Raina Das of Binary10: AI readiness starts with trusted data

The work behind a trusted decision

For Raina Das, explaining a data consultancy means starting with the decisions its clients need to make. Binary10’s Marketing Executive describes the business as helping organisations make decisions they can trust, through work on data migration, integration, quality, archiving and testing.

Speaking at DigiGov, she puts that work in the context of large data transformation programmes. The company’s services page adds practical detail: moving information from legacy systems to modern cloud platforms, connecting systems, checking data and processes, and retaining historical information when older systems are retired.

Is the data ready for the AI ambition?

Raina’s warning is straightforward: enthusiasm for AI does not resolve problems in the information underneath it. She describes interviews for a public-sector report where interest in adopting AI tools was strong, but says poor data quality can amplify existing problems and undermine the resulting reports.

Binary10’s 2026 Public Sector AI Readiness Report overview develops that argument around fragmented information, inconsistent definitions, reactive quality management and unclear ownership. The published overview describes organisational barriers alongside technical ones.

A useful independent reference is the Government Data Quality Framework. It distinguishes six dimensions: completeness, uniqueness, consistency, timeliness, validity and accuracy. Its distinction between completeness and accuracy is especially useful: having every expected record does not mean the values in those records are correct.

That turns a broad question about readiness into a more practical conversation. What decision will use this data? Which quality problems could change its outcome? Who can investigate and correct them? An attractive dashboard needs those answers as much as it needs a clear interface.

Raina describes a Power BI-based data-quality tool being demonstrated at the event to make issues visible. It puts the focus on making problems visible before using that information elsewhere.

A technical business still needs a human conversation

Asked about the marketing message, Raina returns to the people doing the work. She describes herself as non-technical and values technical colleagues who collaborate well with people outside their specialism.

There is a practical lesson in that emphasis. Data quality becomes easier to act on when specialists can explain a problem, its consequences and the next step to the people responsible for the decision. The message is stronger when the technical work and the human explanation support each other.

For a buyer’s view of the questions behind AI adoption, read Bola Soko’s story on AI procurement.