Data Governance is having a moment or, at the very least, is about to. Governance was one of the key topics discussed in the opening address at the recent Gartner IT symposium. Obviously, it goes without saying that the main topic was Artificial Intelligence and Agentic AI. But before we get carried away, here’s some context on the impact Artificial Intelligence is having in the business landscape:
- The UK AI Market is worth more than £16.6B and is projected to grow to over £700B by 2035
- 28% of businesses have built their own AI solutions or are using existing AI tools like CoPilot or ChatGPT
- In the Autumn Budget in November 2024, the UK government laid out plans for an AI Opportunities Action Plan, which should provide a ‘roadmap to capture the opportunities of AI to enhance growth and productivity.
Are you in the AI game?
There’s no question that ‘AI-driven’ business is here to stay… much like ‘data-driven’ (insert your own marketing line here). But Data Governance Consulting at Oakland is very much rooted in the operational and practical side of real-world business.
We prefer to consider it as “AI-enabled: A business that gains real value from their AI solutions’’. Back to our earlier point, 2025 AI is all about Agentic AIs: But is Agentic AI the future or just another AI technology? (although we have been developing intelligent agent solutions way before they were renamed Agentic AI, and all the major tech providers got on board)
The beauty of AI Agents is that they are so much more than chatbots. They enable organisations to deliver focused value and target specific problems and opportunities in the data ecosystem. This is why honing down on use cases and relating them to the pains of business users is crucial in creating lasting AI solutions.
Read the small print!
So yes, AI promises to improve decision-making and deliver real value in a game-changing way. But… make sure to pause and read the small print on this promise, in particular:
‘Your data quality and your AI governance capabilities will directly impact the value you extract from your investments in AI’
A study from RAND concludes that 80% of AI projects will likely fail. This prognosis is not dissimilar to Gartner’s, which predicts that 30% of GenAI projects will be abandoned after POC. A key reason mentioned in both studies is the lack of high-quality data.
What is high-quality data? (A holy grail..? Of course not!)
- First and foremost, it’s important to know what data you have, where it exists, and who in the business has accountability for it. You don’t need to boil the ocean. Start with critical data domains related to your AI use cases.
- It knows what benchmarks and rules need to be applied to your data to classify it as good or bad: it is unlikely that you need 100% completeness for some of your data sets – understand what business purpose the data caters for, and this helps you define your benchmarks.
- Having a view of your data eco-system is not just about having an architecture map that details the technical integrations. Have a view of the business processes and information requirements that underpin this architecture. Highlight where the risks are, e.g., because of duplication, manual interventions, or siloed processes.
- Having mechanisms to monitor, log, and address data issues as they get flagged. This allows the business to mitigate risk and avoid confusion when resolving issues quickly but also allows it to be more strategic in improving business capabilities.
- Documenting your critical data dependencies and how your data flows cross-functionally, i.e., your data lineage. This not only helps to make informed decisions on what protocols are needed to keep your data secure and address issues but also allows you to nail down requirements when scaling your data capabilities quickly.
- If you are in a heavily regulated industry, then data security and regulatory compliance will require more robust data governance unless you want to risk £££ in fines and loss of your reputation.
- People, People, People: Do not forget that ultimately, it is your business users who will make or break any data initiative. Training, engagement, data mindset, and change management are all key contributors to creating and sustaining good data.
AI Governance vs Data Governance
Governing AI solutions is indeed wider than just governing your AI data. Trust is perhaps the greatest challenge to business adoption of AI, which is why it is just as important to govern the technical side of things, for example, how the models and algorithms are developed, trained, and deployed, as well as the architecture they rely on.
AI governance faces a few challenges, including:
- Explainability: A lot of AI models can feel like mysterious black boxes – we know something is happening, but we have no idea how!
- Unstructured Data: Things like emails, documents, images, and videos are everywhere. Unstructured data makes up about 80-90% of all new data. It’s like trying to organise your sock drawer – where do you start with all the holey, single, Peppa Pig themed socks that have found themselves in your draw?
- Ethical Concerns: It’s critical to make sure AI systems are fair and unbiased.
- Data Privacy and Security: Protecting sensitive data used in AI training and operations is essential.
Do I need Data Governance before Artificial Intelligence?
Data governance is essential before implementing artificial intelligence (AI) if you want your AI initiatives to succeed. Here’s why:
1. Data Quality Drives AI Performance
AI systems depend on high-quality data to learn and make decisions. Without proper data governance, your AI models may be trained on inaccurate, incomplete, or biased data, leading to unreliable outputs and potentially harmful business decisions.
2. Ensuring Data Compliance and Security
AI often involves handling sensitive data. Data governance ensures compliance with regulations such as GDPR, CCPA, or industry-specific standards. This reduces the risk of legal penalties or reputational damage from improper data usage.
3. Establishing Data Accessibility and Consistency
Data governance creates a framework for consistent data definitions, ownership, and access control. AI thrives on integrated and well-structured datasets. Governance ensures that the right data is available to the right teams in a usable format, eliminating silos and duplication.
4. Mitigating Bias and Ethical Risks
Poorly governed data can introduce bias into AI models, leading to unfair or unethical outcomes. Data governance provides processes to identify, address, and monitor bias, ensuring AI systems make equitable decisions.
5. Cost and Efficiency Benefits
Investing in data governance upfront reduces the time and cost of preparing data for AI initiatives. It prevents expensive rework or failures by addressing data issues early, rather than discovering them during or after model development.
Read our blog on how you can demonstrate the ROI of Data Governance.
6. Scalability for Future AI Projects
Data governance provides a scalable foundation for expanding AI capabilities. It ensures that as data volumes grow, they remain manageable, trustworthy, and usable for future AI-driven projects.
Nothing’s perfect.. and that’s alright
At Oakland, data governance consulting doesn’t mean you need perfect data to embark on or progress on your AI roadmap.
AI gets your data talking.. you might not like what it says if your data is incomplete or of poor quality, but on the plus side, you can also use AI to flush out where your data issues are (every cloud has a silver lining)
How do we manage AI regulatory requirements
As with everything, a balanced approach is essential.
Yes, it is a balancing act between being compliant and giving the business space to innovate. The good news is that, although the AI regulatory landscape is continuously evolving, a key theme is the ability to demonstrate that your AI is trustworthy—and a data governance framework allows you to demonstrate trustworthy AI data pipelines.
“AI, Generative AI or Agentic AI holds great promise. However, many organisations currently feel held back by the necessity to validate their data thoroughly, unclear understanding of AI risks, and the potential for unforeseen issues, like bias and data security. Either making them hesitant to fully commit, i.e. death by POC, or unrealised sustainable value when deployed.
These are unchartered waters for most. The good news is you can choose the pace you want to go. Navigating AI governance is like steering a ship through a storm; robust data governance is your compass, guiding you to safer, clearer waters”.
– Zareene Choudhury – Oakland Data Governance Lead
Want to read more?
Want to know more about how to leverage Data Governance to get your AI Data trustworthy – tune into our webinar on February 19th where I’ll be joined by Richard Adams of erwin by Quest.
Get in touch
If you’d like to learn more about Oakland’s data governance consulting services, then get in touch by emailing [email protected]Want to read more about what a suitable Data Governance Framework looks like?
And if you want to find out how AI agents can make a difference to our business, read some of our case studies here, where we’re already delivering value to clients (well before AI agents became ‘the phrase of the year’!
