As the Federal government agencies begin the digital transformation journey, becoming a data-driven organization is even more vital. What does it mean to become a data-driven organization? According to one definition, “[a] data-driven company is an organization where every person who can use data to make better decisions, has access to the data they need when they need it.” There are many theories are on how to create a data-driven organization, but few case studies that demonstrate the actual process. In this article, I will describe the results of four recent case studies that showed how a bank, a health care organization, a Fortune 500 company, and a municipal government became data driven.
First Step – 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 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.
Second Step – 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.
Third Step – 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.
Fourth Step – Using Data for Organizational Innovation
The case studies demonstrated how the data-driven organization could commercialize its data assets. Commercialization of data assets does not apply Federal agencies. 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.
Many models exist on how an organization can become data-driven. The four steps above are common to those models and demonstrate how the Federal government agencies can best use their data assets to transform the organization to serve the American public better.
Each week, The Data Briefing showcases the latest federal data news and trends. Visit this blog every week to learn how data is transforming government and improving government services for the American people. If you have ideas for a topic or have questions about government data, please contact me via email. Dr. William Brantley is the Training Administrator for the U.S. Patent and Trademark Office’s Global Intellectual Property Academy. You can find out more about his personal work in open data, analytics, and related topics at BillBrantley.com. All opinions are his own and do not reflect the opinions of the USPTO or GSA.Edit