Unlocking the value of data for improved performance

Data-informed decision-making is a fulcrum for federal government transformation. Our team breaks down the foundations for data-driven decision-making.

Federal agencies are increasingly aware of the value of data and the importance of leveraging data to inform and improve programs, operations, and services. By tapping into data for rich insights, organizations are empowered with the information necessary to achieve their mission goals and objectives.

But beyond this value driver, data-informed decision-making is a fulcrum for federal government transformation. The President’s Management Agenda directs agencies to treat data as a strategic asset, essential to three high priority areas:

  • Improving the citizen experience
  • Sharing quality services
  • Shifting from low-value work to high-value work

But how does that actually happen? Let’s look at the foundations for data-driven decision-making.

From data collection to data insight

Achieving and demonstrating success for these priorities requires moving from data collection to data insight by:

  • Establishing baseline performance standards
  • Determining what drives outcomes
  • Evaluating impacts of course correction efforts (re: programs, services, policy shifts) and actions correlating with the principles of modern business analytics, below.

Often this is described as a journey from descriptive and diagnostic analytics to predictive and prescriptive analytics. Gartner’s IT Glossary defines these types of analytics as follows:

Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?” (or, “What is happening?”), characterized by traditional business intelligence (BI) and visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives.

Diagnostic Analytics is a form of advanced analytics which examines data or content to answer the question “Why did it happen?”, and is characterized by techniques such as drill-down, data discovery, data mining, and correlations.

Predictive analytics describes any approach to data mining with four attributes:

  • Emphasis on prediction (rather than description, classification or clustering)
  • Rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining)
  • Emphasis on the business relevance of the resulting insights (no ivory tower analyses)
  • Emphasis on ease of use, thus making the tools accessible to business users

Prescriptive Analytics is a form of advanced analytics which examines data or content to answer the question “What should be done?” or “What can we do to make _______ happen?”, and is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning.

As a whole, analytics should help everyone in the organization make sense of data and uncover meaningful trends and unexpected relationships, providing new perspectives on the programs or operations the data represents. A modern data analytics platform can also provide near real-time analyses—invaluable for organizations dealing with quickly evolving data, such as children at risk of starvation, rapidly shifting weather conditions, or sudden disease outbreaks.

Setting the stage for effective data analytics

Remove obstacles to data sharing by implementing agreements and standard processes for sharing and adopting enterprise platforms to bring data together.

Establish data governance to improve quality and resolve inconsistency in what data is gathered and how it is formatted and identified through metadata.

Plan for data integration from legacy project, departmental and enterprise systems (project management, grants management, HR, CRM, etc.).

Ensure regulatory compliance for issues such as privacy, security and Section 508 accessibility for data visualizations.

Data-driven decision making in the real world

Agencies bring varied approaches to implementing analytics. For example, the General Services Administration makes new tools and technologies accessible to groups and projects throughout the agency, in a systematic and governed way. These analytics teams grow, and their approaches mature to create mini-centers of excellence. The organization can evaluate how a particular group is providing the most insightful analytics, using technology to see who is using data and how. GSA also collects enterprise data on a common platform, integrating inputs from centers of excellence to make them accessible to decision-makers and operational practitioners. One example is a dashboard for GSA construction projects, enabling leaders and end users to view status on local, regional and national levels with self-service tools.

The Veterans Administration (VA) has long centralized data and established business reports that leaders can access daily through a variety of dashboards. Health centers have the flexibility to assess their operations against other centers based on quality reports and can seek out best practices from peers. As the Department of Defense and the VA standardize on how they capture patient data on soldiers, the VA is planning for the next anticipated data tidal wave: patient-generated data. Data standardization is critical because soldiers become veterans and health records should seamlessly move from the DoD to the VA. The VA hopes to move analytics closer to patients through smart phone and tablet apps, helping providers make data-informed treatment decisions.

USAID has focused on “data as a team sport.” The agency seeks to ensure that leaders and practitioners alike have data availability – knowing where data is, with assurance that it is the data needed for a specific question or use. Access to analytical tools is another imperative, as is fostering data literacy for decision makers and staff who draw on the data to make recommendations to leadership.

The organization also strives to provide complete data for an accurate view across the operating environment and is working to standardize data models for operations around the world. The result is a series of dashboards that provide the agency and public with a real-time view of USAID programs by sector and geography, and a historical view of international development going back to the Marshall Plan era.

Explore more

Visit our Government Solutions page to learn more about how a modern data analytics platform can help federal agencies achieve mission-critical outcomes.

Watch video interviews from FedScoop’s New Business of Government series to learn more about the agency initiatives described above.

Read an earlier post, The essential shift: federal agencies move to modern analytics, part of our blog series that looks at how federal agencies (and other government organizations) can harness the power of modern analytics.