From Dashboards to Data Products: Why BI Needs a Ground-Up Rethink

For years, business intelligence (BI) promised answers. We built dashboards, pipelined data, and told ourselves that more charts meant more insight. But today’s business environment moves faster than batch jobs, and “self-service” tools often serve up more friction than clarity.

The real issue? We’ve been treating data like a byproduct — something we package and ship at the end. Instead, we need to treat data as a product: built intentionally, designed for reuse, embedded in workflows, and owned from end to end.

That’s the shift. From static reporting to living data products. From dashboards to decisions. And it’s long overdue.

The BI Bottleneck

Data teams spend most of their time responding to one-off requests, building custom dashboards, and stitching together tools that were never meant to scale. This creates friction between business needs and technical capabilities. Decision-makers wait. Engineers burn out. Insight gets lost in translation.

In a 2024 Gartner survey, 63% of data leaders listed "building and maintaining data products" as a top investment priority for the next 2–3 years. That’s not just a trend — it’s a warning signal. The BI model as we know it is running out of road.

What Makes a Data Product?

A data product isn’t a dashboard or an integration script. It’s a packaged, reliable, and reusable asset that delivers a specific outcome. Think of it like software:

• Built for a known user
• Designed for a clear purpose
• Owned and maintained across its lifecycle
• Governed by contracts and expectations

A good data product combines:

• Data (curated, up-to-date, quality-controlled)
• Metadata and semantics (so it's understandable and reusable)
• Access logic (permissions, delivery models)
• Templates and interfaces (so it fits into real workflows)

It’s findable. It’s trusted. It doesn’t require a handbook to explain how it works.

How to Build Data Products That Last

1. Modular, Not Monolithic

Avoid hardwiring data logic into dashboards. Build modular systems where storage, modeling, visualization, and governance are decoupled. That means:

• Using a composable infrastructure
• A separate semantic layer
• Federating storage and query layers when needed

2. Governance Baked In

Products don’t work if no one trusts them. Define access, quality, and lineage as part of the product spec — not as an afterthought. Data contracts matter.

3. Embedded in Real Workflows

If it lives in a tool no one opens, it’s not helping. Embed insights where work happens: CRM, support systems, supply chain dashboards. Products deliver value when they’re used.

4. Designed to Be Reused

If you’re building the same churn report ten different ways, something’s wrong. One product, many consumers.

5. Measured and Iterated

Every data product should come with its own KPIs:

• Is it used?
• Does it improve a process?
• Does it help someone decide faster?

Ship, observe, improve.

What This Means for Teams

You can’t do this with a reporting mindset. You need product managers. You need reusable templates. You need platforms that support CI/CD, modular deployment, and observability.

This is a shift in ownership and thinking:

• Data teams become product builders
• BI teams think in terms of services, not deliverables
• Business stakeholders collaborate on roadmap and value

This is where the real work begins. Because it’s not just about getting the tech right. It’s about building the muscle to deliver trusted, high-leverage insights over time.

Making It Real: The Platform Side

Many BI platforms are trying to retrofit these ideas into legacy tools. But delivering data products at scale — with governance, performance, and developer control — requires a foundation built for it.

That’s why GoodData was designed the way it is:

• Composable Architecture: Built on open components like DuckDB, Iceberg, Arrow, and FlightRPC, GoodData integrates with modern data stacks out of the box.
• Lifecycle Governance: Role-based access, tenant-aware security, lineage, and audit trails are built in — not added on.
• Real-Time Query Engine: Designed for speed and cost-efficiency, with hybrid storage models and federated execution.
• Dev-First Controls: YAML configs, APIs, and SDKs for everything from modeling to deployment. No vendor lock-in. Total flexibility.
• Designed for Multi-Tenant Scale: Serve multiple customers or teams without rebuilding. Scoped dashboards, reusable semantics, dynamic data products.

If you're serious about building data products, you need a platform that treats them like products.

Final Thought

Data isn’t just infrastructure. It’s a service. It’s a product. It needs ownership, design, and a roadmap. And it needs the right tools to make that real.

The BI world is changing fast. Static dashboards won’t cut it. The teams that embrace data product thinking will move faster, build trust, and deliver real business impact.