Undoubtedly, most organizations today do not suffer from a lack of data. If anything, they’re overwhelmed by it and struggle to use it effectively.

Data typically lives in multiple tools owned by different teams and is formatted differently across the stack. The result? Data silos, and lots of them.

The inability to bridge silos and form accurate, easy-to-access insights is a huge problem for organizations.

Before you can fix the problem, you need to understand it. This article explains what data silos are, why they form, and how to spot them in your organization.

What is a data silo?

A “data silo” refers to the isolation of a dataset, or multiple datasets, from other systems within an organization. The silos occur because the data lives within a system or tool that cannot integrate with other tools or datasets due to differences in formatting or schema, or a lack of integration endpoints.

Data silos also lead to:

• Difficulty leveraging data management to build accurate, complete pictures of performance
• Delays in teams’ responsiveness due to a lack of real-time insights
• Blocking tech-forward, strategic initiatives such as AI or machine learning, which rely on clean, unified datasets for training and running predictive analytics models
• Wasted resources and engineering hours.

For instance, the marketing team uses a SaaS application to manage advertising spend. However, finance does not use this tool and does not provide native integrations with its accounting systems. The result is a data silo that leads to marketing manually exporting data and finance reconciling it with the data from their system.

Before teams can solve for fragmented data, they need to understand why it happens in the first place.

4 common myths about data silos

Here are 4 myths that reinforce the value of the endeavor but also continue to prevent data and engineering teams from fully realizing it.

Small and medium-sized companies accumulate SaaS apps and disparate databases just like large enterprises. A small business, especially a heavily digital business like an eCommerce store, also collects and stores huge amounts of data across these systems.

Central data integration strategies can unlock huge value and efficiencies for SMBs through faster, more reliable business reporting and cross-functional collaboration.
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Even with cloud technology, data silos can persist unless the data and engineering team prioritize automated data ingestion and standardize schemas.

Alongside user challenges like governance and ownership, data can remain siloed in the cloud just like an on-prem database.
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Systems and data may exist in silos, but the tools are only part of the problem. People update, store, move, and merge data.

When there is a lack of ownership and unclear governance structures, it serves to increase and amplify data silo problems as errors continue and records become stale.
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AI doesn’t fix data, it consumes it.

Centralizing data into a single cloud data warehouse is only 1 among many steps in building AI readiness. Completeness and quality are essential for training and implementing AI and machine learning models.

Poor-quality, incomplete, or inconsistent data makes AI output, like predictive analytics, unreliable. That’s why 46% of enterprise AI initiatives fail due to poor data readiness, and 29% say data silos are blocking AI success.

Why data silos happen in modern companies

From growing tech stacks to legacy system issues and unclear ownership, several common patterns lead to siloed data.

Tech stack complexity

How a sales team tracks and monitors their day-to-day performance requires very different tools from the accounts payable team tracking and paying invoices.

But in a world with higher numbers of SaaS apps available every year (58,000+ and 11.6% growth from the previous year as of 2024), tech stacks are more complicated and leave ever more room for data silos to form.

Mergers and acquisitions

Engineers tasked with unifying the data architectures of newly merged entities often have to untangle completely separate operating systems and stacks.

For instance, 1 entity might rely on an on-prem ERP with tightly bound schemas, while the other takes an API-first approach through SaaS solutions.

Unless there’s a clear integration plan in place from the start, these parallel systems continue to demand heavy lifts from the engineering team to manage pipelines.

Legacy systems

Many companies invested heavily in on-prem databases and proprietary ERP platforms in the past.

But now, the organization is using multiple cloud-native tools and legacy systems with denormalized data models and proprietary protocols make it difficult to expose functionality through modern APIs.

Moving data from legacy systems into cloud-native tools often requires engineers to rely on batch exports or middleware.  

Data security and governance gaps

Proper data security, governance, and ownership of datasets increases the frequency of:

• Quality checks
• Data refreshes
• Access control monitoring

Without accountability for quality and governance, teams apply conflicting logic and have to rely on stale or inaccurate data.

This cause of data silos, among the others on the list, leads to only 13% of companies reporting the ability to derive value from newly collected data in minutes or hours. For 76%, it takes days or weeks for data to become a usable, valuable source.

Tinuiti, for example, overcame these silos with a centralized, automated data lake and unified pipelines to synchronize daily updates.

