A few weeks ago, the VP of Analytics admitted to spending half of your time just tracking down the right data set before any real analysis could begin. Unfortunately, his story was not 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” are not a technical word – they are a very real and 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 various organizations on their data journeys – some have grown exponentially by democratizing their information, while others are still struggling with how to even get started. Let’s dig in.
What are data silos – and why are they so problematic?
At their core are data silos two primary causes:
- People — Departmental structures and cultural boundaries.
- Technology — Specialized tools that don’t talk to each other.
When these forces converge, data becomes locked in pockets throughout the organization. Here’s a quick look at common issues that come up:
- Time and efficiency issues: I’ve heard from teams that are spending 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 and trust issues: Multiple versions of the “same” data set appear and no one knows which is correct. Trust in metrics is plummeting. People start guessing every message, which leads to hesitation and delays.
- Scaling obstacles: As companies grow, data requirements multiply, but core data teams can’t keep up. Teams adopt brand new technologies without integration plans, fragmenting the data landscape.
- Discovering and fighting for access: Without a single “home” for data, teams can’t find what already exists. This leads to repeated confusion and lost opportunities.
- Resource and cost concerns: Silos create hidden budget drains—think redundant data storage, duplicate tools, and wasted engineering hours.
“We were constantly reinventing the wheel. It felt like every project team was spinning the same data feeds—just in slightly different ways.” – A senior data engineer we spoke with recently
Key with you: Powers aren’t just annoying. They slow down teams, erode trust, burn budgets, and ultimately limit a company’s ability to make data-driven decisions.
The Data Power Solution: A 6-Part Framework
Interestingly, the two factors that cause data silos—people and technology—also shape strategy dismount them. From my point of view, it is about building the right culture (people) in the execution of law infrastructure (technology).
To bring this to life, I’ve seen six abilities that consistently lead to success:
- Submit domains using a data center of excellence
- Create a clear management structure
- Build trust through standards
- Create a Unified Discovery Layer
- Implement automated management
- Connect tools and processes
Think of it as a dual approach—culture plus tools—that drives alignment with ownership, discovery, and collaboration.
1. Domain empowerment using a data center of excellence
In a “domain ownership” model, teams are directly responsible for their own data, while a central data group (Center of Excellence) provides the foundation, standards, and shared tools.
A real world example:
- At Autodesk, the central analytics data platform team has been overwhelmed with processing requests – more than in its entire history. By enabling 60 domain teams to manage and publish their own data products (with standardized management in place), they achieved 45 new use cases within two years. The data remained discoverable by everyone, but each domain maintained its own data sets.
Why it works:
- Domain teams become stewards of their data, increasing accountability and quality.
- Centralized guidance still prevents fragmentation or “wild west” chaos.
2. Clear management structure
Governance may sound dry, but it is necessary. It gives anyone, technical or not, a blueprint of how the data is owned, documented and shared.
Management in action:
- Content square uses a hybrid ownership model: their information systems department oversees system-level control, while business units retain ownership of the data. Ambassadors ensure compliance across departments.
- Port designated assets as either “Complete Governance” (complete documentation, classification, quality controls) or “Simplified Governance” (baseline and cataloging). This allowed a five-member data team effectively manage over 1 million data assets.
- Nasdaq has evolved from centralized reporting to a federated model with a central platform team, an economic research group and integrated analysts in business units. They all operated inside 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 frees teams to move faster.” – Chief Data Officer who helped design the federal data strategy
3. Building trust through standards
Standards are rules for how data should be created, named, documented and maintained.
Kiwi.com is a shining example. They were over 100 Postgres databases with tens of thousands of tables – enough to make even the smartest analyst’s head spin! A single “Destination” search was created. 200,000+ hits. By introducing standards for ownership, documentation, quality, architecture, and security, they changed from simplicity saving data to manage 58 reliable “data products”. Each product requires:
- Technical ownership and product-level ownership
- Comprehensive documentation
- Data quality monitoring using SLAs and SLOs
- Formal data contracts between producers and consumers
This structure reduced the workload of central engineering 53% and increased user satisfaction with data 20%.
Why it works:
- Clear standards eliminate guesswork so analysts can use data with confidence instead of guessing.
- Consistent definitions and documentation reduce confusion.
4. Unified Discovery Layer
Nothing destroys momentum faster than searching for data in multiple tools with zero context. Please enter uniform discovery layer—a single “hub” for finding, understanding and requesting access to data.
Case in point: Nasdaq
- Teams bounced between four different groups to get the same answers. Sometimes they stretched all four suddenly hoping someone will answer. Advanced users spent a third of their time decrypting existing data.
- By implementing a “Google for our data” solution (Atlan in their case), Nasdaq gave teams a single location search for assets see metadataand get instant context about usage or origin.
Why it works:
- It creates a self-service culture – people find what they need themselves.
- Eliminates duplication of effort and promotes collaboration.
5. Automated management
Administrative tasks can be tedious – especially in large enterprises. Automating classification, ownership and monitoring helps data teams focus on strategic tasks.
The story of Porto:
- It was overseen by a small management team (five people). 1 million assets. By automating critical workflows, they reduce manual work 40%identifying potential PII fields using pattern matching, automatically assigning ownership, and categorizing each data set based on rules (full vs. simplified).
- Freed from administrative duties, they were able to tackle more value-added projects.
Why it works:
- Automation ensures that management policies are not just well-intentioned, but actually enforced.
- It scales with your data, allowing you to handle growing volumes without getting bogged down in manual tasks.
6. Connected tools and processes
Finally, tie everything together. When teams can raise issues directly from their favorite BI tool—using an automatic linkback to a specific data asset—life becomes easier.
Sever’s Experience:
- Their data team was dealing with confusion across Snowflake and Sigma. Multiple technicians would independently fix the same data problems.
- Integrating Chrome extensions into Jira and Slack could flag issues straight from Sigmawith immediate links back to the asset. Duplicate work has disappeared and the technical burden has decreased significantly.
“By eliminating duplicate work—or eliminating engineers unknowingly fixing the same problem—these efficiency gains add up quickly.” – Daniel Dowdy, describing North’s transformation
Why it works:
- It creates a seamless flow of data work across platforms and teams.
- It centralizes ticket history so that recurring issues don’t pop up without context.
Your Way Forward: From Framework to Implementation
Data silos are multifaceted but very solvent when you combine a people-centric culture with robust technology. Here it is quick recap:
- Domain authorization: Let teams own their data, but lead it with a Center of Excellence.
- Clear governance: Define how data is documented, classified and accessed across the organization.
- Standards for trust: Establish consistent procedures for creating, naming, and maintaining data.
- Unified Discovery: Offer a “Google” hub to explore, understand and access data.
- Automated control: Use technology to enforce policies without manual labor.
- Connected workflows: Integrate your favorite tools and processes for a smooth operation.
We’ve seen these principles in action across giants like Autodesk, Contentsquare, Kiwi.com, Nasdaq, Porto, North – and beyond. Each of them used a variation of this 6-part guide to destroy the powers and unlock the data’s full potential.
Feeling inspired? Let’s talk about how you can map this framework to your organization’s unique needs. I would love to help you find the right way forward. Book a demo with our team to see how Atlan can accelerate your data-driven journey—without getting bogged down in silos.
Remember, the dates are everyone assets, not just the domain of one department. With the right culture, processes and tools, you can create a thriving data ecosystem that drives truly innovative insights. Book a demo with our team to see how Atlan can help you break down silos and democratize your data.