A data silo is a repository of information. Data silos are often used to store digital assets. Typically, a data silo has a single purpose and is not accessible by other entities or programs. In the enterprise, IT teams prefer to use centralized databases with open APIs, which can be accessed by employees, partners and authorized applications.
Data silos can be problematic because they tend to be isolated and proprietary storage systems that are only accessible by a small group of people and perhaps one or two applications. For example, some large enterprises may use dozens of different marketing automation software tools; each of these tools will likely have its own database used to store customer information and marketing campaign results. These tools may not be able to access each other's databases, and, as a result, it would be difficult for the enterprise's managers to get an accurate picture of how well their marketing campaigns are performing across the board.
Data silos typically exist in large enterprise environments where there have been many acquisitions or mergers. In such scenarios, the IT department may have little control over which specific software products are being used by employees in various departments.
In the IT world, data silos describe the barriers between different groups of people's access to data or a system. For example, your company's sales department may have a separate database from the accounting department, making it impossible for those two groups to work together to share data about customers and sales. Worse yet, it's impossible for Business Intelligence to make sense of both sets of data, especially without the help of both parties involved and a data analyst or engineer. Data silos can also refer to applications or sets of data that don't communicate with each other and are only used by one group of people.
In short, data silos aren't in themselves bad, but typically have a negative impact on the cohesion of a business. To maintain a virtual environment that is accessible and transparent, organizations should work to avoid data silos.
Data silos happen for many reasons, including:
There are two kinds of data silos:
Organizational: This happens when departments within an organization have different information systems and don’t share their data. For example, the marketing team may have one tool for analyzing demand, while the sales team has another. Because they’re not working with the same data, they can’t share insights.
Technical: This happens when companies use different software to store their data and can’t access it through a common interface. For example, a company may use one tool to analyze customer service logs and another tool to analyze product usage logs.
Data silos create duplicate work, waste time and money, and prevent organizations from learning from their own experiences.
The cost of siloed data is hard to calculate as it's entirely dependent on how a company interacts with its data, how robust the data collected is, and the scale and volume of the data. However, some costs to consider if your data is siloed are both related to time and money spent:
Organizations, especially those with growing teams should prioritize managing their data in such a way that no data becomes siloed. This means that every function and team that is collecting data for their own purposes are aware of how the other teams are doing so, and ideally, they're doing so with each other in mind. Businesses can do this by finding all-in-one data solutions that are cross function, so, for example, both the finance team and the marketing team are collecting data on the same customers, in the same platform. How they're categorizing and organizing this data is in alignment with each other. On top of this, they're doing this with the guidance and frameworks laid out by the data stewards in their organization (typically data analysts and engineers).