For more information on the benefits of data-driven decision making, challenges agencies face, and ways to move forward, read the full IBM paper here.

Content provided by IBM and GovLoop: 


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