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The third edition of “From Data to Decisions” presents five practical applications of government analytic programs that continue to provide vital ROI to the American taxpayer. By studying these programs, origins, and executions, we can learn how to repeat and improve upon their success. Promoting systems that encourage data driven decision making will provide long term success for the American people.

There is a lot excitement around the “Big Data” revolution. Anticipating 300%-400% return on investment (ROI), companies are trying to gain new insights and advantages through the untapped power of Big Data Analytics. However, like most new emerging technologies, Big Data Analytics does not:

  • Happen overnight
  • Cure all
  • Come with an “autopilot” button

From Data to Decisions III, published by the Partnership for Public Service, in partnership with IBM, explores the topic of Big Data in today’s government landscape by profiling five early analytic programs and identifying their key characteristics. These programs serve as fascinating, need-based programs that continue to provide valuable insights. The programs include:

Social Welfare: The oldest case is the Famine Early Warning Systems Network (FEWS NET) developed by U.S. Agency for International Development in 1986. The $25 million dollar program helps optimize the distribution of up to $1.5 billion dollars per year in USAID Food for Peace assistance.

Public Health: The Center for Disease Control and Prevention (CDC)’s PulseNet consists of 87 public health laboratories across the nation and consolidates foodborne illness cases into a single source. PulseNet serves as an early detection system to help prevent epidemics.

Biometric Intelligence: Since 2003, US armed forces have collected biometric information from non-US citizens in Iraq and Afghanistan. It identifies enemy combatants and permits access into controlled areas. The system, known as The Automated Biometric Identification System (ABIS), stores 4.4 million unique identities and has identified over 3,000 enemy combatants, added 190,000 identities to the watch list, and protected the welfare of the United States and its allies.

Intelligent Inspections  In 2007, the USDA initiated a program to make risk-based decisions to determine which shipping containers should be examined for plant-borne pests. The Agricultural Quarantine Activity System (AQAS) enables the Plant Health Inspection Service to better utilize their resources to tackle a $136 billion dollar problem.

Smarter Healthcare: Predicting the likelihood of hospitalization or death within 90 days, the Patient Care Assessment System (PCAS) calculates the Care Assessment Needs (CAN) Score. This score allows the Veterans Health Administration (VHA) to focus care teams and proactively care for their patients. The system collects 120 unique elements for 5.25 million patients and is supported by an 80-terabyte corporate data warehouse.

These cases provide valuable lessons that should be carried forward:

  1. Collaboration: US government agencies must be willing to collaborate with each other in order to collect and share data and analytics expertise. FEWS NET was a collaborative effort between USAID, USDA, USGS, NASA, and NOAA. The agencies held participating agency program agreements (PAPA) and participating agency service agreements (PASA) for over 15 years in order to compensate each other for information and staff. Inter agency work can be challenging, but navigating through the red tape can be highly rewarding.
  2. Return on Investment: Analytic programs must demonstrate their value and return on investment. They can do so by developing data to create meaningful metrics that reflect the health and progress of the program. Since its creation, PulseNet has successfully identified 32 famine outbreaks. This, in turn, can be quantified as average annual savings of $291 million dollars per year. The operating cost of $10 million pales in comparison to the overwhelming benefit of the program.
  3. Validation: Leaders must be validated with analyses and proof that analytics are improving mission results. In today’s politically charged Washington environment, delivering objective data can accelerate the debate and focus on choice rooted in the data. The DOD biometrics program has not communicated its value as well as FEWS NET. Grassroots programs, such as the VHA’s VISTA system, were able to survive criticism by demonstrative their effectiveness in achieving mission results.
  4. Grassroots Innovation: Successful programs encourage employees to use data to spark insights. Innovation through grassroots analytics is incredibly powerful if management is willing to encourage rather than control. The AQAS system started as Department of Agriculture’s Animal Planet Health Inspection Service (APHIS) analysts built their business intelligence capabilities. It slowly grew and eventually became a major asset to the organization.
  5. Training: Leaders need to demand and use data to guide organizations and ensure their staff receives sufficient training. At both the VA and CDC employees are required to be adept with the skills and tools needed to provide insightful analyses.

Much can be learned from these case studies. ROI on Big Data investments is more important than ever in our value-driven government landscape. Therefore, programs need to highlight program metrics focused on demonstrating achieved ROI and outcomes. Big Data initiatives must start small and focused on mission critical questions or known-unknowns. Potential gains from Big Data results are everywhere: from customized advertising to genome specific personalized medicine.

Originally published on IBM:

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