4 Ways AI Can Transform Data Product Maintenance

Introduction: Beyond the launch excitement

In my previous blog, we explored why maintaining data products is often the breaking point for otherwise promising data initiatives. We examined how the myth of "non-invasive governance" frequently fails and why establishing clear ownership through data products offers a better path forward.

But even with proper accountability structures in place, the day-to-day work of maintaining data products can feel overwhelming. The question remains: How do we make this essential but often tedious work sustainable for the long haul?

This post offers practical solutions to turn data product maintenance from a burden into a manageable, even rewarding part of your data strategy. By leveraging Alation to uncover business value, motivate behavior with social dynamics, provide an intuitive user experience, and harness the power of AI, you can build a program that doesn't just survive—it thrives.

Because even the most valuable data products need practical support systems to thrive long-term. Here are four approaches that can help make maintenance sustainable, with reduced effort:

1. Incentivize with business value

Ensure every data product has a clear and measurable return on investment. With the help of AI agents, it's easier than ever to surface these insights by analyzing data usage patterns and tying them to strategic objectives. For example, an agent can automatically identify:

• Who is using the product and how often—highlighting adoption and reach.

• Which business goals or OKRs the data product supports by mapping usage to outcomes.

• Where it has driven efficiencies or cost savings, such as reducing manual reporting or streamlining compliance workflows.

• How it’s influencing better decisions or even unlocking new revenue opportunities by surfacing previously hidden insights.

When stakeholders can clearly see the value their data products create—backed by real-time AI-driven analytics—they’re more likely to invest the time and energy to keep them current. Data stewardship may feel like a chore in isolation, but when it’s connected directly to business outcomes, it becomes a meaningful and motivating contribution.

2. Close the feedback loop with AI

Social feedback is inherently motivating — and AI can help surface it at scale. Use an AI agent to analyze real usage patterns and generate insights that are shared directly with data product managers:

• How are people actually using their data product?

• How often is it being referenced or shared?

• What parts are generating the most value or engagement?

• Where are users running into friction?

By delivering this kind of targeted, personalized feedback, the AI agent acts like a “social insights coach” — giving product owners visibility into their impact. These signals reinforce intrinsic motivation, much like social media feedback keeps creators engaged. The more clearly data product owners see the value they're delivering, the more invested they'll be in improving and evolving their work.

3. Reduce friction with great UX

Great UX turns maintenance from a chore into an effortless routine. An AI agent can further reduce friction by surfacing and facilitating the right actions at the right moment—whether it’s prompting a quick update, integrating into existing workflows, or suggesting improvements. 

The experience should adapt to the user: intuitive and guided for business users, fast and programmable for developers. When the system feels responsive, lightweight, and personalized, keeping data products up-to-date becomes the path of least resistance.

4. Let AI handle complexity

Recruit AI agents to your team to tackle some of the complex, high-effort tasks involved in managing data products. AI can:

• Automate data curation and integration, ensuring consistent, high-quality datasets by merging and enriching data from various sources without manual intervention.

• Build data products by identifying needs based on use cases, user information, and metadata, suggesting new data products, and even automatically creating data models to meet those needs.

• Identify gaps in data coverage and automatically suggest or add relevant attributes to improve product completeness.

• Streamline version control by automatically tracking and managing changes across datasets, metadata, and models, ensuring consistency and reducing manual errors.

By letting AI handle these types of multi-faceted tasks, data product owners can focus on higher-level decision-making, strategy, and overseeing execution—boosting efficiency and accelerating their impact.

The future: From drudgery to innovation

Let's acknowledge a fundamental truth: data stewardship is critical work, yet it often feels thankless. The meticulous documentation, governance, and quality assurance required to maintain high-value data assets can be tedious and time-consuming. Yet as AI becomes increasingly central to business operations, this work has never been more important—AI systems are only as good as the data they're trained on.

Data products offer a partial solution by creating clear accountability and ownership structures. When specific individuals are responsible for specific data assets, the work gets done. But this is just the beginning.

Our larger vision is more transformative: using AI to automate the tedious aspects of data management entirely. As our CEO Satyen Sangani recently shared, "We spent 12 years building catalogs and we're going to spend the next three making them invisible." The goal is to make data catalogs and governance so seamlessly integrated that they essentially disappear into the workflow. (Our recent acquisition of Numbers Station AI is in service to this vision.)

With AI-driven agents, we're building technology that can auto-generate documentation, intelligently apply governance policies, and guide users to discover and use the right data. These agents combine deep domain expertise with rich metadata to create automated processes tailored to each organization's unique context.

This means organizations can scale their governance programs without massive headcount increases. Data teams gain efficiency, business users gain confidence, and organizations can accelerate their mission-critical initiatives. AI doesn't just make data management easier—it makes it better.

Launch data products with Alation

With Alation's Data Products offering, we're building for long-term sustainability — not just initial success. We're designing features to make ongoing maintenance easier, more rewarding, and increasingly automated through AI.

The future of data management isn't about forcing humans to do more tedious work—it's about leveraging AI to amplify human expertise, allowing teams to focus on driving business value instead of getting bogged down in manual processes.

We won't just put you on the bike and remove the training wheels — we'll give you the tools to ride confidently, keep going, and build momentum. Together, we're creating a world where data products don't just launch successfully—they continuously deliver value, evolve with your business, and power innovation for years to come.

To learn more:

• See the Alation Agentic Platform, Documentation Agent, Data Quality, and Data Products Builder product pages

• Explore the Data Products Marketplace

• Explore our professional services offering, the Data Products Playbook