Data silos can be a major nuisance for organizations regardless of their size. From finance to HR, all departments in an organization need access to data, regardless of the use they have for it. When data used by part of an organization is isolated from the rest of it, then data silos begin to form.
What are data silos?
Data silos, or information silos, are repositories of information in management systems where a portion of the information has been isolated. When data silos occur, information systems or subsystems that should be working in tandem stop working, which leads to a loss of efficiency.
Data silos can create significant barriers to collaboration and communication between team members of an organization. Data silos also put companies at risk of losing control of their data governance, making it impossible for a business to scale its operations. There is practically no upside to having data silos within a data environment.
What data silos look like
Data silos are data sets that have been disconnected from the rest of the system. If one were to imagine an integrated data system as a city, then an information silo would look like a blocked highway, preventing anyone from entering or leaving a particular district.
What causes data silos?
Lack of data management systems and poor communication between departments can lead to data being needlessly duplicated or isolated from the flow of information. Data silos may also occur organically as a company expands, acquires new technology, and develops new strategies.
Why are data silos a problem?
Data silos isolate part of an organization’s data, leading to less efficient business operations. Businesses need data related to customers, products, supply chain, and many other key factors to run properly, making the loss of data a major concern for all kinds of businesses.
The amount of data a business can view and share can be limited by data silos. If every department’s access to data is limited to what they have readily available, then chances for overall growth are limited. Enterprise-wide inefficiencies may remain hidden, possibly for years, if a centralized view of data is not implemented.
Efforts to increase general efficiency are impossible without proper data management. Consolidation of data can lead to discovering new opportunities and streamlining processes. Fixing data silos can help companies cut costs and generate revenue.
Data silos can cause the same information to be kept in two or more databases. If kept unchecked, data silos can lead to data becoming inconsistent over time as different departments develop their own pockets of isolated data differently. This process can exponentially increase the size data silos occupy on a system and the computing power wasted on redundant information.
Streamlining data into a single source can lead to vast amounts of storage suddenly being available for more profitable uses. Furthermore, IT departments will be relieved from stress-inducing tasks related to acquiring, building, and maintaining environments used for storing data that has no purpose.
Threaten data integrity
Data integrity is the overall good measurement of a data set’s accuracy, completeness, consistency, and security measures. When the integrity of data is properly taken into account, a business can be guaranteed that they’re working with reliable data.
Old data, for example, loses its value as time passes by, eventually becoming obsolete. While data like expiry session type cookies will disappear when interaction with the end-user has been finalized, expiry persistent type cookies and other kinds of data may remain and build up space on an unintegrated system.
If information is stored in a data silo, updates to a data set in the general system will not be reflected. This can lead to major inconsistencies and poor communication between departments, who constantly miss their marks because they’re using outdated values in their calculations.
A data silo created by bad corporate practices may only reinforce the corporate culture that created it. If, for instance, management over-incentivizes departments to compete, they may lead them to keep data to themselves to gain a competitive advantage or use classification solutions that will not translate well between departments.
A centralized data strategy is a powerful tool for developing cooperative practices between departments, especially when working with new units or recently acquired subsidiaries. After all, the data will be used by all departments, so all of them should take part in maintaining its consistency and efficiency.
How data silos occur
More often than not, data silos are the result of organizational structures that have not taken data management into consideration. Nonetheless, data silos may be caused by an end-user or a department, so it is always good to be prepared.
Faulty IT strategies
A decentralized IT environment is bound to generate data silos. If there is no central IT strategy, then units may acquire different tech and use incompatible cataloging methods. The variety of data platforms in the market creates difficulties when trying to identify and break down data silos. If teams use different methodologies it may lead to mistranslations, generating more chances for the displacement of data.
Unsystematic organizational structures
If the different teams of a company work in isolation without a single source of guidance, then data silos can happen. A well-defined organizational structure guarantees data silos will be less common. This makes data silos common in big organizations with several subsidiaries, but small businesses can also be affected by a lack of managerial oversight.
Multiple sources of data
Data integration is extremely important for a modern business to run smoothly. IT teams must always make sure that the different applications being used by the business units of an organization properly communicate.
Sometimes IT and management have created a well-structured and synergetic data environment but data silos still appear. If an organization’s corporate culture doesn’t emphasize that good practices in data sharing are crucial to maintaining efficiency, then people may be oblivious to the threats data silos create for a business and end up creating them accidentally.
Team members must understand how important and valuable the data they handle is for operations. If workers understand the value of data as an asset they have ownership and control of, then they will gain the motivation to develop better data management practices.
Data silos might be an unforeseen consequence of a business’s digital transformation and rapid development. When a business expands, new needs appear and they are commonly addressed rather quickly, leaving a certain margin for error that can result in data silos being formed. The same thing can happen whenever a new business unit is developed, or when a merger or acquisition takes place.
What are the business costs of data silos?
Several factors determine how much money a company is losing because of data silos, and how cost intensive it will be to fix data silos. The number of silos, how hard they are to fix, and how often new data silos develop are all important variables to consider.
Generally speaking, a business struggling with data silos will be economically affected by the following factors:
- Increased IT and data management expenses;
- Stunted productivity;
- Reduced business opportunities;
- Low-quality customer experiences;
- Lack of trust in reported data;
- Missed chances of development via operational analytics.
How do you identify data silos?
It is hard to identify data silos because they are disconnected from the rest of the system. To detect a data silo, IT and data management teams should keep an updated inventory of all systems in their organization. If the data being reported is inconsistent with what is on record, then that is evidence of a data silo.
However, keeping an inventory of that nature can be a very complex task, especially when working with multiple business units or with large data environments. Even if properly identifying where a silo is located is complicated, a business can have clues of a potential data silo’s existence.
If an organization is experiencing bloated IT costs that don’t reflect its operations, which may indicate the existence of a silo. A business with an organizational structure that prioritizes data management should not experience unexpected costs.
Incomplete data sets
Data silos may have a negative effect on customer data. If end-users are finding their data sets to be incomplete or out-of-date, then their data might be compromised by a data silo.
If a business intelligence or data science team is unable to access data relevant to their operations, this data may be withheld by a data silo. In well-kept management systems, engineers can find and implement data without any hindrance.
If one suspects a data silo, it is good to investigate the data values reported by different departments. If departments are reporting incongruent data values, a potential data silo may be causing that issue.
How do you break down data silos?
To break down data silos, the most common strategies companies employ are:
- Extract, transform, and load (ETL)
- Data warehouses
- Shifting organizational
- Intelligent data management
Extract, transform, and load (ETL)
Extract, transform, and load is the most popular method of dealing with data integration. By using this method, one can extract data from a data silo, re-consolidate it, and place it into a well-functioning system.
When integrating a system, one can use a data warehouse as a repository for data from different systems. Structured translation data intended for business intelligence, analytics, and reporting applications can be stored in a data warehouse. Large volumes of structured, unstructured, and semi-structured data can also be kept by organizations on data lakes, working similarly to a data warehouse, albeit at a larger scale.
Shifting organizational culture
A long-term solution for data silos is to re-assess the way employees of a company see their relationship with data management systems. A data strategy development process or a data governance initiative can help various departments understand an organization’s data needs and adopt the proper procedures to maintain functionality.
Intelligent data management
Intelligent data management strategies are the best way to deal with existing data silos while also preventing the creation of new ones. Smart algorithms can rapidly run an analysis and review data stored on cloud-based solutions and on-premise storage.
Intelligent data management can help a business optimize its operations and finances by addressing the classification needs of multiple storage environments. A centralized approach to data management can lead to an organization having full control of its data, which can be further analyzed to generate opportunities for growth.
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