Data Silos: Why are they a problem?

Although organizations don’t set out to intentionally create data silos, they are likely to arise naturally over time. In many cases, leaders aren’t even aware that they exist within their own business. This can make collaboration across departments difficult, leading to inconsistent data quality, a lack of communication and visibility, and higher costs over time (among other issues). To effectively fight back against this problem, business leaders need to take a step back and reassess their current procedures and operations, company culture, and technology stack.

What are Data Silos?

A data silo is a repository of data that is controlled by one department or business unit and isolated from the rest of an organization, much like grass and grain in a farm silo are closed off from outside elements. Siloed data typically is stored in a standalone system and often is incompatible with other data sets. That makes it hard for users in other parts of the organization to access and use the data.

Data silos can have technical, organizational, or cultural roots. They tend to arise naturally in large companies because separate business units may operate independently and have their own goals, priorities, and IT budgets. But any organization can end up with data silos if it doesn’t have a well-planned data management strategy.

What causes Data Silos?

Organizational Structure creates Data Silos

As a company evolves, they often add new technology, SaaS services, or a separate data warehouse. Different functional units may have their own database or pull from different data sources. It becomes common to have multiple information systems within the same business that are not tied together in a meaningful way.

Company Culture

Some companies have developed a hierarchy and layers of management within their business that simply do not support the sharing of information. Internal competition, especially in large organizations, can prevent embedding data throughout an organization and lead to organizational silos.

Often, information isn’t shared because one of the teams in the organization doesn’t understand how other teams would benefit from the data. As such, data silos emerge, even if unintentional.

Technology that is not integrated

In some organizations, different work units will use different technology, which can make it difficult to share common information. For example, the sales team might be using Salesforce, Microsoft Dynamics 365, or HubSpot as their sales hub, but the marketing team or finance team doesn’t have access to the application or its information.

The average company is now using 80 different SaaS applications. It’s easy for these to become data silos without a plan for data integration.

Growth Over Time

Over time, organizations grow. With more employees, branches, and offices, it can create structural divisions. As more people join the organization, they may bring different approaches to data. While this can lead to new insights, it can also have a negative impact if there isn’t a strong data management plan in place. The larger the organization, the more likely it is to create data silos, whether it happens organically or whether teams want to hold on to information to maintain control.

How do they affect organizations?

Departments may operate separately, but they are interdependent on many levels. For example, data that comes in from the finance department can be analyzed by the marketing and sales departments. The desire to gain an edge over the competition, improve operational efficiencies, and open up new business opportunities while cutting down on costs pushes organizations to achieve more with their data. For this, access to enterprise-wide information is key. Data silos can hamper this way forward.

Limiting the View of Data

Since silos prevent the sharing of information, every department’s analysis remains contained within itself. Any inefficiencies that may be widespread in an enterprise will not come to light if data is not shared across all stakeholders. All opportunities to find ways to reduce operational costs are thus lost.

Threatening Data Integrity

Data silos cause inconsistencies in departmental data. With time, each occurrence leads to inaccurate and useless data. This is often seen in the medical field when patient information is stored in multiple silos such as doctor summaries, nursing protocols, medicine intake, and procedural notes. When data silos are not connected, they tend to go out of sync and result in widespread discrepancies.

Wasting Resources

Multiple sets of data (often duplicates) burden the financial resources of the company allotted to storage space. When various departments download this information, the resource quality suffers.

Discouraging Collaborative Work

A company’s working culture drives the creation of silos, which in turn reinforces a non-collaborative culture. Data that is difficult to access reduces collaborative efforts.

How do you break down Data Silos?

Eliminating data silos enables an organization to manage and use data more effectively. It often also helps lower technology and data management costs. The following approaches can be used separately or in tandem to remove silos and connect data assets to better support business operations:

• Data integration. Integrating data silos with other systems is the most straightforward way to break them down. The most popular form of data integration is extracted, transform, and load (ETL), which extracts data from source systems, consolidates it, and loads it into a target system or application. Other data integration techniques that can be used against silos include real-time integration, data virtualization, and extract, load, and transform, a variation on ETL.
• Data warehouses and data lakes. The most common target system in data integration jobs is a data warehouse, which stores structured transaction data for BI, analytics, and reporting applications. Increasingly, organizations also build data lakes to hold sets of big data, which can include large volumes of structured, unstructured, and semistructured data used in data science applications. Those two types of platforms provide centralized repositories for data from different systems, making them a natural way to address silos.
• Enterprise data management and governance. Ultimately, it’s best to not only eliminate existing data silos but also prevent new ones from being created. A more comprehensive data management strategy helps achieve both of those goals. For example, data architecture design documents data assets maps data flows, and creates a blueprint for data platform deployments. An enterprise data strategy better aligns the data management process with business operations. And a strong data governance program can directly reduce the number of data silos in an organization and promote common data standards and policies.
• Culture change. To really put a stop to data silos, it may be necessary to change an organization’s culture. Efforts to do so can be part of the data strategy development process or a data governance initiative. In some cases, a change management program may be needed to implement the cultural changes and ensure that departments and business units adopt them.

Conclusion

Data silos can happen naturally in an organization because of structural, technical, and cultural problems. For many companies, breaking down data silos and combining all of their operational and experience data is a top priority. In this article, we looked at several reasons why silos of data are a problem.

The problems caused by silos of data may seem complicated, but you don’t have to be perfect to see the benefits. Start small, find data silos, and eliminate old or wrong data. Once you have a good handle on your current environment, you can start making changes to your infrastructure, such as reducing the number of applications, integrating systems, and encouraging collaboration.