Human trafficking has been reported in all 50 states. To use New York as an example, the state had 414 cases alone in 2020, according to the National Human Trafficking Hotline. New York also has the fourth-highest number of reported cases of sexual exploitation and labor trafficking.
Human trafficking has been able to persist for complex and interconnected reasons. But state, city and local agencies can take a pragmatic approach to reducing the incidence of this social ill. How? By implementing a purpose-built data-sharing platform to identify, correlate and act on relevant data.
In many states, big data already plays an important role in improving residents’ quality of life, from informing states’ pandemic response strategy, to building flooding resilience in rural areas, to driving housing and planning decisions in inner-city neighborhoods. Likewise, cross-agency data sharing and analysis can mitigate trafficking by helping agencies uncover and prosecute the crime.
But effective counter-trafficking has been hampered by a lack of reliable information. Two factors contribute to this data deficit:
1. Lack of physical evidence. Crimes such as narcotics trafficking involve physical evidence such as drugs or vehicles. But human trafficking largely involves people. This “evidence” has a right to travel freely through the state and might actively avoid interaction with the justice system.
2. Lack of mandates. In many states, health-care and social workers are required by law to report suspected abuse such as child, elder and intimate-partner abuse. But there’s no federal requirement, and there are narrow state requirements, to report suspected cases of trafficking.
The result? Social services and law enforcement agencies often lack accurate, up-to-date information about who’s involved in trafficking, where it’s taking place and the co-existing social issues that might predict its occurrence.
The first step in addressing this data deficit is to gather and correlate information that might point to trafficking risk. Such data might include issues as disparate as refugee status, homelessness, illegal drug use and gang membership.
Correlating the right data is important because data relevant in one part of your state might not be relevant in another. For example, in urban areas, a combination of teen pregnancy, active drug use and having significantly older father can signal possible sex trafficking. In agriculture centers, a high concentration of undocumented men could indicate a risk of labor trafficking.
The regional relevance of data indicates the need for effective data sharing. By propagating data across agencies and relevant nongovernment organizations, governments can gain insights into where trafficking might be occurring.
A proven way to connect information across state agencies is an effective legal framework and technology platform for data governance and sharing. Virginia, for instance, is using this approach to better manage opioid abuse. Virginia combines previously siloed information from agencies, secretariats, localities, social services, public safety, corrections and drug courts to understand and reduce the occurrence of drug addiction.
Governments can apply the same data governance concepts and technology to combat trafficking. Data sharing can bring together information from a variety of relevant organizations. Authorized state employees in law enforcement, social services and education can use that information to create data visualizations, such as charts, graphs and heat maps, to track trends and recognize risk factors. Ultimately, a viable data-sharing platform can equip the state to identify, prosecute and prevent trafficking in the state.
Lyd Paull-Flores is senior director of healthcare for GCOM, a provider of government technology solutions. She designs public-sector transformation programs that enable complex data analysis through an integrated data strategy and data management platform.
