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The Value of Federal Government Data in Transforming Government Agencies

The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.

By Bill Brantley
December 12, 2017

The U.S. federal government is probably one of the biggest (if not the biggest) producers of data. Every day, thousands of federal workers collect, create, analyze and distribute massive amounts of data from weather forecasts to economic indicators to health statistics. Federal government data is a major driver of the American economy as businesses use the data to make decisions or blend the government data into products and services sold to consumers.

Just how valuable is federal government data? How is the value of information measured? A recent book attempts to develop a framework to monetize, manage and measure data as an organizational asset. Infonomics is the framework for helping organizations best value and manage their data assets. Although infonomics is intended for private sector organizations, I can see many applications for federal agencies.

Measuring the Value of Data

Probably the most valuable infonomics concepts are the Information Performance Gap (IPG) and the Information Vision Gap (IVP). The IPG is the “difference between the realized value of the information asset and its probable value.” Closely related is the IVP which is the “difference between the probable and potential information valuations.” These concepts are valuable to federal agencies because they give a new perspective on federal data. As agencies work with their data, are the agencies fully realizing the value of government information? With the emerging techniques of infonomics, agencies can better measure the value of their information.

Using Data to Transform Government Agencies

Accurately measuring the value of federal data will aid agencies in digitally transforming. This is one process for transforming government agencies that heavily relies on data assets:

  1. Locating and preparing the data assets – Locating and preparing data assets is the hard work of creating a data-driven organization. Consider the vast number of data sources in the average Federal agency — Where is the data is located, how is it stored, what types of technology are needed to access and manipulate the data, and how to extract the data? Agency data sources often grow organically, which means there is a multitude of technologies that silo the data sources from each other. As I have found in gaining my data science certification, much of the data scientist’s work is locating and cleaning the data to prepare it for analysis. Locating and preparing data assets can be the costliest and time-intensive task in creating the data-driven organization.
  2. Establishing data partnerships – It is the rare organization where all the data resides in one office or department. Often, data sources are spread throughout the organization and subject to different departmental jurisdictions. Delicate negotiations must create data sharing partnerships and an enterprise-wide information governance. Data partnerships may also require going outside the organization to establish access to vital data sources. Creating and managing data partnerships will also take much time and can easily be derailed by even one or two dissenters.
  3. Leadership views data as a strategic asset – Once the hard work of steps one and two are accomplished, being a data-driven organization requires ongoing senior leadership support. Senior leaders must champion the use of analytics to inform agency decisions and support the results of data analysis even if the analysis runs counter to the leadership’s initial assumptions. Senior leaders also must support the governance of data assets and maintaining data partnerships.
  4. Using data for organizational innovation – However, using data assets to create organizational innovations for the agencies is a promising area. Using analytics can help agencies redesign offices to take better advantage of existing agency talent to meet new strategic mission requirements. Analytics can also help agencies to develop new citizen services to meet public demand more effectively.

Using IPG and IVP to measure federal agency information will help senior leadership best extract the full value of the information for organizational innovation. Infonomic measurement techniques will give a monetary value to federal information which will aid senior leaders in making return-on-investment and budgetary decisions about the data.

Mapping Data Ecosystems

Another useful application of infonomics is using it in mapping data ecosystems. In a column for General Services Administration’ DigitalGov, I described a project by the Congressional Research Service to map the big data ecosystem of agriculture. The purpose of the mapping project was to give a “high-level overview of how big data flows in the agriculture ecosystem.” Using the infonomic measures, we can also more accurately track how value is created and distributed in big data ecosystems.

Federal government data plays a large role in the American economy, but this role has been obscured because it is difficult to measure the value of information. With the emergence of infonomics, we have methods for better measuring and managing the value of information. Once citizens can see the monetary impact of Federal government data, citizens and businesses will help in closing the information performance gap to realize the full value of Federal government data.

Author: Bill Brantley teaches at the University of Maryland (College Park) and the University of Louisville. He also works as a Federal employee for the U.S. Patent and Trademark Office. All opinions are his own and do not reflect the opinions of his employers. You can reach him at http://billbrantley.com.

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