Why data foundations matter for Hereford and Worcester businesses

The Open Data Institute’s (ODI) recent white paper warns that weaknesses in the UK’s tech infrastructure could undermine the economic and societal gains promised by the AI boom. Alongside the risks, it sets out practical recommendations for creating diverse, fair, data-centric AI.

Five actions for government
Based on its research, the ODI is calling for the new government to:
Ensure broad access to high-quality, well-governed public and private sector data to foster a diverse, competitive AI market.
Enforce data protection and labour rights in the data supply chain.
Empower people to have more of a say in the sharing and use of data for AI.
Update our intellectual property regime to ensure AI models are trained in ways that prioritise trust and empowerment of stakeholders.
Increase transparency around the data used to train high-risk AI models.

Risks: why governance and transparency can’t be optional
The white paper argues that the potential for emerging AI technologies to transform sectors such as diagnostics and personalised education shows great promise. Yet significant challenges are attached to widescale adoption. In the case of generative AI, reliance on a handful of machine-learning datasets that ODI research has shown lack robust governance frameworks poses risks to both adoption and deployment. Inadequate data governance can lead to biases and unethical practices, undermining trust and reliability in critical areas such as healthcare, finance and public services.

These risks are exacerbated by a lack of transparency that is hampering efforts to address biases, remove harmful content and ensure compliance with legal standards. To provide a clearer picture of how data transparency varies across different types of system providers, the ODI is developing a new “AI data transparency index.”

Beyond the hype
Sir Nigel Shadbolt, co-founder of the ODI, said, “If the UK is to benefit from the extraordinary opportunities presented by AI, the government must look beyond the hype and attend to the fundamentals of a robust data ecosystem built on sound governance and ethical foundations”.

Policy direction: making data AI-ready
Labour’s election manifesto outlined plans for a National Data Library to bring together existing research programmes and help deliver data-enabled public services. However, the ODI says that first, we need to ensure the data is AI-ready. As well as being accessible and trustworthy, data must meet agreed standards, which require a data assurance and quality assessment infrastructure. The ODI’s recent research has found that currently— with a few exceptions— AI training datasets typically lack robust governance measures throughout the AI life cycle, posing safety, security, trust and ethical challenges related to data protection and fair labour practices.

Insights: what to watch
Other insights from the ODI’s research include:
The public needs safeguarding against the risk of personal data being used illegally to train AI models. Steps must be taken to address the ongoing risks of generative AI models inadvertently leaking personal data through clever prompting by users.

Key transparency information about data sources, copyright and inclusion of personal information is rarely included by systems flagged within the Partnership for AI’s AI Incidents Database.

Intellectual property law must be urgently updated to protect the UK’s creative industries from unethical AI model training practices.

Legislation safeguarding labour rights will be vital to the UK’s AI Safety agenda.

The rising price of high-quality AI training data excludes potential innovators like small businesses and academia.

What this means for Herefordshire and Worcestershire firms
For SMEs across our two counties exploring AI pilots or scaling existing tools, the ODI’s says competitive advantage starts with trustworthy data. Practical first steps include:

Map your data supply chain: know where training data, prompts and outputs originate, and document licences/permissions.

Strengthen governance early: set quality standards, retention rules and human-in-the-loop checks before you deploy.

Build transparency into procurement: ask vendors to evidence data lineage and moderation processes, especially for high-risk use cases.

Protect people and IP: verify compliance with data protection, copyright and labour standards across your providers.

Plan for data costs: budget for high-quality, rights-cleared datasets to avoid lock-in and reduce model risk.

www.theodi.org