Data Silos Introduction: What Are They and Why Are They Problematic?

Data silos are collections of data, often managed by different departments or groups, and not available outside of those groups. Think of data silos as islands of information. Because they create barriers to information sharing, data silos make work harder for everyone. The good news is that you can take steps to break down silos, improve the health of your data ecosystem, and speed up access to your data for better, faster decisions and smoother operations.

A data silo is data isolated from and not accessible to other parts of your organization. For example, finance, marketing, HR, and other departments may need different data to do their work. Over time, they grow and maintain their own systems. But data isn’t shared and is often duplicated and inconsistent. Additionally, IT teams often store critical enterprise data securely in data lakes or warehouses, but that data may only be accessible to technical teams as opposed to the broader business.

Data silos can hold back your digital transformation; you won’t be able to scale processes easily, your operations will be riddled with inefficiencies, and collaboration between departments will be difficult. In our example, if marketing runs a successful campaign but the financial system isn’t updated with accurate sales data, the company can’t report accurate financial results at the end of the quarter.

Data silos often develop as your organization grows and adopts new systems or processes. Having separate systems by department was also common practice before the cloud or systems such as customer data platforms became pervasive.

The average enterprise runs on nearly 900 applications , and only one-third are integrated. The challenge lies in the complex data strategies companies are forced to employ. Those who have attempted to organize their data often develop massive and complex data ecosystems across data lakes, data lake houses, and data warehouses. While successful in centralizing data from an array of sources, these ecosystems are difficult to operationalize as the data remains static and stuck in back-end solutions not designed to activate data within the everyday workflows and applications business teams use to engage with their customers. They fall short at the “last mile” of activation, leading to poor customer experiences driven by disconnected or unavailable data.

Data silos often arise from organizational structures where departments operate with separate goals, KPIs, or priorities. When each of your company’s departments manages its own system or data to meet specific objectives, cross-departmental sharing becomes an afterthought. If you lack unified data governance, you may find inconsistencies in how data is collected, stored, and accessed. Office politics and resistance to change also play a significant role, and departments that don’t collaborate may be reluctant to share.

Legacy systems and incompatible technologies ‌commonly lead to data silos. These older tools and platforms often lack the necessary features and standards to seamlessly integrate with modern systems, leading to isolated pockets of data that are difficult to access or combine. When new tools or platforms are introduced, they may not be designed to work with the existing infrastructure, further complicating the issue. As a result, data becomes trapped within specific departments or systems, making it challenging to establish a unified data environment.

Poor communication between your departments can also create data silos. When departments lack shared goals or fail to establish open channels for collaboration, data sharing tends to be overlooked. If you have few shared tools or minimal standardization protocols, you could also be limiting data connectivity.

Mergers and acquisition frequently introduce new data silos by bringing disparate systems and data formats into the combined organization. Data cleaning, standardization, and meticulous integration can address these challenges. However, these essential efforts often get delayed during the M&A process, or the integration obstacles turn out to be more complex than anticipated. In the end, different teams within the new organization often work around the data silos, even though they lead to inefficiencies.

Data silos can harm your business. These are some of the ways they hold you back.

Based on Salesforce’s “State of the Connected Customer” report, 76% of customers expect consistent interactions across departments. However, 54% say it generally feels like sales, service, and marketing don’t share information.

A 360-degree view of your customers gives everyone in your organization access to the same, accurate data so that every customer interaction stays at a consistently high level. Data silos, on the other hand, make it difficult for you to create a unified customer profile. And without a unified view of your customers, it’s nearly impossible to deliver personalized marketing, sales, or customer service.

Data silos give you an incomplete view of your business and customers. Without unified data, your departments are working on fragmented views of customers and incomplete or inaccurate information. For instance, your sales team may be analyzing outdated customer data and your marketing team working with inaccurate demographics — all of it leading to unsuccessful outreach campaigns.

