In business, a data silo may sound like a good thing at first. Just like agricultural silos protect grain from the elements, a secure data silo can keep data safe from competitors and from malicious actors. But it can also have the effect of keeping certain parts of your team out of the loop, leading to flawed, duplicate, or incompatible information.
It’s only by breaking down silos and improving data integration that you can ensure your team members have the data they need to make better business decisions and move your company further along on the path to digital transformation.
Here’s what you need to know about the role of silos in data management, including what they are, why they happen, and how to fix them.
What Are Data Silos?
A data silo is essentially a data repository that’s used in one area of your business but is isolated from other departments. A silo could take the form of a data warehouse, a cloud storage space, or even a paper-based system that hasn't yet been digitized.
Although the data itself may be accurate and serve a purpose, it isn’t living up to its full potential because it isn’t easily accessible by all business units.
Data silos make it difficult to share information between different departments, and they can lead to roadblocks and inefficiencies in large organizations. By taking steps to identify siloed data, companies can improve their workflows and decision-making processes.
But in order to break down data silos, we first need to take a look at where they originate and then determine what obstacles can get in the way of successful data integration.
Where Do Data Silos Come From?
Most of the time, data silos aren’t created intentionally. They’re the result of individual departments taking steps to store their own data in the absence of a comprehensive data management system or data governance policy.
Data silos typically fall into these three categories:
1. Structural
Structural data silos occur as a result of policy choices within your organization. For example, maybe your company formed after the merger of two separate companies, and each one brought its own data management system.
Or maybe your startup grew so quickly that different departments were encouraged to develop their own data storage system. In the absence of data-sharing practices or a unified ecosystem, each team’s data set evolved in a different direction.
2. Technological
Organizational structure isn’t the only thing that can give rise to siloed data. In some cases, a lack of data-sharing tools may be holding you back. If your marketing team uses a customer relationship management (CRM) system but your accounting team uses Excel spreadsheets, they can’t easily incorporate each other’s data even if relevant data-sharing policies exist.
A similar problem can also arise from legacy systems that aren’t integrated with newer, cloud-based tools, making it difficult for teams to access all the data they need in real time.
3. Cultural
Finally, your problem with data silos could be a cultural one. A cultural information silo can occur even when there aren’t any technical or structural obstacles.
If you have a highly competitive company culture, individual departments may come to see data assets as a proprietary resource. Your marketing team or sales team may not want to share customer data with teams that they perceive as rivals.
Even if that isn’t the case, a decentralized work environment can mean that teams don’t think to share company data in the first place.
What’s Wrong With Data Silos?
Maybe the idea of siloed data doesn’t sound so bad. After all, if individual departments can handle their own data in a way they see fit, is it really such a big deal if other stakeholders don’t have access to it? Depending on your business model, it can be. Here are three impacts of data silos that can hold back your business:
Increased costs
Data storage isn’t cheap. Even though the cost of cloud storage space is likely cheaper than building your own data warehouse, having multiple storage systems can add up.
Factor in the cost of cybersecurity, regulatory compliance, and software licenses, and replacing those data silos with a unified data repository may make financial sense.
Poor data quality
Data silos can lead to incomplete or inaccurate data sets if each team has its own way of collecting and storing data. Transferring company data from one system to another can lead to an increased risk of error or introduce security vulnerabilities.
Additionally, teams may spend time chasing down the same data sources, not realizing that the information is already available to them in a siloed data set.
Limited data analysis
Companies with disparate data sets won’t benefit as much from modern data analytics tools, such as business intelligence software and big data initiatives. At a human level, decision-makers may lack a big-picture view, with each stakeholder relying on different sources of data and unable to draw from a single source of truth.
How to Eliminate Data Silos at Your Company
The good news about data siloes is that you aren’t stuck with them forever. Once you’ve identified the problem, you can take steps to break down data silos and develop a more effective data management strategy. Here are three possible solutions:
1. Create a data lake
The most straightforward solution to a data silo is to gather all of your data in one place, known as a data lake. According to Microsoft, a data lake is “a centralized repository that ingests and stores large volumes of data in its original form. The data can then be processed and used as a basis for a variety of analytic needs.”
In other words, individual teams can still maintain their own data repositories, in which data is stored in a more usable form. But with extract, transform, and load (ETL) tools, you can ensure that the information is available for additional use cases. ETL tools allow you to clean up existing data sets, and they also ensure consistent labeling and formatting practices across your organization.
2. Use APIs
An API, or application programming interface, is a way for multiple apps or programs to communicate with each other. For example, if your HR team uses an app for incident management, you can use an API to integrate it directly with your data management system.
APIs reduce the hurdles of data sharing between departments, while still allowing you to set permissions so that only the right team members can access the information.
3. Put automation to work for you
Automation can save you time and money when breaking down data silos, and it can reduce the risk of human error when transferring information between systems.
For example, by digitizing the leave of absence management process, you can reduce the need for paper forms and cut down on the number of steps that employees have to take to request a leave of absence.
Automation can be used to remove duplicate information from data sets, convert information from one format to another, and even send out automated reports to relevant team members. As part of a broader data management strategy, automation can help you simplify workflows and make data more accessible to more members of your team.
Break Down Data Silos With Automation
Data silos are isolated data repositories that make it harder for different departments to share information with each other. At a broader level, the lack of a central repository can mean stakeholders have to make decisions with incomplete or inaccurate information, and without the benefit of some data analytics and business intelligence tools.
With Pulpstream, you can use automation to improve collaboration, break down data silos, and digitize key steps in incident management, leave of absence management, and more. Our no-code interface makes it easy to begin your digital transformation without the need to develop new software from scratch or write custom code.
Contact us today to request a demo and learn more!
