Removing Silos With Integrated Data Analytics Platforms

Removing Silos With Integrated Data Analytics Platforms

Overview – 

To put it simply, silos are what prevent an organization from achieving its broader objectives, and more than often, at least cause delay in achieving them. Pretty much all sorts of organizations, be it big or small, have faced and struggled to crackdown silos. Several physical strategies, largely driven by upper management, have failed to prevent the buildup of silos.

On this note, a data analytics platform that is integrated in nature works out to be a big plus for any organization. Having data from different departments in an organization to be visible and accessible to any department helps in several ways. It allows for one department to access and work on the data generated from another department, thereby reducing any kind of redundancy and repetition of work.

Silos – What are they?

By definition, a silo is the data held by one department that is not fully visible or accessible to other departments of the same organization. Silos happen to exactly be the opposing force to integration.

Think of this in the context of several departments in an organization, like HR, Marketing, Sales, Finance, Administration, etc. These departments work 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 in nature as time passes by 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.

What causes a siloed structure?

Before the likes of Big Data took to the world, it was encouraged that various departments in any organization manage their own data. Since each department has its ways of working and policies and rules, ‘each to his 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 requirement. As a result, data silos were automatically built up. And, it was never considered as a problem. Now, with the revolution of Bigdata, cloud infrastructure, analytics – more insight is a need of the hour. So, business is more concerned to remove the data silos and extract meaningful insight for future growth.
  • Company culture – Attributed to the above point, several functional departments in organizations are accustomed to work in their own worlds. Since they have their own challenges and styles of working, they work distinctly from other departments and this also cats its shadow on their data. Also, departments have hardly ever been encouraged to unify their data.
  • Technology – Many of the legacy systems that organizations tend to use weren’t exactly built to share data easily. The uses of such tools have only but pushed departments into having data silos.

Why are data silos harmful to organizational objectives?

Competition and the need for profitability has been the driving force for all organizations these days to minimize costs and use 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 in the organization 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 availability of inconsistent and inaccurate data.
  • 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 hence, is a waste of resources.
  • Discourages collaboration – Data silos discourages collaboration between departments in an organization as there is no sharing of data involved. Data driven organizations, these days, are relying on integration of data to obtain powerful insights that are further helping them grow their business.

How to get rid of 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. Besides, these are time efficient in their working as well.

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 on the cloud. This central repository will aid in the process of streamlined analysis. In this manner, disparate data maybe homogenized and integrated.

Integration of data

Integration of data in an effective and accurate manner is the best possible way to breakdown the formation of silos. 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 lead to increased complexity and so, a cost and time burden for IT professionals.

  • Using ETL tools locally

ETL, as in extract, transform and load, 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.

  • 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

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 to 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. Corporates 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, an Integration Platform as a Service (iPaaS) 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 the business insight. The better the insight, the better the opportunity to grow. As a result, the organizations are more concerned to integrate the data and grow faster. Data integration tools and cloud based solutions makes our life easy to break the data silos forever.

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