What Are Data Silos? Why Are They Problematic?

When different departments or divisions within an organization independently procure and manage their data systems, they often create data silos. These are repositories of information that are useful to the business unit that creates them but inaccessible to other teams that might benefit from the data they hold. For example, silos may wall off data collected and managed by the sales team from product development, HR, and logistics.

Data silos can adversely impact the larger organization by making it harder for teams to collaborate, for planners to gain insights by analyzing data across operations, and for business leaders to conduct oversight. And because silos fragment data sources, they can erode the quality of business data and make it more likely that valuable information is lost or is difficult or time-consuming to retrieve and use.

In all these ways, data silos are an impediment to organizations that want a central authoritative data repository that all units across an organization can tap into and rely on to be accurate, without omissions or redundancies. A single consolidated data repository is essential to understanding operations, financials, workforce requirements, supply chains, customer behavior, and other facets of the business.

With the right data management strategy and technologies, however, breaking down silos isn’t as time-consuming or expensive as it once was.

What Are Data Silos?

Data silos are repositories of data walled off from other systems within an organization. The cause of this isolation can be technological, when applications and data systems aren’t designed to communicate with others used in the same company. Or it can be organizational, with different business units not structured to share information with one another.

Company culture is often the culprit. A culture that incentivizes different divisions to operate independently, or even in a competitive manner, can foster the development of data silos. Data silos also frequently result from acquisitions, when companies bring in their own legacy systems and operational methods.

Whatever the reason for their creation, data silos can adversely impact a business in several ways. They make it harder for different divisions to work together, for planners to devise data-driven strategies, for data scientists to apply modern analytics techniques that deliver business intelligence, and for company leaders to get a holistic view of their customers and business operations to make informed decisions. The proliferation of silos also tends to result in duplicative, conflicting, missing, or incomplete data.

Data Silos FAQs

Are data silos good or bad?

Data silos negatively impact organizations by making it harder for different departments and divisions to collaborate, for company leaders to gain comprehensive visibility into their operations and finances, and for planners to analyze comprehensive data to implement effective business strategies. For example, if HR and finance data is siloed from the sales organization, regional sales leaders won’t be able to easily use that data to better evaluate the productivity of their reps by factoring in salaries, commissions, and travel and entertainment expenses. Silos also promote duplication, making it more likely that data will be out of date or otherwise inaccurate.

What is the difference between data warehouses and data silos?

Data warehouses are centralized repositories of data that organizations can make accessible to various departments and divisions so they can, for example, run analytics and make more-informed decisions. Data silos are isolated repositories that make it difficult or impossible to share data across an organization.

What is the opposite of a data silo?

Any system architecture that makes it easy to share data across departments and divisions is the opposite of a silo. These architectures can take the form of centralized repositories, such as data lakes, which store unstructured data, and data warehouses, which store highly structured data. Or they can be connectors that bridge disparate data systems, usually in real time, simplifying the job of data transformation and ensuring the timeliness of the data available for analysis.