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Friday, February 23, 2024

PERSPECTIVE: Throwing DARTS: The Key to Advancing Data Analytics

Every day, the federal government works to improve the lives of the American people. There are always many initiatives underway to make life better for citizens—from green energy development to programs that provide resources to vulnerable communities. The success of these objectives can often rely on access to high-quality, actionable data.

However, current processes that guide the collection, storage, and analysis of data at the federal level can sometimes fall short. Government data does not always deliver the kinds of insight that agencies need to make their plans successful.

DARTS: A guide to transforming data management

As the volume of data increases through digitalization of processes, US government agencies have a tremendous opportunity to improve the value and utility of government data. The DARTS method can serve as a step-by-step guide to pave the way toward comprehensive data management improvements:

  • Digitize—The first step for government agencies is digitizing and unifying. Many U.S. agencies still operate on hard-copy systems, which means that they are losing out on valuable insight. Just as detrimental are siloed records which, though in a digital format, sometimes exist only on a single worker’s computer or are otherwise inaccessible to the organization at large. 

Migrating those hard-copy or personal records into a shared digital resource is a daunting task, to be sure, as there are hundreds of years of data on thousands of topics within the federal government’s ecosystems. However, it is a critical first step toward advancing the country’s analytics capabilities. 

  • Automate—Once existing records are made digital, agencies should find ways to automate the processes for maintaining and updating those datasets. Many government agencies already have access to the IT infrastructure needed to deploy some of these types of automation systems. These tools can not only help capture data generated by government operations and research but can also be configured to gather public information such as news articles or emerging research findings to complement government operational data.
  • Reconcile & Integrate—In some cases, incoming data does not agree or is not compatible with other sources of truth. This creates the need to reconcile discrepancies. Acknowledging and resolving potentially duplicative and conflicting facts across one or multiple systems of record is a key component of data management. Data management involves installing systems, processes, roles, and responsibilities to help better ensure that data stays valid. This process can also involve identifying ways to translate data from diverse sources into compatible structures so that the unique information from each source can contribute to an integrated, whole understanding of the context and domain of government decision making. 
  • Transform—A consolidated, validated dataset can be a goldmine for an analyst, but usually needs to be transformed before it is broadly usable. In some cases, transformation may include restructuring of the data to be usable in spreadsheet software. In other cases, the data may need to be visualized using charts, graphs, and maps before key insights become clear. Regardless of the method, using enterprise business intelligence software can help make this data transformation process easier. A multitude of these software applications provide users with tools for streamlining data transformation steps and for rapidly constructing interactive data visualizations. While these capabilities are groundbreaking compared to the data analysis methods that were available a decade ago, they rely heavily on the foundational steps that are described above. Government agencies who proactively manage their data should need relatively little expert support to transform their data into actionable insight.
  • Share—The final component of the DARTS process is to make data accessible. Numerous research agencies do this well, while primarily regulatory agencies may face greater challenges and may not be aware of the value of some of their data to both public and private stakeholders. Although some data may be sensitive and require anonymization prior to sharing, data analytics is about fitting together pieces of a puzzle. Empowering government decision makers with access to compiled, validated, and anonymized (if necessary) data can reduce the number of missing pieces. 

Getting it right

Comprehensive data improvement at the federal level is a monolithic challenge. However, that does not mean it should be avoided. Application of the DARTS method can improve outcomes, even if it is implemented within a single office or department, providing better insight into communities’ needs and allowing for the design of more efficient, cost-effective plans.

Each transformation will look different from department to department. Though the steps outlined above are prescriptive, the overarching plan should be tailored to each agency, department, and team. Flexibility will be critical to success.

The good news for government sector leaders is that organizations worldwide, including those within the government, have already pursued these types of data integration, and succeeded. Though most private companies’ scopes pale in comparison to that of the federal government, their successes bode well for agencies hoping to do the same. With the right approach, America’s federal agencies should be able to enhance their data analytics programs, allowing them to utilize data-driven insight rather than relying solely upon hopeful intuition to guide our future.

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