The real cost of data silos

The true cost of data silos affects technical teams and the wider business stakeholders they serve.

Silos cause duplicated pipelines and duplicated work for engineering and data teams.

When schema changes break integrations, teams spend precious time on manual reconciliations and frequent troubleshooting.

In fact, 80% of data leaders say they at least sometimes have to rebuild data pipelines after deployment, and 39% of companies say it happens often or all the time.

Then there’s poor quality of data, often the result of silos in combination with poor governance.

In KPMG’s 2023 AI Risk Survey Report, businesses cited data integrity issues as their biggest challenge when training AI models, even more so than statistical validity or model accuracy.

Companies can solve these data silos by automating data ingestion into a centralized warehouse or data lake before applying standardized transformation logic.

It’s a route that Scape, a student accommodation provider in Australia, took to remove redundancies in their reporting processes. As a result, they reduced maintenance hours for the engineering team and dramatically improved decision-making speed for the organization. Overall, they increased data processing times by 90% and reduced engineering costs by 50%.

Examples of data silos in action

LVMH broke down data silos across 75 brands for unified data and accurate reporting

LVMH consists of a portfolio of 75 luxury brands, including Louis Vuitton, Moët Hennessy, and Dior. Each brand was using different systems across SAP, marketing, and supply chain data management. Consolidated reporting for the brands was both slow and inconsistent due to the silos these separate systems created.

The engineering team was dealing with different data models refreshing at different cadences, forcing teams to use manual extracts and engineers to maintain homespun, resource-heavy pipelines.  

To remove these silos, LVMH looked for an integration solution that would lift the engineering load and enable each brand to access critical business insights consistently.  

They chose a solution that enabled them to automate data ingestion from multiple disparate marketing, operational, and financial systems into Google BigQuery. The automation implemented a harmonized schema and a single analytics layer to give the business standardized, real-time insights.

Interflora made data a strategic cornerstone to better leverage data from its millions of annual online orders

After a surge in online orders, Interflora recognized the valuable potential of the business and customer data they were collecting. However, that data was scattered across multiple systems, and the silos led to high error rates as the team attempted to export and wrangle the siloed data.

The backend team was responsible for supplying and verifying data and maintaining business reports. They decided to onboard a dedicated data team to tackle the challenges, which quickly sourced a solution for centralizing data across the organization’s key systems.

It enabled the team to create over 200 BI reports so teams across borders can leverage shared KPIs to drive better business decisions while maintaining compliance.

The new centralized data infrastructure has even set the stage for Interflora to deploy a data catalog in preparation for digital transformation through machine learning and an AI platform.

Deliveroo centralized and unified data across marketing, HR, and finance platforms to save engineers hundreds of hours per week

Deliveroo built internal pipelines to handle cross-functional data and enable business collaboration. However, the pipelines were complex to maintain and frequently broke down due to upstream schema changes.

These manual pipelines caused silos between business departments and inaccuracies in shared metrics. The team shifted gear to a full automated ELT process to consolidate customer data across marketing, HR, and finance.

Now, everything lives in a centralized data warehouse with harmonized schemas, and engineers spend 100 hours less per month on maintenance.

What a unified data stack looks like

A complete data stack first leverages automated ELT pipelines to handle data ingestion from every source into a centralized warehouse or lakehouse.

Then, a transformation layer like dbt applies standardized schemas and automates data management according to business logic, something only 14% of organizations have currently implemented tools to handle.

Some modern pipelines then use reverse ELT to push cleaned datasets back into business operating systems for 360-degree accuracy and consistency across all of the organization.

Finally, self-service BI tools can now tap into well-governed data that is ready for analytics and consistent across the organization. This democratization of data enables faster decision-making from more accurate data and reduces the burden on data and engineering teams for ad-hoc builds and manual pipeline fixing.

The team at Dropbox uses a similar stack to unify data as they move through mergers and acquisitions to ensure they can integrate data from companies they acquire. They have saved the equivalent of 3 full-time engineers and cut data ingestion to reporting time from 8 weeks to 30 minutes.

Build smarter workflows with Fivetran

Data silos create stalls and bottlenecks throughout your workflows, from manual pipeline wrangling to stalled insights for data end users. Identifying the root causes of silos in your organization allows technical teams to take actionable steps to break them down.

A unified stack is crucial for ensuring that data supports growth through real-time decision-making and that data operations can scale alongside that growth.

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