Is your organization excited by the potential of generative AI? As a data practitioner, are you wondering how you’ll navigate your company’s journey to LLM-inspired success?
You’re in luck. Recently, Collibra’s Co-founder & Chief Data Citizen, Stijn “Stan” Christiaens, and Accenture’s Cloud First Chief Technologist, Teresa Tung, sat down to chat about the current craze around generative AI and the renewed importance of data.
The result was an informative, inspiring conversation on a range of topics, including:
- How generative AI’s popularity is a great opportunity for data professionals
- What ‘data product thinking’ is and why it’s essential today
- Why AI governance is essential in the new AI era
The opportunity for data pros in an AI era
Thanks to the explosion in popularity of generative AI, it’s a great time to be a data professional.
The opportunity to make a big impact in your organization has never been more apparent — and more visible to senior management. Right now, every CEO is asking, “What are we doing with gen AI?”
In fact, as Stan wrote in a recent blog on AI governance, more than 78% of CIOs think that scaling AI and ML use cases to create business value will be their top priority over the next three years.1
For businesses to stay competitive, they will need to quickly pivot to leveraging generative AI.
However, if you’re an organization that wants to leverage generative AI for your products, your processes, your tools, or anything specific about your company, then that data is likely going to come from an internal source — and it’s going to need AI governance to ensure the data and the model are managed in a responsible way.
In the new world of AI, it will be more important than ever to not only apply an AI governance framework but also to treat data as a product.
Data as a product and why it’s more important than ever
Approaching data as a product is an essential element of another recent technological trend, albeit in the slightly less mainstream world of data management.
Specifically, the introduction and rapid adoption of data mesh, which is anchored around four key principles, including ‘Data as a product.’
The rich framework of data mesh is centered on four guiding principles:
- Domain-driven ownership
- Data as a product
- Self-service data infrastructure
- Federated computational governance
You can learn more about data mesh in our series, starting with our Data mesh overview.
By decentralizing data ownership, data mesh organizations empower business domains to control their data destiny. This is the first principle of data mesh: Domain-driven ownership.
Treating data as a product is data mesh’s second principle — and it highlights the value of data as a strategic organizational asset.
So what is a data product? At its essence, a data product is like any other product: It must provide value to its users. It has a lifecycle. It must find a fit with the people it serves — a product-market fit if you will. And you must be able to measure its ability to meet relevant goals.
In a world where generative AI is widely distributed and foundational across organizational departments, it will be more and more important for business domains to “own” their data product — many of which will be AI data products.
In a data mesh organization, domain owners who are most familiar with their domain-specific data will be in the best position to steward data products and ensure data quality.
As data stewards in the new AI era, domain-focused data citizens will need an AI governance framework to ensure that the data informing AI models is accurate and can be trusted.
Why AI governance is key in a generative AI world
If you’re at a company that’s looking to create generative AI data products, your executives might not know that a data-centric approach to AI is critical.
But you know that AI is only as good as the model and the data that it’s trained on.
As data professionals, it’s critical that we can explain to the C-suite that if significant budgets are now moving to fund an AI roadmap, then a portion of that budget needs to be directed to ensuring data quality.
The proven method for ensuring data (and model) quality when it comes to AI is to leverage an AI governance framework.
Watch the Fireside Chat from Accenture and Collibra
As data mesh is as much a cultural approach as it is a technical framework, so too success in the new world of AI will rely on the implementation of an AI governance framework to manage data and ensure it’s used in a responsible manner.
There’s lots more insights to be found in the Fireside Chat with Teresa and Stan, including getting started on a career in data and recommendations on what to read to become a data leader.
1 Databricks, CIO Vision 2025