Background and Challenge
The estimated number of humans trafficked in the United States alone varies by more than an order of magnitude, with numbers as low as 1,400 and as high as 2.2 million! In any industry, achieving a measure of effectiveness within such a wide range would be impossible. Under such circumstances, sound policy decision-making gives way to emotional responses lacking fact-based evidence, funding requests to prevent trafficking and provide victim care appear dubious, and resources and efforts can be easily misused when adequate measures of the problem remain so vague.
The reasons behind the lack of quality data are real and challenging, including: security concerns, bendable and misunderstood definitions, mixed criminal elements, gray areas especially within labor trafficking definitions, lack of harmony among existing data sources, over and undercounting of participants, and simple unwillingness to share data. These challenges have been documented since 2002, and yet, the problem remains largely the same in 2015.
The Solution: A Data Standard
Clear data standards and standardized security protocols are the beginning of a solution – a groundwork that can help deliver better numbers and efficient continuity of care for survivors.
Affected sectors and likely stakeholders include:
- agencies in the chain of care
- funding organizations interested in accurate reporting
- technology providers (e.g. IBM, Oracle, EMC) interested in security and transmission of data
- industries (e.g. mining, cocoa, shipping, fishing) wanting to eradicate nefarious acts in their logistics path
- uses definitions to lessen confusion
- has well-defined sets of structured data (specific labels, names and values)
- can be the backbone for cooperation
- can be used to derive meaningful statistics
- is constantly reviewed for applicability and efficacy, can be pruned or expanded
- can be the basis for auditing agencies’ processes, both internally and externally
The Human Trafficking Standards Initiative is working toward
building a standard by:
- Observation of how data is used between agencies, uncovering points of agreement and negotiation. On-the-ground observation identifies and assesses how data is captured and read in each participating agency, identifies a minimum set of data, finds sets of values that make sense, and determines what data creates the most understanding and least confusion.
- Developing alliances with the public sector, research and think tanks, commercial tech firms and other stakeholders.
- Normalizing data for preliminary standard to include common attributes, minimum sets, boundary conditions.
- Developing a preliminary Standards Committee that will eventually mature to independence as an international and self-governing body over the course of the project.