Category Data Analytics
Data Silos

Data Silos – Why are they a problem? How to fix them?

Data Silos

When a group holds data that is not easily or completely available to other groups within an organization, it is known as a data silo. Each of these departments requires a separate set of data in order to carry out their respective responsibilities. Data silos, named for the grain storage buildings used by farmers, are structures used to divide data from different departments. As the number and variety of data assets increase, so do the number of data silos.

Silos of data may appear innocuous, but they prevent departments from exchanging information and working together effectively. Data quality frequently decreases as a result of data discrepancies that might occur when data from different silos is combined. Leaders can’t obtain a complete picture of their company’s data when it’s separated into separate silos.

Simply put, data that is kept in separate silos is unhealthy data.

In order for data to be healthy, it must be freely accessible and understood by everyone in your organization. In the absence of reliable and timely data, analysis and decision-making processes are unable to make informed decisions. Without breaking down data silos, a company can’t reap the full benefits of digitization. A 360-degree perspective of data relevant to their analyses must be provided to decision-makers if businesses are to be considered really data-driven.

The study of company-wide data aids in making well-informed decisions and provides a more complete picture of potential possibilities and dangers. As a result, segregated data poses a threat. Data governance is impossible to manage on an organization-wide scale if the data is isolated, preventing regulatory compliance and allowing sensitive data to be misused.

Understanding where data silos come from, how they impede the full benefit of data, and your alternatives for integrating data can help you better determine if data silos are inhibiting your ability to do holistic analysis.

Why are there data silos in the first place?

As with organizational structures, data silos develop over time. A data silo is formed when each department collects and keeps data for its own objectives. Data silos can be traced back to these three factors:

Siloed Organizational Structures:

It wasn’t regarded as a bad thing for various departments to create and handle their own data before big data and the cloud disrupted business. Siloed organizational structure Policy and procedure are different in each department. Each team established their own methods for dealing with and understanding data. Because of the way data is acquired and handled, company divisions continue to create silos.

Company Culture:

In light of the foregoing, several departments have been accustomed to operating in silos. Every industry has its own idioms, processes, and obstacles. Each department will automatically view itself as a separate business unit, distinct from other teams if they work in physically separate areas and have their own processes and goals. Data is no exception to this trend of segregation. There may be a company culture that encourages sales and marketing teams to keep their customer data apart, even though they both operate with the same customer data. Departments have not been driven to combine their data because company-wide data sharing is a new goal.


Many organizations have been forced into data silos by the very tools and systems they use to manage their data. Technology solutions and tools like spreadsheets, accounting software, or a CRM like Salesforce are used by many departments to support their operations. The majority of existing systems were not created with the intention of sharing data readily. Many of the data storage and management solutions are exclusive to the company that developed them, making it difficult to exchange data sets with others in the organization.

How data silos are sabotaging your business in the background

The purpose of each department is to help achieve a common goal. In spite of the fact that each department functions autonomously, they are all intertwined. Administration and other departments can benefit from some of the financial department’s data collection and management.

Competition, the need to save money, and the drive to take advantage of possibilities are driving firms to use their data more effectively. Increasing operational effectiveness and spotting new business possibilities necessitates easy access to all relevant data across the company.

In the end, data silos will be a stumbling block to success. Keeping data in separate silos might have the following effects on a company:

1. Data silos restrict the scope of data analysis.

Silos prevent the exchange of relevant data. The scope of each department’s investigation is constrained by the viewpoints of those within it. Without an enterprise-wide view of data, there is no prospect of uncovering inefficiencies throughout the company. For example, how can you uncover operational cost savings if you don’t unify operations and cost data?

2. The integrity of data is threatened by data silos.

Separate databases are used to store the same data, and this leads to discrepancies across the various departments’ records. Using old data might be risky since it may be out of date and hence less accurate than it was when it was first collected. For example, if a patient’s medical records are housed in many systems, this data may grow out of sync over time.

3. Data silos waste resources

For every time a user downloads data from a shared or personal storage location, resources are consumed. Reduces the burden on IT by consolidating data into a single source, freeing up precious storage space. The more people that download data to analyse it in a spreadsheet, the more copies of the same data there are.

4. Data silos discourage collaborative work

Silos are a product of culture, and culture is a product of silos. When it comes to discovering fresh ideas, data-driven enterprises are embracing the potential of collaboration. It is necessary for departments to be able to share their data in order to promote teamwork. Collaboration is hampered when data is difficult or impossible to share.

The four-step process for dismantling data silos

Silos can be broken down into two categories: technological and behavioural. The cloud has made it considerably faster and easier to centralise data for analysis. Gathering data into a common pool and format for efficient analysis is made easier by cloud-based applications. A task that used to take weeks or even months now takes just a few days or hours.

1. Change Management

To break down data silos, a company’s culture must be a part of the solution. Employees need to be educated about the benefits of a change to data sharing and data integrity. Also make clear the issues with silos, such as data quality issues and the necessity to remain competitive, so that everyone understands. Management must be willing to put in the time and effort required to implement a culture shift.

2. Centralising Data

For data management systems, the most effective strategy to break down silos is to gather all of the company’s data into a cloud-based data warehouse or “data lake.” Consolidation of data from many sources will allow for easy access by people or groups in order to balance commercial needs with privacy and security concerns.

3. Data Integration

A surefire way to keep data from becoming siloed in the future is through efficient and precise data integration. Several methods are used by organisations to combine data:

It is possible to assign the duty of writing scripts in scripting languages such as SQL, Python, or other scripting languages to IT departments. A drawback of scripting is that it might be difficult to learn. Complexity increases as the number of data sources increases. Scripts must be modified to reflect changes to data sources. It becomes increasingly difficult for IT professionals to maintain hand-coded integration.

On-premises ETL tools

Extraction, Transformation, and Loading (ETL) and ELT tools automate the process of transferring data from numerous sources to a database. Using these tools, data is extracted from many sources, transformed into a standard format for analysis, and then stored in an organization’s data centre data warehouse.

Cloud-based ETL

With the advent of sophisticated cloud providers, the ETL process is becoming simpler and faster.

Cloud-based ETL makes use of the infrastructure provided by the cloud provider, including a data warehouse and ETL tools that are optimised for that provider’s setting.

ETL eliminates data silos by giving the tools necessary to collect data from various sources and consolidate it in a single location for further processing.

ETL helps ensure that everyone is always working with the most up-to-date information possible.

3. Ensure that self-service access is regulated.

A data governance framework can be used to centralise data access and control once it has been centralised and integrated. Self-service data analysis is made possible by robust data access policies, which allow business users with authority to quickly and easily gain access to the data they require.

Data storage in the cloud: The Way Forward

When it comes to accessing data from several sources, the cloud has emerged as a natural solution.

Data silos can be bridged with the help of cloud data solutions, which help remove the technical obstacles that stand in the way of effective communication. Organizations may easily upload new and updated data to a cloud data warehouse by using an established ETL procedure to remove extraneous data and avoid duplication. Allows several departments to work together on a single platform that can be scaled to meet demand by providing fresh, clean, and fast data.

Technology and data warehouses in the cloud bring together separate corporate departments. All employees benefit from having a greater understanding of how their work affects everyone else’s. Analysts have a 360-degree perspective of the firm when they have access to enterprise-wide data.


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