You’ve got the tools, but the numbers still don’t match.

The problem isn’t the data warehouse or BI layer; it starts further upstream. Data silos trap valuable insights across disconnected tools and teams, stitched together with manual scripts. And things only get worse as the stack scales.

So, what can you do to break down data silos for good? Read on to learn how these silos form and the technical solutions you can employ to remove them.

Why do data silos form?

Data silos usually form when different teams or departments build the reports and data pipelines they need using the tools and sources available at the time.

As organizations scale, the result is disparate tools and data with inconsistent sources, formats, and schemas.

As engineers build bespoke pipelines to bridge the gaps between these enterprise systems, teams, and processes, they have less time to focus on a solution to the core problem: integration.

Over time, the problem only compounds, and data silos widen and deepen.

Regarding business impact, it also forces teams to make business decisions with outdated or incomplete data. Execution times slow down, and the organization struggles to pivot quickly. In fact, 81% of IT leaders say data silos are hindering their digital transformation efforts.

But even this is only part of the story. Let’s examine the core drivers behind data silos in enterprise organizations.

Where and how data gets stuck

Data silos don’t have a single cause or source. They form when operational routines, governance constraints, and infrastructure limitations prevent data from flowing freely across teams.

Apart from core company-wide systems — like SharePoint, email, or a project management platform — individual business units tend to have unique needs and processes. They often onboard SaaS products and purpose-built solutions to plug these gaps and create their internal efficiencies and reporting.

Here are some of the ways different organizational layers can contribute to these data fragmentation issues.

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Spotting silos: Mapping people, processes, and systems

The discovery process involves auditing people, processes, and technology to identify where data fragments live within an organization. 82% of enterprises report that data silos disrupt their critical workflows, and this is usually where these issues make themselves known.

The goal is to build a complete inventory of data sets, how they interact, and who uses them.

• Inventory systems across departments:

Catalog everything that generates, stores, or processes data — including all SaaS applications, cloud storage, and shadow IT. Shadow IT refers to the unofficial tools adopted by individual teams and team members for some aspect of their job, which leaves critical data unaccounted for and unprotected.

• Clarify ownership and usage:

For each dataset, document the designated data owner and anyone who contributes, edits, or consumes the data. Understanding the full footprint now helps identify untracked dependencies and potential bottlenecks later.

• Audit usage patterns and lineage:
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  • How often is a query or update made?
  • How frequently is the data refreshed?
  • Can you document the lineage of a dataset between tools and processes?

Reviewing the systems in place and understanding what activities teams perform manually, where, and when helps identify duplication points and where inconsistencies most likely originate.

Case study: Supply chain transformation

Redwood Logistics uncovered critical bottlenecks with data siloed between warehouses, spreadsheets, and on-prem systems. After frequent data pipeline failures and reporting delays, there was a clear need to modernize its architecture.

By automating data integration, they were able to build key data connectors in just 2 weeks, a process that would have taken them 6 times longer previously.

Read more: Redwood Logistics transforms the supply chain with Fivetran and Snowflake

Integrate your data: Automating ELT into a single source of truth

One of the main reasons manual pipelines are vulnerable to breakages is changes to source schemas. Engineers often scramble to rewrite code and fix the breakage when columns are added, removed, or renamed.

Modern Extract, Transform, Load (ETL) systems automate manual pipeline management through fully managed connectors. They leverage schema drift handling and change-data capture (CDC) features to keep data flowing, even through upstream system changes.

These tools automate the process of extracting data from different data sources and loading it into your warehouse. Once loaded, they handle data transformations at scale, drastically reducing engineering overhead.

Case study: Migrating to the cloud

After 8 months of manually moving SQL Server data to Azure, the cloud migration project at Oldcastle Infrastructure stalled. Problems with manual processing and scalability led the team to adopt automated ELT to address the root problems.

Through this new data stack, they could replicate all data sources in only 10 business days, saving an estimated $360,000 in setup and maintenance costs. The end result was harmonized data that automatically refreshes every 3 hours for faster reporting and improved decision-making.

Read more: Oldcastle Infrastructure Sees Multiple Millions of Dollars in ROI with a Modern Data Stack | Case Study

Govern unified data: Quality, security, and access controls

With data now centralized, the next priority becomes governance and data integrity. Data must stay trustworthy and secure to remain functional (and compliant). Poor data practices cost organizations 12% of revenue annually, primarily due to rework and regulatory compliance penalties.

