Bedrock picture from DuckDuckGo and not a Minecraft one
Data Governance as the Bedrock of Effective AI Governance
Martijn Veldkamp
“Strategic Technology Leader | Customer’s Virtual CTO | Salesforce Expert | Helping Businesses Drive Digital Transformation”
November 3, 2023
As an organisation you need a plan to address the market disruption of Generative AI. You don’t need to build your own version of ChatGPT. But you need a plan on how your organisation will deal with all the initiatives that will start. Otherwise I wish you good luck with the conversation you will have when one of your CxO comes back from some partner paid conference stating that the company will be bankrupt if you don’t invest right now.
In this series of articles I felt the need to explore some of my current thinking on where Generative AI has it’s place.
Business Braveheart
In this ever-renewing push of the newest flavour of technology, the fusion of architecture, governance, and data governance stands as the cornerstone for reliability.
As organisations navigate their discovery of the complex realm of artificial intelligence, it becomes increasingly apparent that the success of implementing one of these LLMs (ChatGPT, Bard or Bing AI) is deeply entwined with the quality, security, and integrity of the data that they need and produce.
Effective AI governance is not just about fine-tuning algorithms or optimising your LLM models. It begins with the bedrock of quality.
Garbage in, garbage out
Feedback loop
It’s the quality, accuracy, and reliability of the input data that dictates the usefulness of AI’s output. Thus, a holistic approach with a very strong foundation in data quality and it’s governance. And remember, the prompts that you use for getting results is also data that needs to be governed. How else will you establish a feedback loop on effective usage of the tool?
The Interdependence of Data Governance and AI Governance
Data governance, as I’ve stated in the previous blog posts, primarily concerns itself with the management, availability, integrity, usability, and security of an organisation’s data.
AI in any form, by its nature, operates as an extension of the data it is fed. Without a sturdy governance structure over the data that you produce, AI governance becomes a moot point. On another note I’m still surprised nobody came up with an AI that generates cute cat short clips for a Youtube channel. Wait, I’m on to something here…
Quality Data: The Lifeblood of AI
A key aspect is that the quality of data isn’t an isolated attribute but a collective responsibility of various departments within an organisation. We all know that, but where does the generated data sit?
In the past I wrote about System Thinking and I still have to plot for myself where Generative AI sits. Is it like our imagination? Where do you Master the data LLM generates for you? Can I re-generate reliable? What happens with newer generated outcomes? Are these better then the old? Is the generated response email owned by the Service Department or the AI team? These articles are as much for you as for me to fully grok where LLM and it’s outcomes sits in the system.
Security and Ethical Implications
Privacy concerns, compliance with regulations, and ethical considerations in handling and processing data are pivotal components of data governance. As AI systems often deal with sensitive information, ensuring compliance with data protection regulations and ethical use of data becomes a critical component of AI governance. The same goes for the outputs. Where are they used or stored? How do these different data providers compare? The question that popped up in my head was that within the Salesforce ecosystem we use a lot of Account data and have linked with third party providers. We enrich the data that we have of the customer with Duns & Bradstreet information or in the Netherlands with the KVK register. What happens with the ‘authority score’ if we add Generative AI in the mix? We still have a lot to discover together.
Keep it simple
Keep calm meme-o-matic
In short because I harped on it before. Organizations should:
Establish Comprehensive Data Governance Frameworks: Institute clear policies for data ownership, stewardship, and data management processes. This not only fosters quality but also ensures accountability and responsibility in data handling. Promote Cross-Functional Collaboration: Break down silos and encourage collaboration between various departments. Not just good for data quality but many more aspects in life. Leverage Automation for Data Quality Assurance: Harness the power of automation tools to identify anomalies and inconsistencies within data, ensuring high-quality inputs for AI models. Ever did a large migration from one system to another? Right, automation for the win!Continuously Monitor and Improve Data Governance: Implement systems for ongoing monitoring of data quality. We have a Dutch expression which translated goes something like “The Polluter pays”. Bad data has so many downstream effects that I almost want to advise to have a monthly blame an shame high light list. Let’s forget about that for now. I do however want to stress a carrot and stick approach.
Conclusion
In my subsequent articles, I’ll try to delve deeper into the practical strategies and steps organisations can adopt and make it more Salesforcy.
