Data Silos: What They Are and How To Eliminate Them
Organizations that generate and process large amounts of information often face data silos and executives may not even be aware of their existence. However, it’s clear that something is wrong when you encounter outdated or duplicate data or cannot access certain bits of information.
Data silos can lead to obstructed collaboration between departments, inaccurate business reports, increased costs, and other issues. To effectively fight back against data silos, companies need to take a step back to rethink their current business operations, company culture, and IT management. In this article, you will learn what data silos are, the main reasons why they occur, and 5 efficient ways to eliminate them.
What are data silos?
Data silos, also known as information “silos”, are a set of data available to one department but isolated from other departments in the company. Such isolated sets do not extend beyond one department for whatever reason and are usually stored in a separate system. As a result, employees find it difficult to access, search, and analyze the information across the departments due to the data separation.
Let’s look at an example. Imagine a situation when a customer service department, which has information about recent customer requests, and the marketing department, which has information about customers’ buying habits, cannot combine their data. This will lead to an incomplete image of the customer and, as a result, will impact the marketing strategy of the company.
But why does it happen?
Let’s look at the most common reasons.
Decentralized IT management
It often happens that different departments in a company use different IT technologies. Sometimes these technologies are not even approved by an IT department - read more about shadow IT here. The reason for that is that employees want to use the most convenient tools and thus can buy their own software without checking it for compatibility with the existing systems or without approving it with the IT department. As a result, departments begin to function as separate entities and limit access to their data.
In addition, some departments may be better trained to use technologies than others, which also causes different departments not to have access to the same information.
One more reason for data silos to occur is the company culture. That means, a company may not promote and encourage data sharing or it can allow departments to create their own data processing standards. In some companies, such isolation may even lead to internal competition within the company. Hence, if a company does not have a set of principles for managing and sharing information, that might be the primary reason for a data silo to happen.
And last but not least, when a company grows quickly, it becomes difficult to manage the data across the organization and provide access to it properly. The reason for that is that the infrastructure and processes often don’t scale and departments may not implement process updates as rapidly as necessary.
If you want to learn more about how to handle the data in the most efficient ways, check out these articles:
- Data Science in Healthcare: Benefits and Use Cases
- Data Visualization and Trends for the Future
- Database Management Systems (DBMS): to SQL or NoSQL, That Is the Question
- What Is AWS Glue? An Overview and Main Features
Data silos - the hidden iceberg (and the challenges they bring)
As you can see, data silos are a consequence of how organizations are structured and managed as a whole, including their IT operations. Whatever the reason for separate data in your organization, it's clear that such isolation is not good.
But what's really so bad about it?
While siloed data may seem harmless at a first glance, there are many ways in which it can sink companies:
- Incomplete view of the business: companies cannot get a holistic view of their business. As a result, business strategies are not based on accurate data, which can lead to wrong decisions ahead;
- Poor user experience: when departments don’t have a complete understanding of the customer journey due to the lack of data, it can affect brand awareness and user experience in a negative way;
- Limited collaboration: teams have limited access to the information and thus cannot efficiently work together. When employees see only one part of the business process, they miss opportunities to work together toward the company's goals;
- Slow company growth: with separate data, teams can spend a lot of time looking for needed information. This approach reduces departmental productivity, which can affect the growth pace of the organization;
- Security risks: employees may store data sets on private devices or in repositories that are not approved by the IT department. This increases security risks, especially if the company does not have proper security controls;
- Reduced data quality: isolated data quickly becomes outdated, difficult to access, and inaccurate - this can result in incorrect business decisions.
Finally, the problems caused by data silos are exacerbated when companies start to put up with data isolation or try to find workarounds to resolve this problem. Eventually, company operations become worse over time and the silos tend to build up slowly. Thus, companies need to investigate the "weak points" and bottlenecks by paying attention to the daily workflow.
How can companies identify data silos?
Because teams can function as autonomous units within a company, detecting data silos can be difficult, yet it’s possible. Now that we know how and why these silos occur, you can identify them with the following indicators:
- Lack of access to data for specific business units;
- Inconsistent data reported by different departments;
- Inability to find certain data or access it quickly;
- Occurrence of data errors or existence of outdated data;
- Cases of data negligence.
