In general, a data silo is a repository of fixed data. The data remains under the control of one department and is isolated from the rest of the organization.
Today, we will discuss how data silos in Power BI affect data-driven organizations.
The Power BI dataflows, Common Data Models, and the Azure Data Services utilized together to break down and open silos of data information in your organization. And, they enable business analysts, data engineers, and data scientists to share information to fuel progressed examination and open new bits of insights to give you a competitive edge.
How Do Data Silos Occur?
Data silos happen for three common reasons, as follows:
- Company culture: In large companies, the departments are often siloed from each other. Sometimes this occurs because there is internal competition, but often it happens because one department sees itself as separate from another and doesn’t consider where information should be shared.
- Organizational structures: Unless an organization specifically works to integrate different departments, it’s easy to build layers of hierarchy and management that deter departments from sharing information.
- Technology: It’s not uncommon for different departments to use different technology, making it difficult for the departments to share common information.
The Main Reasons Silos Arise:
- Structural: If the application built for a specific use or department and data-sharing isn’t a requirement. The data remains isolated from the other department.
- Political: If there’s a sense of proprietorship over a system’s data. Therefore, it is not readily shared with others due to security purposes.
- Growth: Over the ages, when the new technologies are added, and they are ultimately incompatible with existing systems and data sets, silos arise.
- Vendor lock-in: If the technology vendors don’t give sufficient data access to their customers, it isolates the data.
Why Are Data Silos a Problem?
Data silos are a problem for the following three reasons:
- Inability to get a comprehensive view of data. If your data is siloed, relevant connections between siloed data can easily be missed. Suppose, for example, the Marketing team has excellent data on which Marketing campaigns attracted a lot of attention in a particular geography. On the other hand, the Sales team has information about sales in that same geography. What if you could bring that information together? Imagine how much clearer the relationship between Marketing campaigns and sales would be.
- Wasted resources. Consider what happens if you have a database with customer information for the Marketing team and a separate one for the Sales team. The data will probably be duplicated. It costs money to store all this data, and the more data a company stores, the less the organization can spend on other requirements.
- Inconsistent data. In data silos, it’s common to store the same information in different places. When this happens, there is a high chance that you will introduce data inconsistencies. You might update a customer address in one place and not another. Or, you might introduce a typo in one set of information. When the data is in one place, you have a much better chance of maintaining the correct information.
Challenges in Dealing with Siloed Data
While many companies recognize that data silos are a problem, undoing them can be a challenge. Once you have an established culture of separating data, it becomes a mindset employees have. Needless to say, it’s a challenge to change the mindset of employees.
Moreover, it might be hard to fix a portion of the silos because of how that system is set up with various permission and hierarchies. For example, permissions are often set up by the group, so once the data is siloed for a group, it’s hard to change all the necessary permissions.
If the data is siloed in different systems (for example, data for the Security Operations group is stored in an Oracle database, while the Sales information is stored in Salesforce), it’s even harder to reconcile the silos.
To simplify this process, most companies move their data from their various systems into a data warehouse. The enterprise’s operational systems collect all the data from the warehouse data repository. Data warehouses are optimized for access and analysis rather than transactional processing. The data warehouse is designed in such a way to help management get a 360 view of their company’s data.
Ways to Break Down Data Silos
The best way to remove data silos is to combine your data into a data warehouse. Here are a few different methods a company might use to get data into a data warehouse:
- Scripting. Some companies use scripts (written in SQL or Python, etc.) to write the code to extract the data and move it to a central location. This can be time-consuming, however, and it also requires a great deal of expertise.
- On-premise ETL tools. ETL (Extract, Transform, Load) tools can take much of the pain out of moving data by automating the process. They extract the data from the data source, perform transformations, and then load the data to the target data warehouse. These tools are typically provided on the company’s site.
- Cloud-based ETL tools. These ETL tools are provided in the cloud, where you can grasp the expertise and infrastructure of the vendor. Siloed and cloud data warehouses are commonly used when a company decides to move siloed data to a cloud data warehouse.
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