How to identify and manage data silos

Data silos occur when the logic from one part of the business does not match the logic of another. This hinders organizations from making timely, informed decisions. Identifying these silos is important because they often create risk for an organization and hinder organizations from making timely, informed decisions. They are important to identify because they often create risk within an organization. Duplicate data creates data governance issues exposing the organization to security risks. Furthermore, IT costs often increase because data is being stored and accessed in various locations from different business units. One of these departments might use a data warehouse and another might use a data lake and both will have different schemas. This causes varied data management strategies within one organization. This blog will walk you through what data siloes are, what causes them, how to solve them, and how to avoid creating them.

What causes data silos?

Data silos are caused by a mismatch in business logic across different departments. This is a data integration issue. When this happens, the data sets held by each organization become misaligned and inaccessible. In turn, this makes data potentially impossible for the organization to access, use, and drive business decisions.

Where do data silos come from?

Data silos ultimately reflect incongruities in business logic. When departments have their own goals and IT budgets that are separate from the rest of the organization, data siloes can arise. This incongruity is a direct, technological reflection of the divergence in business logic. The lack of an organized data management strategy allows various business units to acquire technology and IT resources to store their data without having the ability to share it with the rest of the organization. 

Regulatory compliance is another reason that data can become siloed. Data exists across data sources, geographies, regions, and deployment models. To help manage risks associated with sharing data across geographies, various regulatory frameworks and laws exist to manage the movement of data. For example, both the European Union (EU) General Data Protection Regulation (GDPR) and the California Privacy Rights and Enforcement Act (CPRA) require companies to enact specific data protection measures when operating within their respective jurisdictions. As a side effect, data can sometimes end up siloed. 

Additional factors like corporate mergers and acquisitions, and the impact of legacy systems containing sensitive information also play a role.  

How data silos reflect poor organizational structure

Data silos develop as a consequence of organizational structure and management. Large organizations have many different business units, and these units have their own budgets and procedures to manage. They are sometimes even viewed as separate entities. However, the impact of organizational structure can lead to data silos, ultimately hindering overall business performance and goals. This problem is exacerbated when a business grows through mergers and acquisitions and has to take on the IT structure of these other organizations.

Why do data silos create risk?

Data silos create risk in two key ways. First, they impede business operations and data analytics initiatives. This limits leadership’s ability to leverage data to manage the business and make informed decisions. Furthermore, data silos create a situation where operational workers and individual contributors lack the access to relevant data to further their respective initiatives.

Incomplete and inconsistent data

Data silos often lead to incomplete or inconsistent data. Because data silos occur when datasets from one business unit become inaccessible to members of other business units, this often leads to incomplete data. In some cases, a given business unit might not even know that the data they want data exists elsewhere in the organization. 

Furthermore, because the business lacks an overall data management structure or strategy, often the same data is inconsistent. Often, different business units rely on the same data sets. However, how they use these data sets might be different and they might be stored in different schemas. So, because the same data can be formatted differently causing data quality issues and affecting the end users of this data.

Duplicate data platforms and business costs

Data silos also cost businesses money. Over time, business units acquire their own data management platforms. As a result, data silos drive up IT costs due to the growing number of servers and storage devices. This inefficient use of IT resources directly hinders management’s ability to make informed business decisions.

Data silos and business culture

In many ways, the problem of data silos is a cultural one. Without a culture where business units share methods in general, creating a culture where they share data is difficult. This not only reduces the opportunities for data sharing but also overall productivity across the business.  In turn, the data problem does nothing to solve the general culture of business siloing that gave rise to it. Siloed data is often bad data, which further erodes trust in the data and furthers a problematic data management culture. Taken together,  a culture that both creates and feeds data siloing results in missed opportunities for the business.

How to identify if you have data silos?

The type of corporate data culture that causes data silos also makes them hard to find. Data fragmentation drives a culture of data where the comparison of data across different business units is outside the norm. To overcome this, IT and data teams should create an inventory of all the current systems and continue to update this as new ones are acquired. This list will make it easier to identify and document data silos. However, your data teams still need to eliminate the data silos you already have. Often, there are clear signs that they are present.

Evidence of data silos

Despite being hard to see at the surface, there are clear signs of data silos:

1. Business units reporting inconsistencies in the data
2. Data science teams not being able to find data
3. Executives complain about lack of data
4. Unexpected IT costs

How to break down data silos

Once you have identified that your organization has data silos that need to be broken down, then your teams can begin to address them. Updating your data management strategy first begins with eliminating your current silos:

• Step 1: Identify where all your data sits, how it is collected, and its relevance
• Step 2: Ask business teams to identify challenges with their data management systems
• Step 3: Integrate data with other systems using ETL, real-time integration, or data virtualization
• Step 4: Consider adopting a data warehouse or data lake as a central repository for all your organizational data
• Step 5: Update your data management culture and governance strategy to prevent new silos from being created

Turning the corner on data silos 

Cultivating a data-sharing culture is crucial to avoiding data silos. Business units within the larger organization should be encouraged to share information and data they acquire with other units. This would mean that the entire organization will have access to this data, limiting data siloes.

If there isn’t an overall IT strategy, IT buying decisions become decentralized and departments often end up purchasing technology on their own, which don’t get integrated into the technology stack of the organization. Furthermore, without an IT strategy, databases and applications are often acquired that are not compatible with other systems or don’t get connected.

Data mesh 

A common way to approach updating your data management strategy is with a data mesh. A data mesh is a decentralized, socio-technical approach to data management. This decentralized data architecture allows data consumers to do some of the data pipeline work that a data engineer would normally do. This means that the ETL process is decentralized within the larger organization. Organizations can then have an agile data strategy. 

One of the pillars of a data mesh architecture is data as a product. This means that data is treated as an organization’s physical product, created for end-user consumption. 

Data virtualization

Another data management approach is data virtualization. This allows organizations to access their data from multiple sources with a single access layer. However, there are physical and performance limitations to this approach. There is only so much data that you can push over a network, which will hinder your network performance.

To accomplish this, you can start with federation. Trino allows you to do this because it can access a variety of data sources. Then, you can do data discovery to learn what databases have the data you want and then you can connect that data to create a view. Lastly, you can query that data in a federated query.

Caching on a data lake

However, the best way to eliminate data siloes is by caching on a data lake. To do this, take the query that combines two sources and create a materialized view on a data lake. Then, cache this query. If you do this, you have a cost-effective solution that utilizes the separation of compute and storage, while maintaining the performance required.

Utilizing Starburst Data Products

Starburst Data Products can take these materialized views and create data products for your end users. You can apply access controls, like ABAC and RBAC, to limit access and maintain security and governance. You can also add business metadata. This additional context makes it easy for end users to find the data they need efficiently while maintaining data governance. 

By using this method, organizations can easily break down data silos, discover their data where it is, cache it on a data lake, and eventually turn it into a data product for easy use for their entire organization.

You can also use data products to decentralize your data architecture, like in a data mesh. By treating data as a product, end users will have access to relevant data in your organization and a consumable fashion.

Summary

In conclusion, data silos pose significant risks by creating obstacles in data accessibility, consistency, and governance, ultimately hampering informed decision-making and increasing operational costs. To address and prevent data silos, organizations must first identify existing silos and work to integrate data systems across departments. Developing a culture of data sharing, supported by robust governance and strategic use of technologies such as data lakes, can help unify data resources and streamline workflows. Leveraging tools like Starburst’s Data Products enables organizations to harness their data more effectively, turning disparate data into cohesive, accessible assets that drive collaboration, reduce inefficiencies, and empower informed, data-driven decision-making across the enterprise.