What are Data Silos?

Data Silos: Customer data, employee data, process data, and asset data — the list is very long, and this is the reason why today’s businesses run on data. Data ignites the work process, helps to make better business decisions, assists in managing the different projects, and so much more. So how to make sure that the data storage is adequate and helps people meet their needs? The key is to remove the data silos. And this blog explains what they are, why data silos occur, and the challenges we face in dealing with them. Let’s get started.

What are Data Silos?

A data silo is a collection or group of data managed by one group that is not accessible by any other group or team in the organization. For example, an organization’s marketing, finance, and HR departments need different data to manage their work efficiently. These groups store that data in separate locations called “information or data silos.”

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Doesn’t this sound harmless and straightforward? But that is not because this siloed data can develop barriers in communication like information sharing and collaboration across various departments. Let’s take a simple example of how a data silo can create miscommunication.

Consider a toy selling company that has separate marketing and finance teams. And let us say the company is expanding its wings; the marketing teams know what toys are selling more, and the finance teams know the cheap and best toys.

Now comes the clash. The finance team may prepare a budget according to their perspective of cheap and best toys, and the marketing team may not agree with this because they may need more funding to buy the best-selling toys.

This is how data silos may create miscommunication among various departments.

In a nutshell, siloed data isn’t good data. When data is accessible and understandable across your organization, it is healthy. Data isn’t contributing value to analysis and decision-making processes if it’s challenging to find and utilize promptly or if it can’t be trusted once it’s found. The full benefits of digital transformation will not be realized if a business digitizes without breaking down data silos. Organizations must give decision-makers a 360-degree perspective of the data relevant to their analyses to become genuinely data-driven.

Why do data silos occur?

The three main reasons why silos occurs are:

  • Organizational Structure: In the past, when big data was not a big thing, data silos were not considered bad, and different departments in an organization could develop and manage their own data. Each division has its own independent set of policies, procedures, and objectives, and teams devised their own methods for working with and evaluating data that met their own requirements. Because data is collected and housed in silos, they continue to form around company departments.
  • Corporate principals and culture: Even when IT and business activities are managed more collaboratively, company culture can encourage data silos to form. If data sharing is not really a cultural norm and a business doesn’t have similar goals and principles for data management, there are minimal incentives to avoid them. Departments may also regard their data as an asset they own and control, which encourages the formation of data silos.
  • Business acquisitions and growth: Data silos are common in growing businesses. As a firm grows, new business demands may arise, necessitating the creation of additional business divisions. Both of these scenarios are ideal for the development of data silos. Mergers and acquisitions also introduce silos into a company, some of which are visible and others that are not.

Here are some other reasons:

  • Firstly, some companies create silos for their own convenience while planning out their information management systems. 
  • Secondly, many companies don’t choose to store data because the amount of data is so large that it overwhelms their current storage capacity. Lastly, there has been a rise in security breaches where hackers have successfully stolen company data and redistributed it for their own ends.
  • Thirdly, data silos are created when an organization doesn’t know what data they have or where to find it. This happens because many organizations don’t know which data is useful to them and how it can be combined with various other data sources to create new insights. 
  • Another reason for data silos is the lack of interoperability between different databases or software that different departments in the company use.

Why are data silos a problem?

Organizations often have different sources of data, which can be a problem if they are relying on these different sources for critical operational decisions. Data from these sources is often inconsistent, creating additional problems. 

Data silos can also lead to a lack of understanding because staff members may not access the information they require to finish their tasks.

Data silos are a problem for four primary reasons:

  • Data silos inhibit the free flow of information and make it difficult to extract necessary data from disparate sources.
  • Lack of integration can lead to errors such as duplicate or inaccurate records.
  • Data silos create a lack of accountability by keeping data organized in one place that is not easily accessible.
  • Silos are costly, in terms of money and time, when they need to be accessed for analytics or other purposes.

Challenges in dealing with siloed data

Managing siloed data poses many challenges. Since systems with various permissions and hierarchies are set up in a siloed manner, undoing some of the silos may be difficult. 

For example, a company may have a system for communications, which is different from the one for customer records, and both require separate permissions and security measures. 

With all these different systems, it can become very hard to see what someone else is doing and how they’re doing it.

Siloed data impacts data quality and organization, but there are many ways to mitigate the negative effects. 

Ways to break down data silos

The best method to help get rid of data silos is to consolidate your data into a data warehouse. Here are a few different ways a company may consolidate its data into the warehouse:

The more integrated your data is, the more likely it will be aggregated into a data warehouse. Setting up a data pipeline is one possible solution to achieve this.

Here are a few different methods a company can use to get data into a data warehouse:

  • Scripting: Some companies use scripts (written in SQL, Python, etc.) to write code for the extraction and movement of data. This sometimes requires considerable time and expertise, but it is crucial.
  • On-premise ETL tools: ETL (Extract, Transform, Load) tools can simplify the process of moving large amounts of data by automatically extracting them from a source, performing transformations specified by the user, and then loading them into other systems. These tools are often installed on your own site.
  • Cloud-based ETL Tools: The ETL tools available in the cloud are used by organizations deploying ETL tools to the cloud. These versatile tools are commonly needed with cloud-based data warehouses.


In conclusion, data silos are a problem because they have created many different databases that are difficult to manage. This has led to poor collaboration, information overload, and the inability to rely on data for decision-making.

Data silos are a problem because they have created many different databases that are difficult to manage. The result is poor collaboration, information overload, and the inability to rely on data for decision-making. To avoid these problems with data silos, make sure all departments share their data and collaborate. This will keep your company running smoothly and efficiently.