Data Silo Definition & Meaning

A Data silo is a group of raw data in an organization that is isolated from and not accessible by other parts of the organization. This causes a lack of transparency, efficiency, and trust with the organization. Data silos can occur for several reasons, including competition between departments, causing employees to keep data from each other, large organizations being separated by too many layers of management and specialized staff, and applications not being designed or used to cross-reference or add to one another. In most cases, a data silo occurs from data being collected with a business tool that is not connected to the rest of a technological ecosystem.

Data silos cause wasted resources and hindered productivity. Below are the problems having data silos can cause: 

  • An incomprehensive view of data: With data being isolated, relevant connections between siloed data can lead to missed insights, lost opportunity and miscommunication. Organizations forfeit a 360-degree view if data silos are present.
  • Wasted storage space: If data is isolated, it is duplicated across departments that need the data. This wastes data storage, and in turn, money. If the data is streamlined, every employee has one, central platform to access the data.
  • Inconsistent data: When the same information is stored in different places, inconsistencies are bound to happen. Information is fragmented and could be updated in one place and not the other, making it skewed. 
  • Wasted time: A department that needs siloed data has to jump through multiple hoops to gain access to the data, wasting time and increasing the chances the data may no longer be valid when the department obtains it.

How to prevent and get rid of data silos

Getting rid of data silos can be done efficiently if it is treated as a top priority. Siloed data can be consolidated or prevented by:

  • Using integration software
  • Encouraging a collaborative workplace
  • Simplifying and consolidating tech infrastructure
  • Developing new processes for storing data
  • Consolidating data in a data lake or data warehouse

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