In the age of AI, one theme is increasingly clear: data alone is not enough. Organizations must curate data by packaging it with the context and metadata that give it meaning—so it becomes usable by AI, trustworthy to users, and aligned with compliance and governance. At the same time, every data asset must be delivered in ways that are legal, auditable, and safe for the business. That’s where the concept of a data product becomes essential.
Recently, experts from a global sports organization and a data-science subsidiary for a top retailer joined for a fireside chat to discuss how their teams build and scale data products—and what lessons apply to everyone from data engineers to analytics leaders.
What exactly is a data product?
Ask five data professionals to define a “data product,” and you’ll likely get five different answers. For some, it’s the output of a machine learning model. For others, it’s an algorithm embedded in an application, a KPI dashboard, or a data service used for decision-making. The definition varies by role—but the unifying thread is intent and ownership.
The retail data science leader described data products as more about mindset than form:
“It’s about applying product-centric thinking to data, no matter what that data may be. It could be KPIs, algorithms, or operational data. How do you apply ownership? How do you think about the customer first? How do you ensure interoperability across the organization?”
In other words, a data product isn’t just a dashboard or dataset—it’s a deliberate, owned, and consumable asset designed to deliver value. Adopting this mindset changes how data professionals across roles approach their work.
Product thinking for every data role
Product thinking isn’t reserved for data product managers alone.
For data engineers, it means building pipelines that are reliable, reusable, and discoverable.
For analysts, it means ensuring outputs are trusted and consistent.
For leaders, it means aligning data investments to business outcomes.
This shift—from thinking in terms of “outputs” to “products”—elevates data work from tactical execution to strategic impact. Applied consistently, it builds a bridge between technical data teams and business decision-makers.
Data products as a bridge between business and data
A persistent challenge in organizations is the gap between technical teams and business priorities. Data teams often work in isolation, while business stakeholders can feel disconnected or distrustful of analytics. Thoughtfully designed data products help close that gap.
At a global sports organization, teams apply a traditional product lifecycle to data development:
Begin with requirement sessions to define business problems in clear terms.
Run agile sprints to iteratively build and improve the product.
Host bi-monthly showcase sessions where teams demo new releases and gather feedback.
Evaluate success by asking stakeholders whether they can now answer business questions more efficiently than before.
Over time, this process fosters convergence around shared definitions and reduces redundant efforts across departments. By adopting a data product lifecycle approach, data teams can tie deliverables directly to business needs—a principle echoed by the data science leader, who emphasized that “achieving business outcomes” should be the goal of every data product.
The semantic layer: Unlocking value across data roles
Even in mature product systems, fragmentation can creep in. Different teams may define metrics like “customer lifetime value” or “active user” inconsistently, eroding trust and slowing adoption.
A semantic layer (sometimes called an agentic knowledge layer) solves this by harmonizing metadata, definitions, and relationships across data products. It provides a consistent business lens over technical infrastructure.
As the data science leader explained:
“We don’t want to build a monolithic data product. The semantic layer draws relationships and provides definitions. The business needs to know what things mean—and that’s where this layer provides clarity.”
The semantic layer enables:
Business users to refer to canonical terms regardless of where data resides.
Analytics teams to reuse consistent definitions and avoid discrepancies.
AI systems to consume metadata-rich products with confidence.
At the global sports organization, this semantic layer serves as an educational backbone for onboarding, ensuring that new employees can quickly understand data definitions and connections. For engineers, it enforces consistency; for analysts, it minimizes metric debates; and for AI builders, it strengthens model reasoning.
Building AI atop data products
In the AI era, data products matter more than ever. AI agents can be viewed as another persona consuming data. When designing for AI, the same care must be taken to include context, definitions, access controls, and error handling. The more contextually rich the data, the more reliable the AI outputs.
At the global sports organization, AI agents are designed with the end user’s voice in mind. Teams involve real users throughout the build process to ensure AI products are practical, usable, and adopted widely—not just experimental proofs of concept.
Trusted, well-crafted data products are the foundation for AI readiness. But that foundation is best built through deliberate sequencing and small wins.
The playbook: Start small, scale big
One of the most common missteps organizations make is attempting to build too many data products at once. Both the global sports organization and data science subsidiary emphasize the importance of focus and incrementalism.
The latter’s approach was to “create key data products that could demonstrate what good looks like,” proving value through early wins and stakeholder engagement. These early successes then snowball, building momentum and confidence to scale across departments.
Starting small allows teams to demonstrate tangible value, build credibility, and create reusable frameworks—laying the groundwork for broader transformation.
Don’t be afraid to challenge the status quo
Cultural change is often the biggest obstacle in data transformation. Progress requires transparency and a willingness to confront inefficiencies. As Sylvester noted, “A lot of the data product journey is a culture change. But when you start to get first adopters, you see progress—and that enables the business to move faster.”
Openly discussing challenges and gaps fosters trust and accelerates adoption. Over time, resistance gives way to collaboration—and scaling becomes the next frontier.
Why data leaders must think like marketers
Even the best-engineered data products won’t gain traction without adoption. Data leaders must market and evangelize their work internally to build excitement and usage.
As the session moderator observed, “Data leaders have to evangelize and market their work to drive adoption and success.”
At the global sports organization, bi-monthly showcase sessions serve this purpose. What began as a 15-person analyst meeting has grown into a 50-person community of practice. By sharing progress, gathering feedback, and celebrating wins, teams build visibility and momentum across the enterprise.
Measuring success: Beyond adoption metrics
Success isn’t measured by vanity metrics like query volume—it’s defined by outcomes. Leading organizations assess whether data products actually solve business problems more effectively than before.
At the global sports organization, the showcase sessions have created a cultural flywheel: analysts share progress, reduce duplication, and converge on consistent definitions, leading to organic growth in adoption and trust.
Success can take many forms—faster time-to-insight, improved AI performance, or higher stakeholder satisfaction—but all point to the same goal: alignment between data products and business outcomes.
Careers in data product management
The rise of the Data Product Manager (DPM) is reshaping the modern data organization. As companies seek to unify engineering, analytics, and AI strategies, this role has become a cornerstone of success.
Alation recently announced an initiative to train 10,000 Data Product Managers through a first-of-its-kind learning program designed to help professionals build the skills required for this evolving discipline.
For data engineers, analysts, AI practitioners, and business strategists alike, DPM represents a career path that blends technical depth with business acumen.
Conclusion
Data products aren’t just another buzzword; they’re the foundation for trusted analytics, scalable AI, and meaningful business outcomes. From starting small to building semantic layers, from evangelizing adoption to measuring value, the lessons from this session show that every role in data has a part to play in the journey.
For those ready to take the next step, explore Alation’s initiative to train 10,000 Data Product Managers and start building the skills to shape the next generation of data and AI.
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