Data governance protocols ensure that stakeholders can reliably use the data flowing into their dashboards and processes.

One of the best ways to handle this is by implementing automated data quality checks. You can use tools like dbt tests or build validation features into the data warehouse. Either way, the goal is to flag things like schema changes or missing values before they affect end users and send engineers on an investigation mission to find the issue.

Data security protocols can include role-based access controls (RBAC) and column-level security to limit access to sensitive data.

For example, customer support teams can view a transaction record with specific details redacted for data privacy, while accounts receivable need to see the full record. Another way to manage unauthorized access is encryption, both at rest and in transit.

Data from 2024 showed that only 14% of business technology practitioners have operational tools in place to manage data quality processes like profiling, parsing, standardization, and merging. But implementing data governance tools that continuously monitor data freshness means anomalies can quickly be surfaced through alerts and addressed.

Case study: Data-driven decisions at scale

The SKIMS brand was scaling rapidly. With that growth, data quickly became fragmented across marketing and operations platforms, making eCommerce data difficult to leverage correctly.

The team used automated ELT to consolidate over 60 pipelines into one centralized warehouse, which now feeds tools like dbt and Looker. Now, eCommerce data automatically refreshes every 15 minutes, so teams get close to real-time data across the business.

Read more: How Fivetran powers SKIMS’ fashion empire | Case study

Operationalize data and activate insights

Once data is clean and secure and pipelines are appropriately managed, the next step is operationalizing the architecture for the rest of the business.

Self-service BI provides teams with curated datasets using semantic layers or dbt. Instead of waiting for weekly pulls, teams can run ad-hoc queries or build their own dashboards.

Centralized, clean historical data also means data science teams can better train artificial intelligence and machine learning models. Again, consistent and clean data ensures the output from these models is more accurate, whether predicting customer churn or refining product recommendations.

Where teams would have had to spend extensive amounts of time on data preparation for this purpose, unified pipelines reduce that overhead and give AI models much better input.

The third aspect of data operationalization is reverse ETL. This is when tools push clean, fresh data from the now unified data warehouse back into operational systems, so teams can rely on the data within the tools they use daily.

For example, the marketing team can get more reliable customer scores within their CRM to trigger specific campaigns, and the sales team can similarly prioritize lead follow-up based on real-time engagement data.

Case study: Centralizing data for growth

air up® was facing challenges as a result of multiple spreadsheet usage and siloes created from platforms like Shopify.

The team first centralized the data through automated ELT pipelines and then added self-service tools into the stack, like Veezoo.

Read more: air up® centralizes data for growth | Case study | Fivetran

Measuring success: KPIs for a unified data platform

There are several metrics you can track to understand how effectively data silos are being broken down.

First, monthly pipeline maintenance hours are a great place to start. Measuring the drop in hours spent on maintenance shows the time and cost savings on engineering overhead.

Another metric is data freshness lag — the time spent between updates to source data and when it becomes available in the warehouse. Lower data freshness lags indicate pipeline health and the speed of reporting cycles.

Here are a few more KPIs that signal silo breakdown:

Common pitfalls when breaking data silos (and how to avoid them)

Even with a clear project plan and defined steps like discovery, integration, governance, and operationalization, breaking down data silos is a complex task. There are many potential hiccups along the way that can undermine progress.

Unclear data ownership, for example, can result in stale, inconsistent data. That’s why data ownership is necessary during the discovery and governance stages.

Another common area where teams run into issues is hand-coded pipelines. In the moment, this is a solid quick fix. Ultimately, however, it becomes a huge drag on engineering time when schema changes or other issues occur. That’s why using fully managed ELT connectors, which can automatically handle schema drift, is preferable.

Downstream, even when data is fully centralized, BI specialists may continue to gatekeep reporting. Self-service analytics, using semantic layers and dbt, should be operationalized for all users so they can access and leverage it independently.

Turning silos into unified data that provides actual business value

Breaking down data silos is about more than implementing a few fixes for the engineering team. When clean data is available for analysts and end users, it can provide real-time insights that directly impact business outcomes.

But this type of data transformation requires careful planning and a phased approach to ensure success. By following the process from discovery through to integration and governance, you’ll drastically reduce engineering overhead and save hours, both in the short and long term.

Looking for more ways to scale and automate data pipelines? Check out more modern data stack resources.

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