Data silos: Risks, causes, and how to break them down

We’ve been talking about the challenges of data silos for decades now, but the problem remains. In fact, it’s growing. The massive amount of data being gathered today, along with advancements in AI, has compounded the issue. In fact, 40% of organizations surveyed say they are struggling with data silos, which have hurt their efforts to become more data-driven.

This is a major issue, especially as 82% of enterprises say that data silos plague critical workflows. In some cases, as much as 68% of data goes unanalyzed. In other words, your data analysis may not be using the majority of the data you gather. Data silos aren’t just a technical inefficiency; they impact the effectiveness of data platforms. They are actively blocking business intelligence and undermining AI adoption.

Main takeaways from this article:

• Data silos are a growing threat.
• Unifying data storage into a central system like a data warehouse or data lake is crucial to business intelligence and automation.
• Reverse ETL pipelines allow teams to activate centralized data in operational tools, reducing friction and duplication.
• Data governance, consistent data management, shared ownership, and cross-functional alignment are essential for long-term success.
• Tools like RudderStack make it easier to integrate, move, and activate data across your ecosystem.

What are data silos?

Data silos occur when different teams or multiple systems store data separately, making it difficult or impossible to combine and analyze comprehensively. A few examples include:

• Separate databases for sales, marketing, product, and support
• Cloud SaaS tools that don’t communicate with each other
• Teams that collect and define metrics independently
• Fragmented tracking implementations where user behaviors are inconsistently recorded

The results?

Incomplete data creates critical blind spots in your business intelligence and data analytics.

Why do data silos occur?

Legacy software, poor data governance, organizational structure—you name it. There are plenty of reasons data silos exist. Without stringent policies and governance over data collection, storage, and use, silos will only grow.

Team-level tooling decisions

When teams independently adopt tools without coordinating on data strategy, it creates tool sprawl.

Example: Marketing and product tracking misalignment. Marketing teams use a CRM to track leads and attribute campaigns, while the product team relies on a different platform to track in-app behavior. Since the tools don’t share identifiers or event definitions, it’s impossible to connect the campaign source to product activation.

This is a more common problem than you might think. Just 8% of organizations report strong alignment across departments.

Legacy systems and slow migrations

Older data systems often lack modern integration capabilities. Yet many organizations are reluctant to replace them due to cost or risk. This delays efforts to consolidate data, such as moving data into centralized platforms like data lakes.

Example: CRM data stuck in a legacy system. A company’s sales team uses a 10-year-old on-prem CRM that can’t easily export data or connect to its modern data warehouse. Because migrating is seen as too complex, sales metrics remain siloed and are only accessible through monthly manual reports.

Poor or absent data infrastructure

Without a clear data integration strategy, teams may rely on ad hoc solutions, such as manual exports, custom scripts, or other departments' specific databases. This makes data movement slow, unreliable, and hard to scale.

Example: Analytics teams building one-off pipelines. The analytics team spends weeks building custom ETL scripts to extract data from different tools for each new campaign or product release. These pipelines are hard to reuse, leading to delays and frequent outages when data sets change.

Organizational silos and lack of ownership

When departments operate in isolation, data suffers. A lack of shared accountability for data infrastructure leads to fragmentation, inconsistent standards, and misaligned priorities.

Example: No clear owner of customer data. The customer success team wants churn risk scores, but no one knows who owns the customer data needed to calculate them. Product teams own in-app events, support owns ticket data, and marketing owns lifecycle emails.

Without a centralized owner, no one feels responsible for integrating or maintaining a unified customer view.

The risks of siloed data

Siloed data sources can produce massive financial implications. IDC estimates that siloed or incorrect data can cost a company up to 30% of its annual revenue. Gartner puts the price tag of poor data quality at nearly $13 billion a year.

Siloed data also produces:

• Poor visibility and reporting: Without a single source of truth, reporting becomes fragmented. Teams disagree on KPIs, and leadership lacks a holistic view of performance.
• Inconsistent insights: Siloed data leads to conflicting narratives. Other departments might come to conflicting conclusions based on their limited data.
• Inefficient business operations: Duplication of work and redundant tooling drain resources. Teams waste time reconciling data or replicating tasks others have already done. It also hinders opportunities for automation.
• Increased compliance risk: Siloed systems make data governance and data security difficult. It’s harder to manage consent, enforce retention policies, or prove compliance.

What’s more, a whopping 70% of organizations operating with data silos suffered a data breach within a two-year span.

Data security becomes more challenging as your data expands. Protecting all of your endpoints and data locations requires a comprehensive (and unified) solution.

How to identify data silos

Data silos are insidious and rooting them out requires focused effort. Here’s where to start:

• Inventory your data sources and tools: Conduct a full audit of all data sources and systems.
• Look for inconsistent metrics between teams: Identify discrepancies in metrics and definitions across departments.
• Check access and ownership: Assess who has access to what data and whether that access is consistent.
• Look for manual workarounds: Note any workarounds—like CSV exports or manual reporting—that signal underlying silos.

Data silos typically form in a few common places: Take a close look at department-specific applications, such as HR, sales, or finance, and how they integrate within your overall tech stack. You will also want to examine how departments use any spreadsheets stored locally or not connected to your data warehouse.

Legacy systems are a common source of data silos. Structured data and unstructured data, outdated data, and duplicate data can cause significant problems with a centralized data storage solution, especially if they don’t integrate seamlessly with your newer platforms. So are cloud services and cloud storage solutions, especially from third-party vendors, that may isolate data within SaaS platforms.

