Strengthening the Fight Against Human Trafficking: The Critical Need for Data Alignment, Sharing, and True Collaboration

By Kevin Metcalf and Doug Gilmer PhD
February 13, 2025

Republished with permission and appreciation.

The Critical Need for Data Alignment, Sharing, and True Collaboration

Efforts to combat human trafficking in the United States often fall short due to fragmented data systems, inconsistent communication, and isolated operations. While many organizations and agencies work tirelessly to address trafficking, their impact is significantly diminished without a unified strategy for data alignment, efficient sharing mechanisms, and deep, intentional collaboration. To dismantle trafficking networks and provide meaningful support to victims, stakeholders must move beyond cooperation and embrace true coalition-building.

The Role of Lived Experience in Data and Decision-Making

Survivors of human trafficking possess invaluable knowledge that should guide research, policies, and interventions. Too often, data frameworks are built without consulting those who have lived through exploitation, leading to gaps in understanding and ineffective solutions. Survivors must be at the center of data governance, ensuring that the information collected reflects the realities of trafficking, not just external perceptions. This approach leads to more precise identification of vulnerabilities, risk factors, and intervention strategies that genuinely address the needs of those affected.

The Power of a Specialized Ecosystem 

Organizations engaged in counter-trafficking efforts often feel the need to expand their reach, taking on multiple roles to address all aspects of the crisis. However, true impact comes from specialization—where each organization focuses on its strengths while coordinating with others to cover gaps—a fragmented approach results in duplicated efforts and wasted resources. Instead, organizations should function as an interconnected network, leveraging each other’s expertise to build a comprehensive, data-driven strategy against trafficking.

Understanding the Existing Data Landscape

Before initiating new data collection projects, stakeholders must thoroughly analyze existing information. Understanding what data is already available, where the gaps are, and how existing insights can be leveraged prevents redundancy and enhances efficiency. A market analysis approach ensures that efforts complement one another rather than compete. Partnering with established data repositories and research institutions creates stronger, evidence-based solutions that build on previous work rather than constantly starting from scratch.

We must also understand the limitations of the data landscape. Historically, there has been a demand for prevalence data showing exactly how many trafficking victims are in the United States (and globally). We are never going to get there. We can only count what we know, and human trafficking remains a hidden crime. Some cases are never identified or disclosed, and especially in the case of familial trafficking, disclosures of exploitation may not happen until the victim is well into adulthood. Refocusing research on data collection away from identifying the “big numbers” and instead focusing on actionable data analysis, victimization, methodologies, and perpetration trends could help inform intervention better. At the same time, measure progress and outcomes of efforts.  

Further complicating the quest for big numbers is that prosecution data is not an accurate measure of prevalence. Prosecution data only counts cases of human trafficking prosecuted under the predicate human trafficking statutes. Many investigations begin as human trafficking cases, are worked as human trafficking cases, but often end up being prosecuted on other charges for a wide variety of reasons, including law enforcement not wanting to risk the re-traumatization of a victim who may need to provide testimony and be subjected to a hostile defense. This doesn’t mean the victim wasn’t trafficked. It means the defendant was prosecuted on other charges, such as firearms violations, drug crimes, or money laundering. The case, however, won’t be counted in human trafficking data.

The Necessity of a Common Data Language

One of the most significant barriers to effective data sharing is the lack of a unified language across organizations and sectors. Law enforcement, NGOs, research institutions, and policymakers often use different terminologies, making it challenging to integrate data sets and gain a comprehensive picture of trafficking trends. Establishing a standardized ontology—a shared framework for data collection, categorization, and analysis—ensures that information from different sources can be seamlessly integrated and utilized. This common ground allows for more effective tracking of trafficking networks and victim support needs.

The issues go beyond standardized terminology, however. There is a lack of consistency in how human trafficking is reported and identified. The human factor injects some subjectiveness into these cases, as does a lack of human trafficking education, particularly in law enforcement. Indicators of human trafficking, evident to some, could be overlooked or inaccurately perceived by others who are not as well trained.

Beyond Data Collection: The Importance of Intelligence

Raw data alone does not drive change; its true power lies in the intelligence it produces. Large-scale data collection efforts must be accompanied by advanced analytics, cross-referencing, and contextual analysis to uncover trafficking patterns, identify hotspots, and predict emerging threats. Technology, including artificial intelligence and machine learning, plays a crucial role in processing vast amounts of information to detect trends that might otherwise go unnoticed. Intelligence-driven approaches empower law enforcement, service providers, and policymakers to act precisely and efficiently.

Keeping Humanity at the Core of Data Work

Real people are at the heart of every dataset—victims and survivors whose experiences should never be reduced to mere numbers. Data should always be used in a way that protects and serves those it represents. Ethical data practices include ensuring informed consent, prioritizing privacy and security, and using information to advocate for policies that prevent re-victimization. The goal should not only be to document trafficking but to transform that knowledge into action that safeguards vulnerable individuals.

The Need for Continuous Research and Adaptation

Trafficking evolves alongside societal and technological changes, making it essential for ongoing research to inform counter-trafficking efforts. Policies and intervention strategies that were effective a decade ago may not address today’s challenges, such as the increasing role of online platforms in exploitation. By staying engaged with the latest research, stakeholders can continuously refine their approaches and remain ahead of traffickers constantly adapting to evade detection.

Moving Forward: From Cooperation to True Collaboration

We have had far too much cooperation and not enough collaboration in the counter-trafficking movement. There’s a difference. Cooperation aims to garner help to help you achieve your own goals or outcomes. Collaboration, built on trust and accountability, focuses on securing outcomes shared by everyone.

For counter-trafficking efforts to succeed, stakeholders must shift from operating in silos to working in true partnership. This means:

• • Developing a unified data-sharing infrastructure that enables secure, real-time exchange of trafficking-related intelligence.
  • Establish clear collaboration protocols across law enforcement, NGOs, victim service providers, and policymakers.
  • Centering survivor leadership in decision-making processes related to data governance and policy development.
  • Investing in advanced analytical tools that translate data into actionable intelligence.
  • Prioritizing transparency, trust, and ethical considerations in all data-related initiatives.

Efforts to combat human trafficking must be grounded in a shared commitment to impact rather than competition for funding, recognition, or influence. The movement must be guided by a clear vision—one in which stakeholders do not merely cooperate when convenient but operate as a unified force, leveraging their collective strengths to dismantle trafficking networks and support survivors. If we could get all the parties to agree on collaborative efforts, not only would we have more complete data but also more actionable data. Better data could help with improving awareness and prevention activities, aid in victim referrals, bring to light trafficking methodology, and direct resources where they need to go for lasting impact; we must get data out of the silos in which it currently lives.  

To accomplish this, however, there must be a commitment from the national level to provide leadership and rally the counter-trafficking community around their shared purpose. The human trafficking space has not had trusted, respected, and unifying leadership, leadership that can move the policy needle and allocate resources on these issues at the executive level, for some years. Acknowledging the seriousness of the problem and the need for reforms, the creation of an Interagency Policy Committee is needed, where various stakeholders, including survivors, NGOs, victim-serving organizations, and law enforcement, have the opportunity to contribute to the creation of a national strategy or action plan to include a collaborative data model. It is difficult to efficiently dedicate resources to issues we cannot quantify or qualify.  

The challenges ahead are substantial, but by aligning data efforts, embracing strategic collaboration, and keeping survivors at the center of the conversation, the anti-trafficking movement can move from incremental progress to systemic change.

About the Authors