What are data silos and what problems do they cause? (2023)


  • craig stedman,Industry editor
  • Aislyn Fredsall

What are data silos?

A data silo is a data warehouse controlled by a department or business unit and isolated from the rest of an organization, just as the grass and grain in a farm silo are isolated from outside elements. Isolated data is usually stored in a stand-alone system and is often incompatible with other data sets. This makes it difficult for users in other parts of the organization to access and use the data.

Data silos can have technical, organizational, or cultural roots. In large organizations, they tend to arise naturally, as separate business units may operate independently and have their own IT goals, priorities, and budgets. But any organization can end up with data silos if it doesn't plan well.data managementStrategy.

(Video) What are Data Silos and Why Are They Bad for Business?

Why are data silos a problem?

Data silos hinder business operations anddata analysisinitiatives they support. Silos limit executives' ability to use data to manage business processes and make informed business decisions. They also prevent call center agents, salespeople, and other operational personnel from accessing relevant data about customers, products, supply chains, and more.

The specific possibilities thatData silos can hurt a businessInclude the following:

  • Incomplete records.Data silos lock data away from users who don't have access to it. As a result, trading strategies and decisions are not based on all available data, which can lead to wrong decisions. Silos can also derail construction effortsdata warehouseYdata lakesthat integrate disparate data sets for business intelligence (BI) and analytics applications.
  • Inconsistent data.Many data silos are not consistent with other data sets. For example, a marketing team may format customer data differently than other departments. Data errors by a sales team may not be able to be detected or corrected. Data updates in other systems are not done in an isolated customer service system. Such inconsistencies arisedata quality, accuracy and completeness issues affecting end users in operational and analytical applications.
  • Duplicate data platforms and processes.Data silos increase IT costs by increasing the number of servers and storage devices a business must purchase. Also, in many cases, these systems are implemented and managed separately by departments rather than by those of an organization.data management team. This further increases the expense and inefficient use of IT resources.
  • Less collaboration between end users.Siloed records reduce opportunities for data sharing and collaboration between users from different departments. It's harder to collaborate effectively when employees don't have visibility into siled data.
  • Silo thinking about the departments.Data silos contribute to organizational silos: departments and lines of business that are heavily guarded and reluctant to share their data with others. You can also counterattackdata officePrograms aimed at breaking down data silos and ensuring that data is consistent and accurate across all systems in an organization.
  • Data security and regulatory compliance issues.Some data silos are stored by individual users in Excel spreadsheets or online business tools like Google Drive, often on mobile devices. that increasesSecurity risks and data protectionfor organizations when they do not have adequate controls. Silos also complicate efforts to comply with privacy laws.

How data silos are created

A department or end user can go rogue and create a data silo, even in an organization with strong data management processes. More often, however, data silos are a consequence of how organizations as a whole, including their IT operations, are structured and managed. The following factors often lead to silos:

(Video) What is a Data Silo?

This article is part of

What is data architecture? A model for data management

  • Which also includes:
  • 5 principles of a well-designed data architecture
  • Data Modeling vs. Data Architecture: What's the Difference?
  • Data architecture vs. information architecture: how they differ
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(Video) Why Data Silos Kill Content and Conversions
  • Technology implementations and IT strategy.Some organizations have decentralized IT purchasing decisions, allowing departments and business units to purchase technology themselves. This often results in the deployment of databases and business applications that are not compatible with or connected to other systems. The same can happen when corporate IT teams are involved in purchasing decisions when a department needs a specific technology. The variety of data platforms now available also contributes to the creation of data silos: companies can use them in addition to common relational databases.big dataplatforms,NoSQL databases, cloud object storage services and specialized databases to meet different business needs.
  • organizational and management structure.Data silos regularly arise when business units become fully decentralized and managed as separate entities. This is more common in large organizations with multiple subsidiaries and operating companies, but can also occur in smaller organizations with a similar structure and management approach.
  • Culture and principles of the company.Even when business and IT processes are managed in a more unified way, corporate culture can drive the formation of data silos. There is less incentive to avoid them when data sharing is not a cultural norm and an organization does not have common goals and objectives.Data Management Principles. Departments can also view their data as assets they own and control, further driving the development of data silos.
  • Corporate Growth and Acquisitions.Growing companies tend to have data silos. As a business expands, new business needs may need to be quickly addressed and additional business units created. Both situations are natural data silo incubators. Mergers and acquisitions also create silos within an organization, some known and some potentially hidden.
What are data silos and what problems do they cause? (1)

How are data silos identified?

Due to their disjointed nature, data silos can be difficult to spot. Ideally, IT and data management teams take stock of the systems in their organizations and regularly update them to add new ones. This should help identify and document data silos. But finding them all can be a challenge, especially in large organizations with self-contained business units.

However, evidence of data silos can come to light. The signs that indicate them are:

  • different departments report conflicting data;
  • BI ydata scienceTeams cannot find or access relevant data;
  • executives who complain about the lack of data on some business operations;
  • end users discovering that records are incomplete or out of date; AND
  • Unexpected and out-of-budget IT costs pop up.

How do you break data silos?

By eliminating data silos, an organization can more effectively manage and use data. It also often helps reduce technology and data management costs. The following approaches can be used separately or together to break down silos and connect data assets to better support business operations:

  1. data integration.Integrating data silos with other systems is the easiest way to break them down. The most popular form of data integration is Extract, Transform, and Load (ETL), where data is extracted from source systems, consolidated, and loaded into a target system or application. OtherData Integration TechniquesExamples that can be used against silos include real-time integration, data virtualization, and extract, load, and transform, a variation of ETL.
  2. Data warehouses and data lakes.The most common target system for data integration jobs is a data warehouse that stores structured transactional data.BI applications, analysis and reporting. Increasingly, companies are also building data lakes to store large data sets, which can contain vast amounts of structured, unstructured, and semi-structured data used in data science applications. Thisof types of platformsProvide centralized repositories for data from disparate systems, making it a natural way to address silos.
  3. Enterprise data management and governance.Ultimately, it's best to not only break down existing data silos, but also prevent new ones from emerging. A more comprehensive data management strategy helps achieve both goals. For example,Data architecture designdocuments databases, maps data flows and creates a blueprint for the provision of data platforms. An enterprise data strategy better aligns the data management process with business operations. and arobust data governance programit can directly reduce the number of data silos in an organization and promote common data standards and policies.
  4. culture changeTo truly break down data silos, it may be necessary to change the culture of an organization. Efforts to do this may be part of theData Strategy Development Processor a data governance initiative. In some cases, achange managementA program may be required to implement cultural changes and ensure that departments and business units embrace them.

What are the business costs of data silos?

The financial cost of data silos depends on the organization: how many silos it has, how successful efforts to eliminate them are, whether they keep growing. In general, increased IT and data management costs are the most tangible costs. But data silos also have a number of hidden costs, including:

(Video) What Are Data Silos? | Data Bytes

  • reduced productivity;
  • less effective business management;
  • lost business opportunities;
  • inferior customer service; AND
  • a lack of confidence in the data that limits its use and commercial value.

The conditionsdata siloYinformation silosometimes they are used as synonyms. More often though, information silos are seen as a cultural problem caused by departments or individual employees not wanting to share information. In addition to cultural change, there is a way to address this last problemCreation of an information architecture.along with a data architecture.

This was last updated onOctober 2021

Learn more about the data silo

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  • Why Data Silos Matter: Clarifying Ownership of Data Problems
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