What Are Data Silos?

Data silos create unnecessary overhead and additional work when multiple departments maintain their own versions of the same data. Here’s how to spot and mitigate your data silos.

Written by Artem Oppermann
What Are Data Silos?
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UPDATED BY
Matthew Urwin | Nov 06, 2023

Data silos refer to single, isolated data stores within an organization that are not connected or don’t interact with other data sources within the same organization. Data silos can be a result of different factors, such as departmental silos within an organization, business units using different systems, or the lack of a centralized system for storing and managing data.

We can view each data silo as an independent system. The data in these silos are usually difficult or impossible to access for other departments or systems in the same organization. The consequence is that business intelligence and data science teams can only get a fragmented view of the company’s data and information. In other words, it becomes difficult (or impossible) to gain cross-departmental insights into the data or make well-informed data-driven decisions.

What Is a Data Silo?

Data silos are groups of data that are disconnected from other data sources within a business. They often result when one team develops their own methods for storing data, leaving it inaccessible to other teams across the organization.

Data silos should be broken down to improve data sharing and accessibility. To do this, companies should take a more holistic approach to managing their data by implementing data integration and governance strategies.

More on Data Storage From Built In’s Tech DictionaryWhat Is a Data Lake and When Should I Use One?

 

How Do Data Silos Occur? 

Departmentalization 

Within an organization, different departments or business units often work independently of each other and maintain their own data and systems. Over time, this can lead to the creation of isolated data silos that are specific to each department.

 

Legacy Systems

Legacy systems that aren’t compatible with modern technologies can also contribute to the formation of data silos. These systems may not be able to integrate or share data with newer systems, thereby resulting in data silos separate from other systems.

 

Poor Data Integration 

Poor data integration between systems can also lead to the creation of data silos. When systems are unable to communicate with each other or exchange data, they are more likely to become isolated and operate as standalone systems.

 

Different Data Formats

Data silos can form when systems use different data formats. When we store data in different formats, it can become difficult to integrate and exchange data between systems.

 

Organizational Culture

Organizational culture and policies can also play a role in creating data silos. Certain departments may be hesitant to share data with other departments due to data security, privacy or control concerns.

What Is a Data Silo? | Video: intricity101

 

How to Identify Data Silos

Identifying data silos can be difficult. However, there are several steps a company can take to identify where silos exist within the organization.

  • Conduct a data audit. A company can start by reviewing all of the data sources that the company is using. This includes customer, sales, marketing and operational data. You should identify where data is stored and how it’s used.
     
  • Look for redundant data. In many cases, data silos exist because different departments or teams are storing the same data in different places. A company should look for instances where data is duplicated and consider consolidating it into a single repository.
     
  • Identify communication barriers. Data silos often arise because different teams or departments are not communicating effectively with each other. A company should look for instances where there are communication problems and consider implementing cross-functional teams or collaborative tools to improve communication and break down silos.
     
  • Analyze data flows. A company should analyze how data moves within the organization, from sources to end-user. Identify any gaps or inconsistencies in the data flow. Furthermore, consider how you can better integrate to create a more comprehensive view of your business.
     
  • Consider technology solutions. There are various technology solutions, such as a customer data platform (CDP), that can help organizations break down data silos by unifying data from different sources into a single repository.
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Why Are Data Silos a Problem? 

In short, data silos limit an organization’s ability to use data effectively and make informed decisions. When data is stored in different silos within the organization, it becomes increasingly difficult to get a complete and accurate picture of what the data tells us. This can lead to missed opportunities to gain insights or create additional value for the business.

At the same time, data silos can pose a risk to data security and privacy because data may not be properly managed or protected when stored in silos. Finally, data silos make it difficult for companies to respond to changing business needs because the required data may not be readily available. 

Related Reading From Our ExpertsWhat Is Data Management?

 

Impact of Data Silos on Business Efficiency

Data silos can have a particularly devastating effect on an organization’s efficiency, hampering decision-making since data has become inaccessible or unusable.  

 

Narrow View of Data

When data is disconnected, teams are unable to develop a broader view of data across an organization. This can prevent companies from detecting opportunities to address inefficiencies and reduce costs. Teams may also miss out on relevant data stored by other departments that could inform campaigns, simplify workflows or boost productivity, leading to losses in revenue.   

 

Excess Resources

Storing data in different formats makes it harder for departments to work together, and it may force personnel to spend time transferring this data into a different format. In addition, teams using their own data storage techniques can result in two departments accidentally creating duplicate data. These instances may require more energy and resources from the IT team to store and re-format data when much of this data may be unnecessary in the first place.  

 

Less Reliable Data 

Data stored in various systems can become inaccurate over time. For example, one department may have updated a customer’s contact info, but another department may have failed to do so or entered details incorrectly. Inaccuracies like this can cause employees to lose trust in a company’s data. And if each department has its own methods and standards for storing data, it becomes impossible to maintain the quality and consistency of company data. 

 

Worse Decision Making

Siloed data may be available to one department but inaccessible to others, making it harder for teams to retrieve relevant information. For example, customer service reps may struggle getting updated product details to clients if they can’t access the product team’s data system. Leaders may also make ill-advised business decisions if they can’t see company-wide data that could provide them with an in-depth look at each team’s performance.   

 

How to Prevent Data Silos

There are many steps an organization may take to fix data silo problems.

 

Centralize Data Storage

Data lakes and data warehouses are common methods for organizing and centralizing data. Businesses may also want to adopt other systems like database management systems and customer relationship management (CRM) platforms. With these tools, companies can create a single source of truth that employees can turn to and make it easier for all departments to monitor, share and update company data.   

 

Develop a Stronger Tech Stack

Integrating data into a single system is a demanding task, so companies may want to equip their IT personnel with an advanced tech stack. IT teams can use Python, JavaScript and other languages to perform scripting, although this is less effective in complex databases. ETL tools and software are more powerful and can automate the data integration process. Businesses can choose from open-source, cloud and other types of ETL tools that best meet their needs. 

 

Cultivate a Collaborative Culture

There will be more urgency around the need to remove data silos if there’s a need for teams to work together and share data. Leaders can reorient their company culture around collaboration by encouraging cross-departmental initiatives and restructuring multiple teams under the same department. Company executives can further ingrain collaboration into their organization’s culture by making it a core value that managers and employees are expected to practice.   

 

Establish Data Governance Policies

Following well-defined data governance standards makes it clear to employees who has access to what data and how that data is to be used. This allows businesses to better keep track of their data and ensure its effectiveness while abiding by laws concerning data privacy and other areas. Companies can even implement a data governance framework to maximize the security of their data and make sure it is stored properly.

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