As digital evidence becomes more complex, fragmented, and increasingly shaped by automated systems, confidence in the quality of that evidence has never been more important.

The Data Quality & Integrity Council brings together practitioners, researchers, technology providers, and industry leaders to strengthen the credibility of social intelligence. Together, we explore the principles, guidance, and professional practices needed to ensure digital evidence is interpreted responsibly, communicated transparently, and used with confidence.

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Why this Council exists

The value of social intelligence depends on trust.

Every day, practitioners make decisions based on digital evidence. Yet questions about representativeness, inference, transparency, metadata, platform change, and AI-assisted analysis continue to challenge the discipline.

Rather than prescribing rigid methodologies or endorsing specific technologies, the Council focuses on developing shared principles that help practitioners make better judgements, communicate limitations honestly, and strengthen confidence in social intelligence across industry, academia, government, and research.

Our goal is simple: to help ensure social intelligence remains a trusted source of understanding as the digital landscape continues to evolve.

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What we're exploring

• Representativeness and Inference: Clarifying what social intelligence can—and cannot—reasonably claim about populations, behaviour, and cultural patterns.
• Transparence and Disclosure: Developing shared expectations for documenting data sources, analytical context, assumptions, and methodological limitations.
• Responsible Interpretation: Exploring how professional judgement, context, and transparency strengthen the interpretation of digital evidence.
• AI-Assisted Analysis: Defining the role of human oversight and accountability as AI becomes part of the analytical workflow.
• Data Access & Platform Change: Understanding how platform policies, metadata availability, and structural changes affect the quality and integrity of digital evidence.

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Current workstreams

The Council is currently focused on developing practical guidance across five priority areas:

• Clarifying representativeness and appropriate inference in social intelligence.
• Establishing transparency and disclosure standards for analytical outputs.
• Defining expectations for human oversight in AI-assisted analysis.
• Exploring metadata reliability and data quality across platforms and languages.
• Monitoring how platform changes influence the availability and integrity of digital evidence.

These workstreams will evolve as new challenges emerge and members identify priorities for the discipline.

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What we're building together

The Council exists to create practical outputs that strengthen professional practice across the industry.

Current and planned outputs include:

• Principles for the responsible interpretation of digital evidence.
• Guidance on communicating limitations without undermining credibility.
• Disclosure standards for social intelligence outputs.
• Position papers addressing common misconceptions about social data.
• Contributions to training, professional development, and discipline standards.

These outputs are intended to support practitioners, organisations, educators, researchers, and policymakers—not to prescribe methodologies or enforce compliance.

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How to get involved

The Data Quality & Integrity Council is open to members who want to help strengthen the foundations of professional practice.

Whether you work in research, insight, analytics, technology, academia, government, or consulting, your experience can help shape the principles and guidance that define how digital evidence is understood and responsibly applied.

If you believe stronger methods, greater transparency, and better interpretation are essential to the future of social intelligence, we'd love you to contribute.

Become a member and help strengthen the discipline.

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