Data silos are a major roadblock to effective decision-making, forcing teams to spend more time finding and preparing data rather than analyzing it. In fact, IDC reports that data professionals dedicate nearly 80% of their time to data-related tasks, leaving little room for actual insights. In this article, we explore an actionable framework designed to encourage a more connected approach to data management, highlighting real-world examples and practical strategies.
What are data silos and their impact?
At their core, data silos emerge when information is isolated within two primary mechanisms: people and technology.
Departmental boundaries and organizational structures naturally create barriers to information sharing. Additionally, the increase of specialized tools and platforms means critical business data often remains trapped in disconnected systems.
And the impact of these silos? These are some of the common issues customers have shared with us.
- Time and Efficiency Impacts: Data silos force teams to spend days or weeks on simple data requests, and can cause widespread duplication of work as different groups unknowingly tackle the same projects. As data volumes grow, backlogs expand faster than teams are able process, creating mounting efficiency problems.
- Data Quality and Trust Issues: Multiple versions of the same data emerged across different systems, making it impossible to determine which version was accurate and up-to-date. Eroding trust in data and KPIs throughout the organization. This creates more effort to verify data and hesitation to even use it.
- Scale and Growth Problems: As companies expanded, centralized teams couldn’t keep up with increasing data demands, leading to a proliferation of uncoordinated tools and systems. Teams began operating in isolation, and new technologies were acquired without proper adoption planning, creating an increasingly fragmented data landscape.
- Discovery and Access Challenges: Valuable data assets became effectively invisible within organizations as teams had no centralized way to find or access existing data. Users remained unaware of relevant data that could help them, while data owners had no visibility into who was using their data or how it was being applied.
- Resource and Cost Implications: Organizations waste money on duplicate data storage, redundant tools, and underutilized systems while inefficiently deploying engineering resources. The hidden costs of data silos manifested in both direct expenses and opportunity costs from inefficient resource allocation.
Despite these issues, breaking down data barriers is achievable with the right strategy.
Solving for data silos requires a dual strategy with a 6-part framework
The same two factors that cause data silos to emerge, people and technology, also illuminate the strategy for dismantling them. Examining each area reveals a path forward for organizations seeking to break down data barriers.
Let’s start with the human element. At its core, the solution begins with fostering the right culture. When teams understand how data impacts business decisions and their role in owning that data, a mindset emerges where information is viewed as a shared asset rather than departmental property.
Creating this culture will require investment and organizational change. It means educating teams on data literacy, securing cross-functional commitment to collaboration, and gaining strong buy-in from leadership.
While these changes are challenging and take time to implement, the outcome is transformative: a culture where collaboration thrives because everyone shares and has access to the data they need to make business decisions.
Addressing technology is just as critical as tackling organizational silos. In the modern data landscape, information assets are often scattered across various systems and platforms. Sales teams may rely on Salesforce, while marketing teams use HubSpot. Data can be spread across multiple cloud providers like Azure and AWS, and even dispersed globally.
Without proper management, this diversity of tools can create chaos. Effective data management requires more than just connecting disparate systems. It demands a comprehensive approach that ensures data quality, maintains consistency, and preserves vital context. This means implementing processes and governance to ensure data is accurate, up-to-date, and adheres to organizational standards. It involves establishing clear ownership and accountability for each dataset. Crucially, it also necessitates capturing and sharing critical metadata that provides insight into the data’s origins, meaning, and lineage. When done right, it enables teams across the organization to understand, trust, and effectively utilize the information they discover.
While there’s no universal checklist for solving data silos, we’ve developed a comprehensive 6-part framework—a powerful, adaptable roadmap that organizations can implement and master. The success stories of visionary companies like Autodesk, ContentSquare, Kiwi.com, Nasdaq, Porto, and North support this approach and showcase how the framework can navigate unique challenges and democratize data across different organizational contexts.
1. Domain Empowerment with a Data Center of Excellence
Empower teams to be directly responsible for their data through a strong centralized data team that establishes the foundation, standards, and tools for trusted data products.
At Autodesk, by early 2021, their central Analytics Data Platform team faced an overwhelming backlog of data ingestion requests that exceeded all previously ingested data in the platform’s history. They solved this challenge by implementing a collaborative domain ownership model, one where teams were empowered to publish and manage their data products while working within a shared governance framework. This ensured interoperability and accessibility across the organization. Their platform team established common standards, tools, and processes that allowed 60 domain teams to independently build data products while making them discoverable and usable by others through a central catalog. This balanced approach led to the successful delivery of 45 new use cases in just two years while ensuring data remained accessible and valuable across team boundaries.
2. Clear Governance Structure
Provide the framework for how data is managed, documented, and shared across an organization.
Leading companies have implemented diverse yet effective governance frameworks to manage their data assets:
- Contentsquare implemented a hybrid ownership model where their Information Systems Department (ISD) maintained system-level control while business units retained data ownership, complemented by data compliance ambassadors embedded across departments to ensure consistent standards during acquisitions.
- Porto established a tiered governance model, designating assets as either “Complete Governance” (requiring full documentation, classification, quality checks, and defined ownership) or “Simplified Governance” (needing only basic lineage and cataloging), allowing their small five-person team to efficiently manage over 1 million data assets.
