An organization comprises different departments working on diverse goals and objectives. Each department requires a distinct set of data to work upon and hence stores its data in an independent and isolated system. Such systems are called Data silos.

In the past and to some extent even now, companies are collecting data about a single business initiative or application. It would store that data in one place, and it would use that data to drive decisions about that initiative or application.

This led to a situation where every business function and every application created its own data model and its own data repository. That led to a huge proliferation of data within the organization.

Data silos are storehouses of data accessed by only one department or team. Similar to the silos used in farms to separate grains from cross-contamination, Data silos used to store data restrict cross-communication with other systems.

Sharing of data, transparency, and collaboration becomes difficult among departments and thus increases inconsistencies. Data available to us must be easy to access by various departments to make data analysis accurate and reliable, but data silos prevent such sharing.

Preventing data silos is thus crucial for effective decision-making and discovering future opportunities for an organization.

HOW DO DATA SILOS AFFECT AN ORGANISATION?

1. Incomplete data: Since data silos restrict data sharing among various systems, all the required information is not available for analysis. It affects overall decision-making, the working of operations, and data integration activities. 
   
2. Data Redundancy: Even if different departments require the same information, they create a separate system for themselves. Duplicate data is stored and managed by different departments resulting in increased IT costs and efforts.
   
3. Reduced cross-communication: Data silos are isolated systems and reduce interaction among departments. It prevents effective communication and data sharing which are essential for data analysis.  
   
4. Data inconsistency: Inconsistent data occurs when one department stores the data in a format not supported by other departments or when systems do not reflect data updates or corrections made in siloed data systems.
   
5. Difficulty in data management: Inconsistencies, reduced data sharing, redundant data, degrades data quality, and hinders data governance practices leading to overall complications in data management

WHY DATA SILOS ARE CREATED?

1. Business Expansion: When an organization starts expanding its business in various fields, various departments emerge that work individually. These departments have their own budget, objectives, roles, and independent data storage system. 
   
2. Competition among departments: A competitive environment may arise among departments of the same organization, preventing them from sharing information. They tend to use practices that keep their data isolated and secure, and as a result, they create data silos. 
   
3. Decentralized IT Services: Different departments of an organization are allowed to work on and purchase their own software which leads to incompatibility among the data collected by these departments.
     
4. The difference in Technologies: Different departments might work on different technologies according to what suits them the best. It becomes difficult for teams to access or work on data from other departments if the technologies they work on are different.

HOW CAN WE REDUCE DATA SILOS?

1. Change is management style: If the current management style of a company is resulting in the creation of data silos, then the problems must be addressed, discussed, and solved. The necessary changes in management must take place in order to increase communication between different departments.
   
   
2. Data Integration: Developing methods to store data from various departments at one source like data warehouses can decrease the number of silos an organization creates. This will increase accessibility, transparency, and communication.
   
   
3. Centralize Data: Storage of data in a centralized data warehouse or data lake which grants easy access to stored data to teams and members of various departments for effective analysis.
   
   
4. Using Data Fabric: Data Fabric is a type of unified architecture that helps in the better management of data. It provides access to data to members of the organization irrespective of their team or group and solves the issue of inaccessibility and isolation of data.

HOW DATA FABRIC HELPS IN ELIMINATING DATA SILOS?

The issue with the creation of data silos is how it makes different departments incompatible with each other. This hinders data governance, data quality, and hence overall data management of an organization. All this drastically affects an organization as it makes decision-making and other important operations less efficient.

Since the context in which data is consumed is changing, companies now want to use the same data across multiple initiatives or applications. So they have to collect the same data, cleanse it, and govern it differently.

Over the last few years and it will be a trend in the future too, developments within the hybrid cloud, AI, IoT, real-time, social media, and edge computing have led to the exponential growth of data, creating even bigger data silos, security, data management issues, and other complexities for companies to manage.

In this situation, it is imperative that data is all at one place with greater automated data governance in place for all the data environments to analyze the data for enterprise at one go.

The need of the hour is an intelligent data fabric that follows an architecture that facilitates the end-to-end integration of various data sources, on-premise or on the cloud through the use of automated systems and focuses on governance and data quality at the same time. Gartner calls Data Fabric Architecture is Key to Modernizing Data Management and Integration.

To solve Data Silos and at the same time cater to the future, a unique architectural approach is used called Data Fabric. The aim of Data Fabric is to create an integrated environment where data can be stored and accessed by various departments in an organization whilst being independently collected.  The goal of Data Fabric is to create better customer experiences, develop better services as well as upgrade the efficiency of operations.

This approach is an advanced version of traditional integration systems. It fulfills the requirement of real-time connection, transformations, automation, self-service, and security, and provides a unified environment for an organization.

As the name suggests, Data Fabric can be imagined as a piece of massive clothing, spread all over the world wherever the organization’s users exist. Being on this fabric, the user can access data from any location without any restriction in real time.

The importance of data fabric can be understood with the claim made by Gartner for Data Fabric to be the top 10 data and analytic trends of 2021. They also predict that by 2024, 25% of data management vendors will provide a complete framework for data fabric.

With the massive amounts of data being collected every day by organizations and for using it for analysis and effective decision-making, Data Fabric is a platform where this ever-increasing amount of data can be integrated and communicated across users connected over a network.

SCIKIQ is a first-of-its-kind AI-driven business data fabric that delivers a trusted and real-time view of data across an enterprise in days or weeks instead of months and years by integrating and governing data from multiple data stores and business applications to deliver the right data, at the right time and in the right format to its data consumer.

ScikIQ helps Enterprises to transform, disrupt and simplify the existing data landscape & stay ahead of the competition by becoming a Data-Driven Organization where “DATA� is a key Asset Class & responsible for generating newer Revenue Streams through the introduction of a new breed of “Data Products�

SCIKIQ combines different data integration design patterns and utilizes active metadata, knowledge graphs, and Machine Learning to augment data integration and data delivery tasks, across all environments, including hybrid and multi-cloud platforms.