“Data is knowledge, knowledge is power, and bad data equals bad decisions,” says Appian Senior Solutions Consultant Ben Crawley.
We’ve all felt the sting of poorly integrated solutions, hard-to-access information, and sometimes, inaccurate data. This “bad data” is often the result of information that’s spread across different systems, creating data accuracy challenges and preventing 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.
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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 types of data silo issues:
Every department relies on their own set of systems and tools for day-to-day work, resulting 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.
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 facilitate data unification across different systems can help combat these negative impacts. For instance, implementing a data fabric can help unify and connect data from an organization’s CRM, ERP, and HR systems, ensuring that data flows smoothly across applications. You can think of a data fabric as a virtualized data layer.
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 often 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.
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.
But 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 kinds of solutions could end up contributing to your data silo problem rather than solving it.
That’s why a data fabric approach is a better option for many. A data fabric aims to overcome silos by connecting them rather than eliminating them. This means that your data stays exactly where it is, so you don’t have to deal with complex migrations or integrations that require specialized data and software engineers. A data fabric grows with your business, seamlessly expanding to connect new data sources as you adopt more solutions. And because a data fabric doesn’t require you to move any data, your data stays yours.
Some process automation solutions—the Appian Platform, for instance—have a built-in data fabric that unifies information from multiple systems, enabling secure and easy access to enterprise data while delivering a 360° view of your organization.