Organizations may often be reassured by a false sense of security when they see small shortcomings and thus they don’t usually worry about them too much. But these shortcomings could lead to more serious problems. In order to prevent it, use the methods below to eliminate the silos.
5 ways to eliminate data silos
Companies need to make the process of eliminating data silos a priority, supported by a well-designed strategy. So here are the top methods to help you get started.
Use effective integration tools
Data from different departments is likely located in different repositories, so proper integration of all systems and apps is one of the most effective ways to avoid data separation. Organizations can utilize several methods to move information from siloed sources into a single repository, including specialized integration software. In addition, integration software helps different teams work on the same page, thus improving cross-team collaboration.
Here are the best data integration tools:
- Scripting: writing scripts in SQL, Python, or other languages helps move data from isolated sources to the repository. However, as data sources grow, it becomes difficult and costly to scale scripting;
- On-premises ETL tools: ETL (extract, transform, and load) tools can help a company automate the process of moving data from various sources by converting it into a common format for analysis and uploading the results to a repository;
- Cloud-based ETL: these tools take advantage of the cloud provider’s infrastructure and break down the silos by providing technological means to gather the data from different sources into a central repository.
Next, you might pay attention to:
Another great way to eliminate silos is to consolidate your data into a suitable repository- either a data warehouse or a data lake but you will have to choose the platform according to your business needs. Both solutions provide centralized storage, but you need to know the difference between the two.
A data lake is a pool of structured and unstructured data that you can store on any scale. Data lakes consist of both relational and non-relational data and hence, can be used by various users and for several different purposes (i.e. machine learning or predictive analytics).
A data warehouse, on the other hand, stores structured relational data that is highly curated. The data from this repository is used by business analysts mostly and it cannot serve as many purposes as the one from a data lake. Hence, before selecting the right platform, analyze your business needs carefully to make sure you invest in a profitable solution.
Sort legacy data
To create an effective data management system, a company needs to make sure that all its data is up-to-date and accurate. That is the reason why companies should invest some time into looking for outdated, disparate, or duplicate data that may have been stored in an organization for years.
Nearly at the finish line:
Create a collaborative culture
To really put a stop to data silos, executives need to change an organization's culture or make adjustments to it. This may include encouragement to collaborate with different departments, promotion of data sharing, or establishment of transparency within the organization. For that, companies can kick-start new initiatives, review internal politics and entrenched cultural norms, and overall encourage employees to be open and collaborative.
Consolidate data management systems
Executives can gather information about each team's data collection methods and management systems to decide which systems should be merged and which ones discarded. The company should have a single central data management system, with flexible user rights and an option to easily share the information and generate meaningful reports. An efficient and user-friendly data management platform can reduce the number of silos and promote common data standards and policies.
As organizations and data sets grow, data silos become a significant threat. For this reason, it’s important to take some time to break down the silos and address the issue at its root. Companies that are in control of their data can make accurate business decisions, benefit from more accurate analytics and reporting and uncover trends and patterns that were previously invisible.
Q: Why do data silos exist?
A: Data silos exist because the information isn't shared across departments or between employees within an organization. This happens due to many reasons, such as an organization’s growth, decentralized IT management, company culture, or organizational operations. All of these reasons lead to the creation of data silos.
Q: What are silos in data?
A: Silos in data are data repositories that are controlled only by one department within a company and cannot be accessed by other business units. In this way, certain data becomes isolated, which leads to business owners not being able to get a comprehensive view of business operations as well as other issues.
Q: What is an information silo?
A: Information silos, also known as data silos, are a set of data available to one department but isolated from the other departments (teams) within an organization and stored in a separate system. In other words, it is an isolated data set that prevents an organization from getting a holistic view of its data.
Q: What are silos in business?
A: In business, data silos mean that business divisions tend to work independently (lack communication and collaboration between departments) and avoid sharing information because of system limitations or personal reasons.