Data-driven companies need to keep their data flowing throughout their organization. Occasionally, some departments or applications might not share their data, either for technical reasons or because of the organization's structure. When this occurs, it creates a data silo.
Data silos differ from data marts, which offer a department-specific view of the data. Marts are a carefully planned part of the overall data infrastructure; silos are an impediment to the even flow of data.
How Do Data Silos Occur?
Availability is one of the core concepts of data architecture. Every person or process within an organization should have access to all the data they need. However, sometimes data can get isolated in one part of the network for any of the following reasons:
Data silos are often the result of organizational silos within the company. When companies don't have a collaboration culture, teams may not adhere to data sharing policies. In some circumstances, data owners may actively work to retain exclusive access to their department's data.
Silos can also arise because the organization lacks the infrastructure to facilitate data sharing. This can create problems with legacy systems or sharing data between on-premise and cloud systems. Technology-related silos often arise in companies that don't use Extract Transform Load or a similar data integration platform.
Data systems within the organization's network may be incompatible with each other. For instance, a team that works extensively in Microsoft Excel might have trouble integrating with data from a relational database. Similarly, a relational data repository like a data warehouse cannot handle unstructured data, such as text files and images. Issues like these are often symptomatic of a deep architectural problem. They can occur when a company has experienced rapid growth and its infrastructure hasn't yet caught up with its additional needs.
The Dangers of a Data Silo
Most businesses try to avoid organizational silos, as they understand that this kind of partition can negatively influence productivity. Data silos are equally damaging, as they can cause issues like:
Data inconsistency: When one department can't access a record, it will create its own record from scratch. For example, consider a sales department that doesn't share customer data with other teams. If that customer calls in with a query, the service team will create an entirely new record. These two records won't be consistent with each other, which can lead to errors down the line.
Low-quality analytics: Data analytics calls for a 360-degree view of the entire organization, requiring access to as much data as possible. Data silos indicate that the analytics team is missing a piece of the overall puzzle. The resulting insights will be less accurate, which affects the results of data-driven decision-making.
Security risks: When people can't easily access the data they need, they may devise their own workarounds. This can include emailing files or copying data to a USB stick, risking data loss.
Data redundancy: Even when silos don't cause problems, they can still lead to wasted resources. Imagine a company that has two teams working with the same data. If they're not sharing a common version, then both teams will need their own instance of the data. This doubles the cost associated with providing data access.
Lack of visibility: Silos also make it hard to get a bird's-eye view of an organization's data activity. This could have data governance implications, as siloed teams may not be following the organization's guidelines. There are also compliance risks. For example, if a siloed database contains Personal Identifiable Information (PII), the organization might have trouble complying with a Right To Be Forgotten request.
Data-driven organizations consider all their data a single entity. When data exists in a silo, it's part of a different entity, so the organization can't use it correctly. Over time, this can develop into a serious problem.
Solutions to Data Silo Issues
Data silos can pop up in even the most data-conscious companies, which is why the data governance team has to stay vigilant. This could mean conducting regular data audits, reviewing the data infrastructure, and interviewing users about data availability.
When a company identifies a data silos, they can take several steps to remedy the issues:
Improve Data Integration
Silos often emerge when there are fundamental issues of infrastructure. If it's too difficult to integrate a system to the main data pipeline, the relevant IT team might choose to leave the system untouched. The solution is to build a better data infrastructure, using a versatile connector like an ETL platform. This allows the IT team to build a series of one-to-one connections and also to manage the flow of data between locations.
Use Role-Based Data Access Policies
Data siloes sometimes reflect internal policies. For instance, the organization might choose to keep PII or commercially sensitive data in a silo so that it can prevent unauthorized access. This approach can cause issues in the long run. A better solution is to use role-based data access so that each user only has access to information relevant to their job.
Transform Sensitive Data
When the organization needs to keep data secret for compliance reasons, it can obfuscate the sensitive values. Data obfuscation can involve encrypting the data so that authorized personnel can only decrypt it. Obfuscation can also involve replacing confidential data with random characters. This method preserves the data structure while eliminating any sensitive information.
Challenge the Culture
A data silo sometimes indicates a larger issue. When companies don't have a culture of active collaboration, leaders may choose to isolate the data relevant to their department. This is a problem to tackle on a cultural level by encouraging teamwork and trust between all parties. Technology can help by making it easy for teams to share data.