A few weeks ago, a VP of Analytics confessed he’d spent half his time just tracking down the right dataset before any real analysis could begin. Unfortunately, his story wasn’t unique. It’s a sentiment we’ve heard from countless data teams: valuable insights are trapped behind layers of disconnected systems and bottlenecks. Today, “data silos” aren’t a technical buzzword—they’re a very real, very human challenge.
In this article, we want to share a practical framework for tackling data silos head-on. It’s shaped by what we’ve learned from working with diverse organizations on their data journeys—some have soared by democratizing their information, while others are still wrestling with how to even begin. Let’s dig in.
What Are Data Silos—and Why Are They So Problematic?
At their core, data silos emerge from two primary causes:
- People — Departmental structures and cultural boundaries.
- Technology — Specialized tools that don’t talk to each other.
When these forces converge, data gets locked in pockets across the organization. Here’s a quick look at the common problems that arise:
- Time & Efficiency Woes: I’ve heard from teams who spend days or weeks fulfilling simple data requests. Different groups often waste time duplicating the same work because they don’t know it’s already happening elsewhere.
- Data Quality & Trust Issues: Multiple versions of “the same” dataset pop up, and no one knows which is correct. Confidence in metrics plummets. Folks start second-guessing every report, leading to hesitation and delays.
- Scaling Roadblocks: As companies grow, data requests multiply, but core data teams can’t keep pace. Teams adopt shiny new technologies without integration plans, fragmenting the data landscape.
- Discovery & Access Struggles: Without a single “home” for data, teams can’t find what already exists. This leads to repeated confusion and lost opportunity.
- Resource & Cost Concerns: Silos create hidden drains on budgets—think redundant data storage, duplicated tooling, and wasted engineering hours.
“We were constantly reinventing the wheel. It felt like every project team was spinning up the same data pipelines—just in slightly different ways.” – A Lead Data Engineer we spoke with recently
Key takeaway: Silos aren’t just annoying. They slow teams down, erode trust, burn budgets, and ultimately limit a company’s ability to make data-driven decisions.
Solving Data Silos: The 6-Part Framework
Interestingly, the two factors that cause data silos—people and technology—also shape the strategy to dismantle them. From my perspective, this comes down to building the right culture (people) while implementing the right infrastructure (technology).
To bring that to life, I’ve seen six capabilities consistently lead to success:
- Empower Domains with a Data Center of Excellence
- Establish a Clear Governance Structure
- Build Trust Through Standards
- Create a Unified Discovery Layer
- Implement Automated Governance
- Connect Tools & Processes
Think of it like a dual approach—culture plus tooling—that drives alignment on ownership, discovery, and collaboration.
1. Domain Empowerment with a Data Center of Excellence
In a “domain ownership” model, teams are directly responsible for their own data, while a central data group (a Center of Excellence) provides the foundation, standards, and shared tooling.
Real-World Example:
- At Autodesk, a central Analytics Data Platform team was inundated with ingestion requests—more than they’d handled in their entire history. By empowering 60 domain teams to manage and publish their own data products (with standardized governance in place), they delivered 45 new use cases within two years. Data remained discoverable by everyone, yet each domain took charge of its own datasets.
Why It Works:
- Domain teams become stewards of their data, improving accountability and quality.
- Centralized guidance still prevents fragmentation or “Wild West” chaos.
2. Clear Governance Structure
Governance might sound dry, but it’s essential. It gives everyone—technical or not—a blueprint for how data is owned, documented, and shared.
Governance in Action:
- Contentsquare uses a hybrid ownership model: their Information Systems Department oversees system-level control, while business units retain data ownership. Ambassadors ensure compliance across departments.
- Porto labeled assets as either “Complete Governance” (full documentation, classification, quality checks) or “Simplified Governance” (basic lineage and cataloging). This allowed a five-person data team to effectively manage over 1 million data assets.
- Nasdaq evolved from centralized reporting to a federated model, with a central Platform Team, an Economic Research group, and embedded analysts in business units. Everyone operated within agreed engagement protocols.
Why It Works:
- Clear governance frameworks scale across large organizations.
- By defining how data is documented, classified, and accessed, teams can collaborate without stepping on each other’s toes.
