概要
Answering the question "What is a data silo?" at first seems easy. A data silo is any database, set of files, or other repository of data that is overseen by a single department or business unit and isolated from the rest of the organization and its data architecture.
On the surface, that might not seem like much of a problem. But in an era where interconnectedness is more important than ever for any enterprise, data silos can be quite troublesome—as such, some consider silos to be among the more troublesome data trends affecting today's organizations. Here, we'll look at how data silos show up in the first place, the challenges they pose, and the best practices and cloud-based tools that can help minimize the risks of siloing.
How do data silos form?
The circumstances described below are some of the most common causes of data siloing.
Outdated approaches
To some extent, data silos became common because a great deal of traditional technology infrastructure—data-related and otherwise—was siloed to some degree. While the classic data center aggregated all of the enterprise's backend systems, including servers, mainframes, and databases, into a centralized whole, the data itself was separated among infrastructure dedicated to different departments.
In the early days of information management, the leaders of different enterprise business units likely thought it made sense to have devoted, private data infrastructure for their departments—finance, HR, marketing, customer relations, and so on. But as organizations grew, and became more reliant on cross-departmental cooperation and cross-functional teams, the access issues of silos became obvious. Nevertheless, organizational silos are still far from uncommon in numerous enterprises.
Differing technologies
Data silos also sometimes form when different departments use different technologies, especially for their data management needs. This may be connected to team leaders encouraging their business units to function autonomously, making silos all but inevitable. Yet regardless of the driving force behind siloing, data sharing is much more difficult when silos have formed.
Mergers and acquisitions
Data silos can form as a result of mergers and acquisitions if a newly acquired or merged-with organization's data resources aren't quickly integrated into the parent enterprise's data ecosystem. If data teams neglect to take this step, old, incompatible systems and resources will cause interoperability problems.
What are the risks of siloed data?
By its very nature, data that is siloed cannot be accessed easily or at all by those in other departments. This limits the ability of teams to collaborate, and also infringes upon enterprise leaders' ability to have a genuinely comprehensive view of the organization's data ecosystem.
Consider these more specific examples of "unforced errors" siloing can cause:
Inefficient analytics
Imagine that it's time for the latest analytics project focused on comparing the performance of different business units. It will be extremely difficult for data teams to derive broad insight through analytics initiatives if the analytics tools used by data science and analysis personnel can't access certain departmental data due to silos. The onus will be on members of those departments to get analytics professionals the data they need, making the process incredibly inefficient.
Impaired interdepartmental collaboration
Marketing and customer service departments both regularly need access to data from many other business units—ranging from product data sheets and historical sales records to research and development data—for various reasons. If other departments have silos, these teams' jobs will be much harder than they need to be. Even a system like a customer data platform designed to prevent siloing can turn into its own silo if it's not properly managed. Moreover, a silo mentality may also discourage teams from wanting to collaborate.
Inconsistencies and other risks
Siloed data can lead to incomplete data sets, inconsistencies, redundancies, inefficient use of resources, and a decline in overall data integrity. Security risks may also present themselves if siloed departments don't use tools with adequate security measures, or if team members access siloed data using shadow IT devices.
How to overcome data silo limitations
Breaking down data silos isn't something that can happen overnight, and if an enterprise's silos have been in place for a long time, the process may be difficult. Communication and collaboration will steadily improve when reliable data sharing is enabled through de-siloing, and business leaders will be able to have full visibility into the enterprise and its data ecosystem.
A simple way to begin a campaign to deconstruct silos is for the data team to conduct a thorough audit of the organization's data architecture, looking for siloed data. Strong indicators of a data silo's presence include:
- Data that should be discoverable but isn't—e.g., customer data from the sales department that can't be accessed
- Incomplete data sets
- Inconsistent reporting between different departments
- Data management costs for one or more departments that are radically different from others—usually higher than average
Once data professionals have found the organization's silos, they can begin the process of breaking them down and incorporating their data resources into the larger architecture.
The importance of data integration
Data integration is extremely helpful when looking to eliminate silos. Integration will allow the enterprise to ingest data from many different sources—regardless of file formats or any other differentiating characteristics—and unite it through the extract, transform, and load (ETL) process.
Object storage and data warehousing
Given the scope of the data associated with any integration project meant to break up data silos, it makes sense to use low-cost, cloud-based object stores in the cloud. These can be the basis of a data lake to serve as a central repository for once-siloed data. But if undertaking this approach, operate a reliable and effective data analytics and warehousing platform with integration capabilities atop the data lake. This will help ensure the data lake doesn't become disorganized or ungovernable.
API connections
Using application program interfaces (APIs) is another fairly simple but valuable way to help prevent siloing. APIs keep systems and apps actively communicating and sharing data through the use of a common format, and most enterprises will already be using them to some degree. However, in the interest of breaking down silos, data teams must make sure that APIs are adopted and implemented throughout the entire organization.
Strong data governance
Firm governance is essential to preventing the buildup of silos, as it establishes standards for data management with a focus on sharing. Going forward, data teams, chief data officers, and other relevant stakeholders must endeavor to design and maintain a data ecosystem that does not lend itself to the development of silos.
Data silos that developed as a result of issues with organizational or departmental culture will have to be addressed as well. While there can be value in departments running autonomously without excessive upper-management supervision, each business unit should be willing and able to share its data when necessary.
Cloud-based data architecture and tools help prevent siloing
While it's certainly possible to use tools like a data warehousing platform and data lake on-premises, those resources can have considerably more flexibility and utility when deployed in the scalable and resource-rich context of a cloud deployment.
The cloud's unlimited scope gives data teams the freedom to craft the architecture necessary for a data ecosystem that promotes data integration and sharing while minimizing the formation of silos and the risks they can present. Object storage that can expand and contract as needed to allow for the ingestion of structured and unstructured big data. Meanwhile, implementing a warehousing and data analytics platform like Teradata Vantage in a hybrid cloud architecture allows integration from both cloud-based and data center sources, further limiting the likelihood of silos forming in either setting.
Teradata Vantage is ranked highest across all use cases in the latest Gartner Critical Capabilities for Cloud Database Management Systems for Analytical Use Cases report—data warehouse, logical data warehouse, data lake, and operational intelligence. To learn more, read the Gartner report today.
See the Gartner Critical Capabilities Report