Destroying Silos With Integrated Data Analytics Platforms

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An integrated data analytics platform can foster collaboration between disconnected departments, enhancing productivity and efficiency.

Silos prevent an organization from achieving its broader objectives, or at the least cause delay in achieving them. Pretty much all sorts of organizations, be it big or small, have faced siloing and struggled to break them down. Despite being aware of the harm silos can cause and the advantages of preventing them, they still happen.

An integrated data analytics platform works out to be a big plus for any organization. Having data from different departments in an organization be visible and accessible to any department is the goal. It allows for one department to access and work on the data generated from another department, thereby reducing redundancy and repetition of work while maximizing efficiency and interdepartmental communication and cooperation.

What is a Silo?

"Silo" refers to the data held by a department that is not fully visible or accessible to other departments of the same organization. Siloing can be seen as the exact opposite of integration.

Think of this in the context of several departments in an organization, like HR, Marketing, Sales, Finance, Administration. Each department works to meet their functional goals, and in a broader context, work towards organizational goals. Now, if these functional departments were to store their respective data separately, then they form data silos.

These silos tend to grow as time passes and more data is added to them. The different departments, being disconnected from one another, serve as the perfect cause for zero communication between all of this departmental data.

Furthermore, due to such isolation between the departments, there is every chance of having redundant work, leading to wasted effort and expenses. Hence, this entire existence of silos works rather unfavorably for the organizational and hinders it from achieving it objectives. (Read also: Big Data Silos: What They Are and How to Deal With Them.)


What Causes a Siloed Structure?

Before the likes of Big Data took to the world, various departments in any organization were often encouraged to manage their own data. Since each department has its ways of working and policies and rules, ‘each to their own’ was the apt way to look at it. This was one of the primary reasons for the formation of silos.

  • Organization structure – In an organization, different departments have their own structure, process and policies. So, they used to manage their own data as per their own specific requirements. As a result, data silos were automatically built up. And, it was never considered to be a problem. Now, with the revolution of Big data, cloud infrastructure, analytics – more insight is a need of the hour. So, business is more concerned with removing the data silos and extracting meaningful insight for future growth.

  • Company culture – Tied to organization structure, departments are accustomed to work in their own worlds as a function of company culture. Since they have their own challenges and styles of working, they work distinctly from other departments and this can cast its shadow on their data. Also, departments have rarely been encouraged to unify their data – often there just wasn't a need seen for it.

  • Technology – Many of the legacy systems that organizations tend to use weren’t built to share data easily. The use of such tools and continuing to do things "the way they've always been done" have only pushed departments into creating and maintaining data silos.

  • Scalability – Growth and change in a company can also lead to data silos. What worked for a small start-up with only a few employees won't work for a scale-up or a business that has grown exponentially. Once a company moves from a person doing a role to a team or department with those responsibilities, data sharing must be approached in a different way.

Why are Data Silos Harmful to Organizational Objectives?

Competition and the need for profitability has been the driving force for business. It is important to minimize costs and maximize their data resources. However, data silos are the exact things that stand in the way of such utilization.

  • Limitation in data view – Silos prevent sharing of data between various departments, which means departmental analysis is limited by its own view. This prevents the discovery of any enterprise-wide inefficiency.

  • Threat to data integrity – Siloed data is stored in different databases and that can result in the inconsistent and inaccurate data availability.

  • Waste of resources – The presence of silos is waste of resources. Storing redundant data and the resources required to maintain and access them can be an additional burden and eats up resources that could be used more efficiently elsewhere.

  • Discourages collaboration – Data silos discourages collaboration between departments within an organization as there is no sharing of data involved. Data-driven organizations rely on integration of data to obtain powerful insights that are further helping them grow their business.

Breaking Down Silos

The methods to get rid of silos are both technical and organizational. With the advent of the cloud, there are integrated data analytics platforms that help organizations get the best out of their data. Additionally, these platforms are time efficient and an effective use of resources.

Change in Organization Culture

Since company culture is a cause that leads to the creation of silos, it is also what holds the key to getting rid of them. Encouraging sharing of data from a management level can inherently change the way employees look at data sharing. The positives that come out of data integrity must be effectively communicated so that they may also be incorporated in employees’ daily work practices.

Centralization of Data

The simplest means to have all the data in one place is to pool all business data from different departments into a data warehouse that is based in the cloud. This central repository will aid in the process of streamlined analysis. In this manner, disparate data maybe homogenized and integrated. (Read also: Data Center Transition Operations Plan: A Critical Strategy.)

Integration of Data

Integration of data in an effective and accurate manner is the best possible way to break down silos and prevent them from forming in the first place. Such a task can be carried out by:

  • Scripting

IT departments in organizations can be entrusted with the writing of scripts in scripting languages, such as Python, to move data into warehouses from siloed sources. This process does have a disadvantage though as it can become highly complex with time. A growth in the number of data sources leads to increased complexity and so, a cost and time burden for IT professionals.

  • Using ETL tools locally

ETL (extract, transform and load) tools, are used to automate the process of moving data from sources to the data warehouse. Locally, this is implemented by transforming and moving data from various sources to the data center of the organization. (Read also: 4 Ways AI-Driven ETL Monitoring Can Help Avoid Glitches.)

  • ETL tools on the Cloud

The cloud and data tend to go well together and several cloud-based providers have also been providing faster ETL processes these days. Making use of the service provider’s infrastructure and expertise, ETL tools are efficiently designed to work in such an environment. They offer a streamlined process for data analysis and also an integrated solution bereft of data integrity issues.

Busting Data Silos with Integrated Data Analytics Solutions

As the cloud has evolved into a natural space for the centralization of data, there are several companies that offer integrated data analytics as a product for large, mid and small sized firms. These are largely beneficial to organizations that may not have the resources in-house to manually get rid of silos.

  • Snowflake is one of the most prominent services that have been around. The service that they offer is essentially termed as data warehouse-as-a-service. Corporations can use the cloud to store and perform data analysis.

  • Cloudera is another well-known service that offers working across on-premise, hybrid, and multi-cloud architectures. It uses machine learning and analytics to obtain insights over a secure connection.

  • Databricks, founded by the creators of Spark is a product that is turning a few heads. Projects like Delta Lake, MLflow and Koalas undertake domains of data engineering, data science and machine learning. Databricks has a web-based platform that works with Spark.

  • Talend Data Fabric is one of the most popular tool to centralize the data in the cloud. It simplifies ETL process, data governance, compliance and security. Talend data fabric enables users to collaborate and bust silos across departments.

  • Mulesoft is an “Integration Platform as a Service (iPaaS)” software. It is the other solution for high-quality data integration. It also ensures automatic data upload from different sources.


Data silos are very common across different organizations. It was not treated as a problem in earlier days. But, with the introduction of big data and cloud, it becomes very important to break the data silos and extract business insights easily and effectively.

The better the insight, the better the opportunity to grow. As a result, the organizations are more concerned with integrating data and growing faster.

Data integration tools and cloud based solutions makes our lives easier to break the data silos forever.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…