Data silos are a major obstacle for businesses that want to leverage big data for better decision making and innovation. Data silos occur when data is stored in separate systems or locations that are not connected or integrated with each other. This prevents data sharing and collaboration across different teams, departments, or functions within an organization. Data silos can also lead to data quality issues, such as inconsistency, duplication, or incompleteness, as well as security and compliance risks. Data silos are often the result of legacy systems, organizational silos, or lack of data governance and management. This article will discuss why data silos happen and how to break them down.

Why The Data Silos Occur?

The term ‘Data Silos’ is derived from the agricultural structures that store different types of grains separately. Data silos have been a longstanding issue in organizations, but they have become more problematic in the contemporary era of big data and digital transformation. Data silos refer to the situation where data is stored and managed in isolated and disconnected systems that are not easily accessible or shared by other groups or departments within an organization. This leads to a situation where businesses have a lot of data but use very little of it, because most of the data is scattered and unorganized. There are several possible reasons for the existence and persistence of data silos, such as:

  • Incompatible data formats and standards: Data silos can arise when different applications use different formats or standards for their data. This makes it difficult for them to share or access data from each other. For instance, one application may use XML while another may use JSON. This creates a hurdle for data integration and exchange. Sometimes, another application may need to access data from these applications for a specific purpose. However, if the data formats or standards are not compatible, this may not be feasible. This results in data silos that restrict the availability and usability of data across the organization. 
  • Legacy system and technology: Legacy systems are outdated systems that are still in use by the organizations. Often, such systems are hard to replace with modern software due to the amount of customization and integration that is in place and sometimes due to legal requirements like data residency. They often pose a challenge for creating a centralized data source, which is a single and consistent repository of data that can be accessed and used by different applications and users. For example, a legacy system may use a flat file or a hierarchical database, while a modern system may use a relational or a NoSQL database. These systems have different data models, schemas, and queries that require complex and costly data transformation and mapping processes.
  • Organization culture and politics: Organization silos often lead to data silos. They are interrelated and mutually reinforcing. Organization silos create data silos by limiting the access, availability, and quality of data for different groups or functions. Data silos, in turn, reinforce organization silos by creating information asymmetry, distrust, and inefficiency among different groups or functions. This leads to data duplication, inconsistency, and fragmentation.

Overcome Data Silos

There are several possible ways to overcome data silos, such as:

  • Centralized Data System: Data integration is a key aspect of developing microservices. It means combining data from different sources and systems into a unified and consistent view. One way to achieve this is by using a centralized data system, where several applications can read from and update a single data source. This can simplify the data integration process, as the data is stored and managed in a uniform and standardized way. However, a centralized data system can also have some limitations, such as scalability, performance, and reliability issues, as the data source can become a choke point or a single point of failure for the applications.
  • Standardize data formats to standardize tools: Data integration, quality, and usability can be improved by standardizing the data formats used across the organization. Data formats are the methods of representing and storing data in a file or a system. Different data formats may have different structures, syntaxes, and semantics that can affect how data can be accessed, processed, and exchanged. By using standardized and widely accepted open data formats, such as xml, json, csv, or sql, the organization can ensure that data can be easily and consistently communicated between different applications, platforms, or systems. This can reduce the complexity, cost, and error of data transformation and mapping processes.
  • Develop data culture: Big Data analytics is becoming a vital and ubiquitous aspect of modern organizational life. Organizations are investing trillions to become more data-driven, but only 8% successfully scale analytics to get value from their data. To achieve this, the organization should develop a data culture that fosters data sharing, collaboration, and innovation across different groups and functions.
  • Integrated Systems and Data Integration: The organization also aims to build integrated systems that can communicate and cooperate with each other. To achieve this, the organization should have a plan to migrate its isolated monolith applications to microservices. Monolith applications are applications that are built as a single and indivisible unit, where all the components and functionalities are tightly coupled and dependent on each other. Microservices are applications that are built as a collection of small and independent units, where each unit has a specific function and can communicate with other units through well-defined interfaces. By migrating to microservices, the organization can benefit from increased scalability, flexibility, reliability, and performance of its systems.


Data Silos aren’t necessarily anyone’s fault — it’s just a natural consequence of time, growth, and evolution in a business. Other says data silos happen due to lack of forward planning. This article explores the potential reasons and remedies for data silos.

About the Author

Subhadip Kumar is a seasoned IT professional with over 18 years of experience, currently serving as a Technology Specialist and Architect. Renowned as a leader in the industry, Subhadip brings together extensive technical expertise and a profound understanding of business operations. His contributions as a key industry influencer have been marked by innovative solutions that seamlessly integrate technology and business strategy.

Subhadip’s distinctive approach is characterized by the fusion of technical acumen with a deep comprehension of core business functions. Beyond his IT focus, he has proactively pursued knowledge in the intricacies of the railroad industry. This strategic immersion enables him to adeptly bridge the gap between business objectives and IT solutions.

Throughout his professional journey, Subhadip has demonstrated not only technical prowess but also a commitment to continuous learning and adaptability in the dynamic landscape of the industry. Outside of work, he channels his passion for writing into scholarly journals and blogs. Actively engaged with IEEE and a frequent presence at various conferences, Subhadip remains dedicated to advancing knowledge and contributing to the ever-evolving realm of technology.

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