Data stored across multiple silos without standardized security and governance guardrails is harder to monitor and protect. This opens up risks of data breaches and regulatory fines. Conflicting reports or metrics can also undermine your confidence in your organizational data — leading to hesitation to act. This mistrust creates a ripple effect that can limit your business’ ability to make data-driven decisions.

Data silos can increase your operational costs for storage, maintenance, and integration — as there are costs associated with maintaining disparate data sources in different locations. Additionally, inefficiencies, challenges with data-driven decision-making, and missed business opportunities caused by inaccessible data all add up to lost time and money. Moreover, when you do try to use information from multiple silos, you will likely have to reconcile discrepancies, and that can be time-consuming and expensive.

Data silos stifle teamwork by limiting access to critical information. This isolation can create an “us versus them” mentality, where your departments prioritize their own goals over organizational cohesion. Over time, silos contribute to feelings of frustration and disengagement among your company.

With AI on every CEO’s and executive board’s agenda, data has gained new focus. Everyone knows that AI and agentic AI are only as good as the underlying data. Unified, clean data will power your AI algorithms and AI agents to automate tasks, improve operational efficiencies, and personalize experiences for your customers and employees.

Let’s take marketing, for example. Marketers know that precise targeting leads to more effective campaigns. AI agents can now segment target audiences with precision and speed. In the Salesforce ecosystem, you can chat with AI agents using natural language prompts to describe your ideal target audience. The AI agent will ground that prompt in the unified data in Data Cloud and segment your audience based on your desired attributes. But if your customer data is trapped in silos, duplicated, and filled with errors, you won’t realize the benefits of AI. Targeting and segmentation, in this example, may not be precise or inclusive enough.

How do you know if you have data silos? Here are some key signals.

• Department-owned systems with limited access outside the departments themselves
• Data trapped in systems such as data lakes or data warehouses inside or outside your organization
• Duplicate data in inconsistent formats
• Limited integration among systems
• Long delays in getting access to the data you need when you need it

How do you start to address and resolve data silo issues? A good first step is an internal audit to map out your organization’s data flows and repositories. Note where data is stored, who uses it and how, who has access to it, and which departments need it and for what purposes.

Employee feedback is another way to pinpoint silos. Employees who frequently interact with data systems can offer insight into the roadblocks they encounter and describe the information they are missing to do their best work. To start out, you can send out surveys to your teams or hold focus groups to learn about their roadblocks and identify ways to bridge data silos.

A next step will be to list and categorize the issues by priority for resolution. Focus on those with the highest impact on operations and decision-making first. For instance, customer data silos may take precedence over less-critical internal workflows.

Data silos aren’t going anywhere, anytime soon. But it’s possible to bridge them. Below are four best practices.

Outdated or disconnected systems often perpetuate silos, so it’s a good idea to prioritize data integration technologies and tools. Trusted data platforms, such as Data Cloud, can integrate with your existing data lakes and warehouses and securely unify your external and internal data sources, giving you a 360-degree view of your customers. A customer data platform like Data Cloud will also power your AI and agentic AI action.

Create and enforce good data hygiene practices, such as organization-wide data standards and protocols that outline how data should be collected, stored, protected, and shared. Forming a data governance committee to oversee these efforts can boost accountability and ongoing compliance. You may also use tools or frameworks like master data management (MDM) solutions to enforce standardization.

Bridging data silos starts with fostering a culture of collaboration across your organization and that starts with your leadership team, who can champion collaboration in all aspects of your business. Consider forming cross-functional teams, hosting regular collaborative meetings, and implementing shared projects. Explain why reducing silos is beneficial for everyone at your organization and offer shared incentives that motivate employees to prioritize teamwork. Training programs can also reinforce the value of collaboration and teach employees how to effectively work together and share data.

Strong data governance policies provide the framework you need to maintain consistent data sharing, ensure data security and privacy, and bridge silos over time. Start by defining clear roles and responsibilities for data ownership, with accountability at every stage of the data lifecycle . Establish policies for regular data audits and compliance checks to maintain data quality and address issues proactively.