These hunters are often disguised under several different job titles including data acquisition specialists and data scouts.
Faced with exponential data growth, stakeholders in both the public and private sectors struggle to effectively sort, analyze and leverage data to improve systems and services. In this new ecosystem of big data, many IT pros are turning to advanced analytics solutions, including artificial intelligence and machine learning, to help offload some of this data analysis burden and fundamentally change the way we interpret and use data.
For the federal government, though, in order for these AI capabilities to be effectively implemented, agencies will require additional resources to locate the data most relevant to improving services and advancing missions.
This is where data hunters can play a key role.
What is a data hunter?
Data hunters look for external sources of data that can be combined with internal information to generate new insights and optimize processes for the federal government. Often disguised under several different job titles including data acquisition specialists and data scouts, data hunters are those who have a firm grasp of agencies’ needs or challenges and then move to analyze that data accordingly.
Hunters use their knowledge of both the agency and the external environment to identify which new—often external—data sources could be helpful in addressing a particular issue. They then apply their practical skills to get the data and/or develop the new data streams and make them available to the organization, making them an upstream partner of data scientists and analysts.
Data hunters also play a critical role in data governance and management. Because of their role making data discoverable, data hunters can provide the feedback required to make sure that data is maintained securely, which is a primary concern for most government entities. They also need to be able to explain these issues to others to ensure that data quality is maintained. Finally, they need the skills to manipulate and manage data themselves to the required standards.
Data hunters are already an important part of many data enterprises in the private sector. A recent webinar from Forrester explored experience with data hunters among IT professionals and found that nearly one-third of those on the webinar said that their organization already had at least one data hunter role. Almost half (44%) said that they had a formal process for sourcing external data. Though this sample is not likely to be representative of the entire IT industry, it certainly demonstrates early adoption of the new job role in the private sector and is likely indicative that the trend will soon move to the federal government, if it has not already.
The Path Forward for Advanced Computing in Federal Government
Following the executive order on artificial intelligence and the launch of AI.gov, the federal government is in a position to leverage cutting-edge technologies to keep pace with citizens' needs. To effectively incorporate AI and ML technologies, however, agencies will require data hunters to look beyond their traditional boundaries for data insights.
I have personally seen the impact that data hunting can have on a government’s mission. In my role as director of the National Biosurveillance Integration Center in the Department of Homeland Security from 2012 to 2015, we were charged with providing early warning and situational awareness for health threats to the nation. As part of that effort, we relied heavily on the Centers for Disease Control, the Food and Drug Administration and other government health and disease surveillance data, but we also leveraged external open data to supplement and enrich our government datasets. For example, we built a system that scraped information from millions of health-related websites around the world every few minutes looking for evidence of new outbreaks. We analyzed passenger flight routes to understand how potentially sick travelers could enter the United States. We even incorporated weather data to help us understand where we might expect increases in mosquito-borne illnesses.
More recently, the FDA’s Center for Food Safety and Nutrition has taken a similar approach to keeping the nation’s food supply safe. In addition to their internal data at FDA, they’re pulling in external web data, as well as data from scientific journals, and applying artificial intelligence and machine learning to that information to more proactively understand risks to the U.S. food supply.
Data hunting can also help solve challenges for federal benefits programs such as eligibility verification. The government can take a “recipient-centric” approach that incorporates contextual information from outside sources, in accordance with privacy regulations, to understand when changes in a recipient’s life may have altered their needs. Or in social services for example, child welfare caseworkers assigned to families with children at risk of abuse or neglect can make better-informed decisions when data external to the agency—like school records, birth records and law enforcement/emergency services—can be brought to bear to augment their internal data.
The ability to combine internal and external data to glean deeper insights has myriad applications in the public sector and will shape the way that the federal government's services are delivered moving forward.
Steve Bennett is currently the director of the Global Government Practice at SAS, and he formerly worked in various roles at the Department of Homeland Security.