Data silos have often had a negative connotation. They describe isolated data islands that appear or are discovered upon finding disjointed Data Management components.
These include:
- Systems that cannot programmatically work with other systems because of older or incompatible code
- Fixed data that is controlled by one department or team but is cut off from others in the organization
- Lack of Data Management strategy development or implementation
- Organizational politics that hinder people sharing data
- Lack of Data Governance
Data silos develop due to the different goals and priorities among teams or departments across the company. They have been criticized for “impeding productivity and negatively impacting data integrity.”
Other
Definitions of a Data Silo Include:
- “A consolidated place for storing both internal and external content of many different meta-types.” (Jill Shuman)
- “A system with lots of inputs but few outputs.” (TechRepublic)
- “An isolated point in a system where data is kept and segregated from other parts of the architecture.” (Techopedia)
- “Isolated islands of data [that] make it prohibitively costly to extract data and put it to other uses.” (Harvard Business Review)
- “A separate database or set of data files that are not part of an organization’s enterprise-wide data administration.” (PC Mag)
Data
Silo Examples Include:
- Implementing a new platform without ensuring legacy systems can be connected
- Analyzing data from a system whose original purpose was not designed to answer the desired queries
- Adopting a data lake without significant thought to use case
- Two departments storing the same data (e.g., customer name and address), causing confusion about accuracy and relevance
- An individual cloud application in and of itself
Businesses Want to Eliminate Data Silos to:
- Make data analysis easier
- Access multiple systems in real time
- Integrate big data
- Effectively do machine learning and AI
- Become more competitive
- To be more agile and flexible
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