Another area to review is when you have users in a different business unit or geographical location. There may be differences in how data assets are handled, depending on this location.

How to break down data silos

Solving company data silos is as much about mindset and ownership as it is about technology to enable data-driven decisions. You need a comprehensive approach to breaking down data silos and a commitment from top leadership to make it happen. You also need the resources to make it happen, which requires an investment.

Let’s break the process down into steps.

1. Adopt a centralized event streaming infrastructure

At the core of any integration strategy is a central source of truth, such as an integrated data warehouse or data lake. Streaming real-time data from all sources into one place enables unified analytics, data science, and AI workflows.

A central data repository ensures consistent schemas, scalable storage, and better control over data movement, mitigating areas where data silos limit integration.

2. Standardize tracking and data collection

Disjointed tracking is one of the fastest ways to create silos. Implement clear naming conventions, definitions, and tracking plans so data across tools is consistent and compatible.

Unified tracking enables identity resolution and improves attribution, helping users to trust the data.

3. Align on data governance early

Governance cannot be an afterthought. It is foundational to eliminating data silos and creating trustworthy data across your organization. To unlock value, you need this foundation to build data maturity and data integrity to centralize and streamline delivery across applications and teams.

Here are three key areas where early governance matters most:

Data access and permissioning

Set clear policies on who can access what data, at what granularity, and under what conditions. Not everyone needs access to raw personally identifiable information (PII) or sensitive data. Use role-based access control (RBAC) to enforce this consistently across tools like your data warehouse or BI platform.

Consent management and compliance

With regulations like GDPR, CCPA, and others, user consent and data privacy are essential. You need to track when and how consent was given and enforce data usage policies accordingly. Your integration pipeline should support consent flags and carry them downstream to marketing tools.

Data retention and archival

It’s just as important to set rules for data retention as it is for data gathering. Retention policies help limit liability, reduce storage costs, and ensure data freshness. Use automated solutions to enforce data cleanup in your data warehouse or data lake environments.

4. Invest in data integration flexibility

Your data integration strategy should empower both technical and non-technical users. This means:

• Connecting to all relevant data sources (SaaS tools, apps, CRMs)
• Ensuring bi-directional data flow
• Supporting batch and real-time syncs

The ability to move data freely, especially into and out of warehouses and lakes, is essential to eliminating data silos and ensuring everyone is working off of the most currently available data.

5. Involve cross-functional data stakeholders

Sales, marketing, product, engineering, compliance teams, and data scientists—everyone involved in the collection, processing, and use of data—need to be heard when shaping your strategy. Cross-functional collaboration builds trust and ensures long-term alignment.

6. Enable reverse data flows (reverse ETL) for business data pipelines

A reverse ETL pipeline pushes enriched data from your warehouse back into operational tools. This helps to ensure data works seamlessly across your tech stack, but requires careful attention to detail.

Examples include:

• Converting lead data from Salesforce into a format that works across marketing automation.
• Updating master customer profiles in your customer relationship management platform by combining data from multiple sources.
• Converting transaction data into machine learning training sets.

7. Make data discoverable and usable

If your data is stuck somewhere or you can’t find it, it’s not helping you meet your goals. Your policies and systems need to help teams find, trust, and understand the data available to them. This includes:

• Metadata tagging and documentation
• Data catalogs or internal wikis
• Role-based search and discovery

8. Foster a data-sharing company culture

Research shows that 85% of IT leaders say data silos are hindering their digital transformation efforts, preventing them from fully leveraging AI tools. Without a culture that prioritizes data governance and shared responsibility, you are unlikely to create the change you want.

You need cross-functional collaboration, open communication, and leadership from the top ranks to create the culture you need.

How RudderStack helps teams eliminate data silos

RudderStack is purpose-built to eliminate data silos, producing data you can trust.

By routing data directly to your existing data warehouse or data lake, you stay in control of your architecture and ensure compliance. Stream unification collects data from apps, websites, and devices through a single pipeline, and built-in governance tools manage identity, permissions, and data transformation.

Reverse ETL pipelines push clean, enriched data back into operational tools, empowering marketing, sales, and customer success. This ensures unified data and workflows, eliminating fragmented data.

Break down silos and simplify data management with RudderStack

Breaking down data silos is essential for growth, compliance, and innovation. The volume and velocity of business data today demand a smarter, more unified approach to data integration.

By investing in infrastructure that supports centralized data collection, movement, and activation, organizations can:

• Improve visibility and reporting
• Accelerate automation and personalization
• Strengthen governance and compliance
• Enable AI-driven insights
• Future-proof their business

Siloed data undermines all of these efforts. RudderStack simplifies data collection and integration with code-level control where you need it, working across your entire data environment:

• Collecting: Capturing standardized customer event data from every source and managing integrations in a central platform.
• Transforming: Modifying payloads in flight to ensure your data is activation-ready when it gets downstream.
• Delivering: Enabling data in all of the tools and systems your company uses to drive growth.
• Governing: Implementing compliance and data quality measures in the pipeline so downstream teams can use data with confidence.

RudderStack offers the infrastructure, flexibility, and governance tools needed to connect your entire data ecosystem. Try RudderStack for free or schedule a quick demo to see how it can help your team break down data silos and unlock real-time, unified customer insights at scale.