- At Nasdaq, they evolved from a centralized reporting structure to a federated model with specialized teams. The Platform Team maintained core technology, an Economic Research team focused on data science, and embedded analysts established within business units, all operating within defined engagement protocols.
- Kiwi.com took a product-centric approach to governance, covering ownership, documentation, quality, architecture, security, and processes, which helped them reduce engineering workload by 53% while increasing data user satisfaction by 20%.
These varied approaches showcase that effective governance structures can take different forms while achieving the same goal: creating clear, scalable frameworks for managing and sharing data across complex organizations.
3. Building Trust Through Standards
Establish consistent practices for how data is created, maintained, and shared within an organization.
Kiwi.com’s vast data landscape of 100 Postgres databases and tens of thousands of tables resulted in overwhelming search results for analysts, with simple terms like “Destination” returning over 200,000 entries. They tackled this by developing a governance framework for data products and establishing standards around ownership, documentation, quality, architecture, security, and processes. This transformed their approach from simply storing data to curating 58 carefully managed, top-tier data products that met strict criteria for reliability and accessibility. Each data product now requires technical and product-level ownership, comprehensive documentation, quality monitoring through their observability platform, and formal data contracts specifying SLAs and SLOs between producers and consumers. Their central engineering team saw a 53% reduction in workload as teams became more self-sufficient, data user satisfaction increased by 20%, and they successfully onboarded over 20 teams to responsibly share and use data across the organization. This standardized approach not only improved efficiency but also built confidence in their data, enabling teams to make faster, more informed decisions with reliable data products.
4. Unified Discovery Layer
Provide a single place to find, understand, and access data across an organization.
At Nasdaq, teams would go to multiple groups simultaneously to get data answers, with Michael Weiss noting “If you were a business unit, you could go to one of four teams to try and get an answer. As a matter of fact, they would go to all four teams to see who would win first.” Their survey revealed that power users spent one-third of their time just trying to understand the context around data they already had access to. They transformed this by implementing Atlan as their unified metadata layer, which users described as “having Google for our data.” This allowed teams to quickly discover existing data assets, understand their context, and ask the right questions, rather than duplicating work across multiple teams.
5. Automated Governance
Use technology to consistently apply data management rules and policies at scale.
At Porto, a five-person data governance team was manually managing over 1 million data assets, spending significant time on tedious tasks like updating classifications and ownership in spreadsheets. They transformed this by implementing Atlan to automate their governance processes. The automation classified assets as either “Complete” or “Simplified” governance based on preset rules, automatically assigned ownership based on data domains, and identified potential PII fields through pattern matching. This resulted in a 40% reduction in time spent on manual governance tasks, allowing the team to focus on more strategic work. As Danrlei Alves noted, “With the time we saved, we are increasing the scope of work we have, and projects we do. For the manual activities we still have, we’re finding a way to automate them, too.”
6. Connected Tools & Processes
Create a seamless flow of data work across different platforms and teams.
At North, their data team was struggling with inefficient issue resolution and duplicate work across their Snowflake and Sigma environments. Team members would receive tickets without clear references to specific data assets, often leading to multiple engineers unknowingly working on the same issues. By integrating Atlan’s Chrome Extension with Jira and Slack, they enabled users to raise issues directly from Sigma with automatic hyperlinks to specific assets. As Daniel Dowdy explained, “Eliminating duplicate work, or eliminating your engineers working on the same project without knowing about it? These efficiency gains are huge, and the savings add up really quickly.” The integration also provided engineers with a comprehensive history of all tickets and conversations relating to specific data assets, helping them better understand and prevent similar past issues.
Your Path Forward: From Framework to Implementation
The challenges posed by data silos are multi-faceted, but solvable through a strategic, coordinated approach. As we’ve seen, siloed data stems from two core issues:
- Fragmented ownership and accountability
- Lack of unified processes and tooling for discovery, governance and access
To systematically break these barriers down, organizations should focus on building six key capabilities:
- Establish clear domain ownership to empower teams
- Implement strong governance structures and processes
- Build trust through consistent standards and data contracts
- Create unified discovery and exploration layers
- Automate governance tasks to ensure scalability
- Connect tools and workflows to enable seamless collaboration
The power of this framework has been proven out by pioneering data organizations. North American Bancard struggled with scattered data and manual governance holding them back—applying automated governance with Atlan drove a 700% increase in managed data assets. Autodesk dealt with data trapped in team silos, but by rolling out standardized processes and a central discovery layer, they broke barriers between domains. Contentsquare lacked reliable access to trusted data, through structuring governance and ownership, they unlocked fluid, secure data sharing.
Each of these successes validates how a systematic approach to dismantling data silos can accelerate an organization’s progress toward becoming truly data-driven. While the journey takes commitment, the destination is clear: empowered teams seamlessly accessing and enriching a shared pool of trusted data to drive innovation.
Ready to explore how your organization can operationalize this framework to achieve data-driven transformation? We’d love to help you map out the right path forward. Book a demo with our team to see how Atlan can help you break down silos and democratize your data.
