DHS’ Technological Transformation Must Be Data-Driven

Use Synthetic Data Modeling to Test Technology

Once organizations have a baseline and the data to understand the problem, synthetic data modeling can uncover solutions. For the government, testing new technologies or data solutions in the field is helpful but high risk. Testing with synthetic data modeling prior to piloting in the field saves time and resources.

"Homeland security components like TSA must respond to many threats. Often, they seek emerging technologies to manage those threats, but those technologies must be adequately tested before implementation," says Brant Horio, a data and computational sciences fellow at LMI. "With synthetic data, you model how well technology responds to changes in threats and understand the resources required to respond ahead of time."

Synthetic data modeling also assists with adversarial insights—learnings that hackers might already be trying to develop about technology systems. "If you have artificial intelligence trained to seek certain things, you understand what is important to the system. Then, you can start thinking of ways to beat that system, which adversaries are already doing," he says. "Now, you've got a sense for potential exploits before they happen and create security problems. That's very valuable data."

Build on Incremental Success

Synthetic data modeling supports project teams as they create pilots or incremental technology prototypes for the technological transformation. Keith Rodgers, vice president of advanced analytics & AI at LMI, says these data help organizations understand a project’s potential and can ultimately lead to the long-term internal support necessary for success.

"There is a tendency with organizations that, if a pilot or small project fails, the broader project gets abandoned. That's not ultimately helpful to achieving big goals like technology transformation," Rodgers says. When organizations discover potential through insights from data analytics, then project teams have wider latitude to build pilots and prototypes (which may need refinements over time) without a setback that tanks the whole project.

According to Rodgers, building an integrated project team that includes people from across an organization makes technology transformation more likely to succeed. If this team creates smaller project prototypes that can fail, the lessons learned from those small projects lead to a solution that works better. "Building on incremental successes and failures is how big projects move forward, and you can do that in a controlled way so you aren't negatively impacting operations in the field," he says.

Paul Dufresne, a senior analyst at LMI, agrees. "Often, the technology tool works, but not in the way an organization needs. If you have representatives on a project team from all parts of an organization who can stand up and say, 'This adds too much time to our process' or 'We can't use this because it doesn't solve for x,' then you can make changes before your frontline people have to implement it," he explains.

Manuel Galvan, a program manager at LMI, adds that organizations using an integrated project team construct help ensure that resources for leveraging existing enterprise solutions and a project’s effort don’t get wasted. “Large organizations tend to silo,” he explains. “Sometimes one group purchases a technology that was previously enabled by another group but isn’t being leveraged as an information technology (IT) service or capability by others. When a project team works across silos, they uncover where enterprise or services already exist and can exploit those capabilities to create efficiencies. These efforts multiply the return on IT investments throughout entire organizations.”