It’s no longer news that data is the most critical resource for organizations looking to capitalize on emerging technologies like AI. BI vendors are bringing exciting capabilities allowing more people real-time, self service access to data but requiring high quality data to accomplish this. Recognizing this, companies have poured vast resources into data platforms. And yet, many still struggle to scale their success. Why?
A common early pattern is to build data products via artisan coding: small, highly skilled teams manually engineer pipelines, often moving data raw to a “stage” and then straight to the publish layer. This approach can work—for a while. But like handcrafted goods, it doesn’t scale. There aren’t enough expert developers to go around, and it’s unrealistic to expect consistent quality or sustainable delivery across a growing enterprise.
If you want to develop a true enterprise-grade data asset—one that scales with the organization—you’re not playing in the minors anymore. You’re in the big leagues. And that means embracing structure, automation, and collaboration.
Here’s what that looks like:
- Resilience through data modeling. You need robust architectural patterns like Data Vault or similar methodologies to enable auditability, agility, and long-term maintainability. A raw-to-publish pattern might look fast at first, but it becomes a liability when data changes, business logic evolves, or new teams need to engage. Your data platform must stand the test of time and built for it from day one.
- Enable the many, not just the few. Your data platform must serve not only centralized data teams, but also domain experts embedded in the business. These users won’t all be fluent in Python, SQL, or dbt. You need an environment where low-code and pro-code coexist—where the skills of senior data engineers are scaled through automation, templates, and reusable components.
- Agility despite complexity. Enterprises can't wait weeks or months for data products. The platform must allow rapid, iterative delivery—even as it manages lineage, versioning, security, and orchestration under the hood. Think “DevOps meets DataOps.” Your workflows must be automated to understand dependencies, halt data flows for updates and catch up when deployments are done.
- Outcome over effort. The measure of your platform’s success isn’t how much data it stores or how elegant the codebase is. It's whether it helps the business make better decisions—fast. Can your teams deliver the right data to the right people, in time to act on it? That’s the benchmark.
- Make effectiveness measurable. To scale your data efforts and earn continued investment, you need visibility. That means full transparency across the platform: which teams are producing which data products, how quickly they’re delivering, how reliably pipelines run, and how often data is actually used. This transparency enables skill development, highlights bottlenecks, supports governance, and gives stakeholders a clear view into progress—essential for sustaining momentum and justifying funding.
This calls for a shift in mindset. From craftsmanship to engineering. From heroic effort to team success. From fragmented tooling to a cohesive data operating model. And above all, from isolated pipelines to an enterprise platform where automation, collaboration, and agility are not just buzzwords—they’re built in.
Because if you’re serious about data, you’re in the big leagues. And it’s time to play like it.