Silos prevent an organization from achieving its larger goals, or at least delay their achievement. Almost all kinds of organizations, whether large or small, have faced silos and struggled to break them down. While we are aware of the damage silos can cause and the benefits of preventing it, it still happens.

An integrated data analytics platform is a big plus for any organization. The goal is for data from different departments of an organization to be visible and accessible to all departments. It allows one department to access and work on data generated by another department, reducing redundancy and duplication of work while maximizing efficiency and inter-departmental communication and cooperation.

What is a silo?

“Silo” refers to data held by one department that is not fully visible or accessible to other departments in the same organization. The silo can be seen as the exact opposite of integration.

Think of this in the context of multiple departments in an organization, such as HR, marketing, sales, finance, administration. Each department strives to achieve its functional goals and, in a larger context, to achieve organizational goals. Now, if these functional departments were to store their respective data separately, then they form data silos.

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

In addition, due to such isolation between departments, there is every chance of having superfluous work, which leads to wasted effort and expense. Therefore, this whole existence of silos works rather unfavorably for the organization and prevents it from achieving its goals. (Also read: Big Data Silos: what they are and how to manage them.)

What are the causes of a silo structure?

Before big data spread to the world, various departments in an organization were often encouraged to manage their own data. Since each department has its own working methods, policies and rules, “beggarly” was the most appropriate way to look at it. This was one of the main reasons for the formation of silos.

  • Organizational Structure – In an organization, different departments have their own structure, processes and policies. Thus, they used to manage their own data according to their own specific needs. As a result, data silos were automatically created. And that was never seen as a problem. Today, with the revolution of big data, cloud infrastructure and analytics, more insight is urgently needed. Thus, companies are more concerned with breaking down data silos and extracting meaningful insights for future growth.

  • Corporate Culture – Linked to the organizational structure, departments are used to working in their own world depending on the corporate culture. Since they have their own challenges and working styles, they work distinctly from other departments and this can cast a shadow over their data. Additionally, departments were rarely encouraged to unify their data – often it just wasn’t necessary.

  • Technology – Many legacy systems that organizations tend to use were not designed to easily share data. Using such tools and continuing to do things “as they always have been” have only pushed departments to create and maintain data silos.

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

Why are data silos detrimental to organizational goals?

Competition and the need for profitability have driven business. It is important to minimize costs and maximize their data resources. However, data silos are exactly what stands in the way of such use.

  • Limiting the view of data – Silos prevent the sharing of data between different services, which means that the analysis of the service is limited by its own view. This prevents the discovery of any company-wide inefficiencies.

  • Threat to data integrity – Siled data is stored in different databases and this can lead to inconsistent and inaccurate data availability.

  • Waste of resources – The presence of silos is a waste of resources. Storing redundant data and the resources needed to maintain and access it can add additional load and consume resources that could be used more efficiently elsewhere.

  • Discourages collaboration – Data silos discourage collaboration between departments within an organization because there is no data sharing involved. Data-driven organizations rely on data integration for powerful insights that further help them grow their business.

Break down silos

The methods of getting 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 most out of their data. In addition, these platforms save time and use resources efficiently.

Change in organizational culture

Corporate culture being a cause that leads to the creation of silos, this is also what holds the key to getting rid of it. Encouraging data sharing from a management level can fundamentally change the way employees view data sharing. The positives that arise from data integrity need to be communicated effectively so that they can also be incorporated into the daily work practices of employees.

Data centralization

The easiest way to have all the data in one place is to consolidate all the business data from different departments into a cloud-based data warehouse. This central repository will facilitate the streamlined analysis process. In this way, disparate data can be homogenized and integrated. (Also read: Data Center Transition Operations Plan: A Critical Strategy.)

Data integration

Integrating data efficiently and accurately is the best possible way to break down silos and prevent them from forming in the first place. Such a task can be performed by:

IT departments in organizations may be tasked with writing scripts in scripting languages, such as Python, to move data into warehouses from siled sources. This process has a downside, however, as it can become very complex over time. An increase in the number of data sources results in increased complexity and therefore a burden in terms of costs and time for IT professionals.

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 organization’s data center. (Also Read: 4 Ways AI-Based ETL Monitoring Can Help Avoid Problems.)

Cloud and data tend to mix well, and several cloud-based vendors are also providing faster ETL processes these days. Using the infrastructure and expertise of the service provider, ETL tools are efficiently designed to operate in such an environment. They offer a streamlined process for data analysis and also an integrated solution devoid of data integrity issues.

Break down data silos with integrated data analytics solutions

As the cloud has become a natural space for centralizing data, several companies are offering integrated data analytics as a product for large, medium and small businesses. These are of great benefit to organizations that may not have the in-house resources to manually get rid of silos.

  • Snowflake is one of the most important services that have been around. The service they offer is basically referred to as a data warehouse as a service. Businesses can use the cloud to store and analyze data.

  • Cloudera is another well-known service that offers to work on on-premises, hybrid and multi-cloud architectures. It uses machine learning and analytics to get information through a secure connection.

  • Data bricks, founded by the creators of Spark is a product that turns some heads. Projects like Delta Lake, MLflow, and Koalas address the areas of data engineering, data science, and machine learning. Databricks has a web platform that works with Spark.

  • Talend Data Fabric is one of the most popular tools for centralizing data in the cloud. It simplifies the ETL process, data governance, compliance and security. Talend Data Fabric enables users to collaborate and break down silos between departments.

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

Conclusion

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

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

Data integration tools and cloud-based solutions make it easier for us to break down data silos forever.