I’ve seen one truth hold firm throughout my 25 years in data management: organizations thrive when they trust their data. That’s why helping teams create trusted data products is one of the most rewarding parts of my job. And that work is accelerating in exciting ways.
During my recent webinar with Tiger Analytics, we spoke about advancements in automation and AI that will radically change how teams build data products. And it will happen starting this year. Given what I’ve been hearing from customers about the growing demand for faster data product delivery in the age of AI, these innovations couldn’t come at a better time.
Why trusted data products matter more than ever in 2026
Data trust and transparency are nonnegotiable because AI amplifies everything. Our traditional databases aren’t designed for real-time inferences, continuous validation, and activating agents. AI capabilities are growing faster than we can use them and this gap is widening. However, using AI to blend data modeling, cataloging, data quality, and governance matches speed with trust.
Data products are now logically defined, curated, scored, contextualized, and packaged from use-case-driven prompts. No longer are you self-serving a pile of data and saying, “Figure it out,” to your business. Instead, AI is serving a packaged response based on your enterprise logical model. We call it a data product “spec” combined with business curation and controls. The data products are delivered reliably and responsibly for both analytical users and AI systems. And it’s fast.
A good data product package includes:
- A logical data product model of all relevant datasets (structured and unstructured), views, reports, models, and subsequent data contracts for prompts like customer experience, fraud detection, client onboarding, claims, energy pipelines, etc.
- A data product “trust score” based on the data quality profiling, end user likes, usage, business curation, and scarcity.
- Transparency into the lineage of the data product and subsequent business rules and transformations
- Business controls and guardrails with clear terms, policies, and SLAs
- Governance defining ownership and accountability
- Classifications and tags for privacy protections, regulations, and governance policies
This comprehensiveness fuels effective AI and analytics at scale. And in today’s competitive AI race, that makes trusted data products extremely valuable.
Tiger Analytics’ Ravi Shankar agreed in our recent discussion, noting that customers are realizing data alone isn’t enough. Context, ownership, lineage, and governance must travel with the data. He added that packaging all these capabilities together is what helps people and systems understand and trust data.
What’s changing now is a shift from delivering raw data to providing ready-to-use outcomes. Instead of handing the business massive datasets without context, organizations are packaging curated, governed data products that are directly related to their prompt. And they’re no longer waiting four to six months, to the point when everyone’s moved on and their request is irrelevant.
In contrast, an AI-driven approach saves time, reduces ambiguity, aligns teams around enterprise standards, and creates a usable foundation for AI development. And once organizations begin working this way, they quickly discover an additional benefit: trusted data products create momentum that builds over time.
How trusted data products deliver compounding value
How big is your data product library today? Most organizations already have hundreds or thousands of data products. But do all these LOBs think of and use data products the same way? Probably not. Data crosses business lines, and so do data products. Creating a standard approach will allow your business lines to share and collaborate while increasing the value of your data products over time.
Another advantage of a data product factory-type approach is reuse. If it takes four to six months to create a well-defined, logically modeled, governed, and profiled data product, let’s just say with four different resources, we’re looking at a minimum 400K per data product. And if we’re doing this redundantly because we haven’t centralized sharing and collaboration on these data products? We’re caught in the chasm of data chaos.
The value comes in reusing, collaborating on, and enhancing data products versus always starting with a blank slate. Measuring and monitoring these data products for value and trust is also important because data products aren’t static. They’re ever changing and must be monitored for data drift.
A comprehensive approach is required to blend these steps effortlessly with their own MCPs, agents, and vector database. The advantage of including data modeling in this approach is having that logical classification in your semantic layer. Your LLMs come to life and speak the language of your business versus random AI calls. Organizations with an enterprise logical data model can accelerate and affirm the build of this semantic layer.
A well-designed data product eliminates ambiguity by providing a single, governed version of the truth, creating cohesion across business teams. It also increases trust, and when teams trust their data products, they reuse them. That reuse compounds value, as teams move forward faster. And that speed is set to increase.
Accelerating data product creation with AI in 2026
What once took months of manual, siloed work will now take days, freeing experts for high-value work. It’s one of the most profound changes I’ve seen in my career, and I love how it’s removing friction without sacrificing rigor.
Yes, building trusted data products will feel fundamentally different in 2026, but I promise that’s a good thing. AI will streamline everything, helping you:
Generate a model by using natural language. You’ll clearly state the business question you want to answer or the outcome you’re driving. Instead of weeks spent translating requirements between business, analytics, and engineering teams, AI will generate an initial logical model almost instantly. This will create alignment early and eliminate the endless back-and-forth conversations that slow progress today.
Discover and source the best data. AI will scan your landscape, evaluate quality and freshness, and pinpoint optimal sources. If gaps exist, it’ll generate synthetic data grounded in your standards, making it more trustworthy than generic alternatives.
Apply guardrails automatically. AI will tag sensitive info (PII), associate business rules and policies, and assign trust scores. No more handoffs across modeling, cataloging, and governance teams.
Package and prepare for use. Everything required, including context, controls, documentation, and trust signals, is packaged together. You decide if you want to share and collaborate in a data marketplace or export it to your medallion architecture.
Organizations can start with a focused portfolio of high-value data products and continuously refine and expand from there. With this approach, data scientists innovate, analysts derive insights, and business moves at the aforementioned warp speed, only this time, with you in the driver’s seat.