“When governance is invisible, it’s easy to ignore. When it’s well-defined, it actually liberates teams to move faster.” – A Chief Data Officer who helped design a federal data strategy
3. Building Trust Through Standards
Standards are the rules of the road for how data should be created, named, documented, and maintained.
Kiwi.com is a standout example. They had over 100 Postgres databases with tens of thousands of tables—enough to make even the savviest analyst’s head spin! A single search for “Destination” produced 200,000+ hits. By introducing standards around ownership, documentation, quality, architecture, and security, they pivoted from simply storing data to curating 58 reliable “data products.” Each product requires:
- Technical & product-level ownership
- Comprehensive documentation
- Data quality monitoring with SLAs and SLOs
- Formal data contracts between producers and consumers
This structure cut central engineering’s workload by 53% and boosted data user satisfaction by 20%.
Why It Works:
- Clear standards eliminate guesswork, so analysts can confidently use data instead of second-guessing it.
- Consistent definitions and documentation reduce confusion.
4. Unified Discovery Layer
Nothing kills momentum faster than searching for data across multiple tools with zero context. Enter the unified discovery layer—a single “hub” to find, understand, and request access to data.
Case in Point: Nasdaq
- Teams used to bounce between four different groups to get the same answers. They sometimes reached out to all four at once, hoping someone would respond. Power users spent a third of their time deciphering existing data.
- By implementing a “Google for our data” solution (in their case, Atlan), Nasdaq gave teams one place to search for assets, see metadata, and get immediate context on usage or lineage.
Why It Works:
- Creates a self-service culture—people find what they need on their own.
- Eliminates duplication of effort and fosters collaboration.
5. Automated Governance
Governance tasks can be tedious—especially in large enterprises. Automating classification, ownership assignment, and monitoring helps data teams focus on strategic tasks.
Porto’s Story:
- A tiny governance team (five people) oversaw 1 million assets. By automating critical workflows, they cut manual work by 40%, identifying potential PII fields via pattern matching, automatically assigning ownership, and categorizing each dataset based on rules (Complete vs. Simplified).
- Freed from admin chores, they were able to tackle more value-add projects.
Why It Works:
- Automation ensures governance policies aren’t just well-intentioned but actually enforced.
- It scales with your data, letting you handle growing volumes without drowning in manual tasks.
6. Connected Tools & Processes
Finally, tying everything together. If teams can raise issues directly from their favorite BI tool—and do so with an auto-link back to the exact data asset in question—life gets simpler.
North’s Experience:
- Their data team struggled with confusion across Snowflake and Sigma. Multiple engineers would fix the same data issues independently.
- By integrating a Chrome extension into Jira and Slack, issues could be flagged right from Sigma, with instant references back to the asset. Duplicate work disappeared, and the engineering load dropped significantly.
“Eliminating duplicate work—or eliminating engineers unknowingly fixing the same problem—those efficiency gains add up fast.” – Daniel Dowdy, describing North’s transformation
Why It Works:
- Creates a seamless flow of data work across platforms and teams.
- Centralizes ticket history, so repeated issues don’t keep popping up without context.
Your Path Forward: From Framework to Implementation
Data silos are multifaceted, but very solvable when you combine people-centric culture with robust technology. Here’s the quick recap:
- Domain Empowerment: Let teams own their data, but guide them with a Center of Excellence.
- Clear Governance: Define how data is documented, classified, and accessed organization-wide.
- Standards for Trust: Establish consistent data creation, naming, and maintenance practices.
- Unified Discovery: Offer one “Google-like” hub to explore, understand, and access data.
- Automated Governance: Use technology to enforce policies without manual labor.
- Connected Workflows: Integrate your favorite tools and processes for a smooth experience.
We’ve seen these principles in action across giants like Autodesk, Contentsquare, Kiwi.com, Nasdaq, Porto, North—and beyond. Each used a variation of this 6-part playbook to tear down silos and unlock data’s full potential.
Feeling inspired? Let’s talk about how you can map this framework to your organization’s unique needs. I’d love to help you figure out the right path forward. Book a demo with our team to see how Atlan can accelerate your data-driven journey—without getting bogged down by silos.
Remember, data is everyone’s asset, not just the domain of a single department. With the right culture, processes, and tools, you can create a thriving data ecosystem that powers truly innovative insights. Book a demo with our team to see how Atlan can help you break down silos and democratize your data.