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Data Silo Examples and Tips to Eliminate Them

Rachel Nizinski, Appian
February 22, 2024

Only about a quarter of organizations report that they are actually data-driven, according to Harvard Business Review. Much of this is due to data silos. Data silos are often the result of information that’s spread across different systems, and they create data accuracy challenges and prevent you from having a single source of truth for your organization's information.

And it’s a problem that’s getting worse as app sprawl proliferates. In fact, most businesses today rely on hundreds of apps to run their day-to-day processes—many of which were adopted in the last several years with the digital transformation boom.

Why do so many organizations struggle to overcome data silos? Let’s explore the meaning of data silos, examples of data silos, why they occur, and best practices for eliminating them. 

What is a data silo?

The term “data silo” is used to describe information that’s isolated in databases, applications, or departments within an organization. It can also be used to describe situations where data isn’t being shared effectively across an organization. 

Just like grain silos on a farm separate and store different types of crops, data silos isolate and store different sets of data. But without good airflow, the grain in a silo can mold and become useless. The same can happen to your data. 

So why are data silos problematic? When data is separated in different systems and applications without open connectivity, it becomes a hindrance to your organization and decreases the value of the data.

Data silos have become more prevalent as organizations continue to rely on more technology and collect more data. But before you can improve your data architecture and pave the way for success with automation, you need to understand what’s contributing to your data silo issues.

What causes data silos? Tech, governance, and cultural factors.

Siloed data is often the result of disparate systems and technologies, a lack of data governance policies, and cultural barriers to sharing data. These things have been fueled by the rapid adoption of cloud computing and SaaS applications, which can create isolated data sources that are difficult to integrate. Additionally, the rise of big data and the Internet of Things (IoT) has led to an increasing volume, variety, and velocity of data, making it more challenging to manage and share.

Data that’s siloed in disconnected systems makes continuous improvement incredibly difficult and results in processes and experiences that aren’t living up to their full potential. 

3 data silo examples.

Because nearly all of your applications and business processes rely on information from different systems, data silos are bound to occur. Here’s a look at some common examples of data silo issues:


When every department relies on their own set of systems and tools for day-to-day work, it results in limited data sharing and collaboration. An enterprise-wide data management solution can combat this by centralizing data from various departments into a unified system. For example, a company could integrate its customer relationship management (CRM) system with its accounting software to ensure that customer data and financial information are connected and accessible across marketing teams, sales teams, and finance departments.

Database and systems

Data stored in various databases, applications, or legacy systems often comes with formatting or integration limitations that make it difficult to connect and unify. Platforms that unify data across different systems can help combat these negative effects. For instance, implementing a data fabric (a virtualized data layer) can help unify and connect data from an organization’s CRM, ERP, and HR systems, ensuring that data flows smoothly across applications.

Security and compliance

Sensitive or regulated information often needs to be kept separate from other data to ensure privacy and compliance with regulations, leading to isolated storage or restricted access. While these measures are necessary to ensure data security and compliance, organizations can establish data governance frameworks and use secure data sharing platforms or encryption methods to strike a balance between security and collaboration. 

Discover how you can eliminate data silos with an integrated data fabric: Watch our on-demand webinar

5 best practices for identifying and resolving data silos.

The data silo examples above highlight how easy it is for data in a modern enterprise to become scattered and inaccessible. This is a big problem for business leaders trying to create a data-driven company culture. And it prevents business users from accessing valuable insights that help them make more informed decisions and track performance toward business goals. 

Breaking down data silos isn’t easy. Most data management strategies aim to eliminate silos by undertaking massive data migration efforts to port all enterprise data into one place. This approach often isn’t practical for businesses that need easy and fast access to insights but have limited resources. And over the long term, these solutions could end up contributing to your costly data silo problem rather than solving it. 

Organizations can remedy this and eliminate data silo disadvantages by using these five best practices:

1. Identify disconnected data.

The first step to eliminating data silos is to identify them. Data discovery tools with pre-built connectors can vastly improve this process, helping you quickly uncover and connect data sources.

2. Implement a data governance framework.

A data governance framework sets out policies, procedures, and standards that guide how data is managed within an organization. Implementing a data governance framework ensures consistency across the organization and establishes best practices for how data is collected and shared.

3. Invest in a data management solution.

Data management solutions can help organizations break down data silos by connecting disparate data sources and enabling data to be shared across systems. Popular data management technologies include data warehouse, data lake, data mesh, and data fabric (more on data fabric below).

How does data fabric differ from older technologies and what benefits does it deliver? Read also Data Fabric vs. Data Mesh vs. Data Lake

4. Promote cross-team collaboration.

Eliminating data silos requires a culture of collaboration, where teams work together to share data and insights. You can encourage this by incentivizing cross-departmental communication and training employees on the importance of using data in their work.

5. Establish data ownership.

Establishing roles and responsibilities for managing data—including defining who is responsible for data quality, security, and sharing—is a key component of good data management that promotes effective data sharing across an organization.

Want to learn about how data fabric works and its real-world benefits? Get the Data Fabric Guide

How to break down data silos with a data fabric.

Even with the best data management practices in place, you’ll still need the support of tools and technology to connect your data and turn it into insights that help you take action. 

A data fabric is the best data management option for organizations that want to build real-time applications with a 360-degree view of their operations. Data fabric takes the unique approach of keeping data where it is. Rather than migrating or trying to get your data in one place, a data fabric sits on top of your systems and stitches them together with a virtualized data layer. 

This power to connect disparate data sets means that data is no longer hidden in silos, giving you a complete view of your data that allows your organization to truly become data-driven.

Data fabric also plays a key role as your enterprise works to scale success with hyperautomation technologies. You can’t automate complex business operations without the support of a strong data architecture. That’s why data fabric is a must-have in a modern platform for process automation, which applies a wide range of technologies, including robotic process automation (RPA), intelligent document processing (IDP), workflow orchestration, and artificial intelligence (AI) to improve business processes end to end.

Want to learn more about how to succeed with hyperautomation? Get the report: Gartner® Emerging Tech Impact Radar: Hyperautomation Report.

With the rapid adoption of technologies like IoT and AI and the demand for more tailored user experiences, it’s more important than ever before to have good data management practices. The better and more connected your data is, the better the output from the technology that it feeds. 

Companies that will realize the most value from their data are those that already have good data practices in place. For example, when it comes to implementing AI, McKinsey found that companies who attribute 20% of earnings to AI are also much more likely to have data practices in place to support it.

Learn how to solve your data silo problems and speed up innovation. Get the guide: The Data Fabric Advantage.