Dangers of Data Silos And Efficient Strategies for Eliminating Them
Modern business organizations generate tons of new data during each interaction with current or potential customers. Being aware of your customer’s pain points and knowing how to address them is crucial for long-term relationships. Your failures can be a source of valuable information as well. Analyzing the history of communication with clients who refused your services can help better understand what and when went wrong and adjust your business strategy accordingly. Due to these reasons, there is almost no data from which you could not extract helpful information.
However, without a proper approach, generating, collecting, and analyzing business data can become a complete mess. For example, at some point, you might discover that your organization lacks desirable transparency due to data silos. Even though they can decrease your overall efficiency, there’s nothing to be afraid of. The trained eye can easily detect business silos, and eliminating them is not rocket science for an experienced team.
What Data Silos Are and Where They Come From
Data silos refer to the isolation of data within a specific department or team within a bigger organization, resulting in limited access to it by other departments or units. In other words, data silos occur when different parts of an organization store data in separate, often incompatible systems, leading to difficulties in sharing and utilizing that data. It can lead to inefficiencies and a lack of coordination within the business organization.
Here, we speak of something more than a bunch of Excel files forgotten on somebody’s computer. Imagine a customer service department that collects vast amounts of customer data regarding certain services and goods they have problems with. Structured and analyzed, this data can provide valuable insights into how customers interact with the business organization and what causes the most struggle. However, if this information is “locked” inside the data silo, the marketing team won’t be able to use it for building a more efficient advertising campaign, for example. Now, let’s consider some most common causes of data silos.
One of the primary reasons is the lack of a comprehensive data management strategy. Many organizations store business data in various systems, applications, and databases, each managed by different teams or departments. As a result, there is no standardized approach to data management, and it remains siloed within those individual systems.
Another reason for the appearance of data silos is the use of disparate systems across different parts of the business organization. For example, one department might use an enterprise resource planning (ERP) system, while another uses a customer relationship management (CRM) system. These may not be designed to work together, making it difficult to share data between them. Even when they are intended to communicate, data can still become siloed due to differences in how it is organized, stored, or managed.
Data silos can also occur due to a lack of communication and collaboration between different departments within an organization. If teams work independently without sharing their data with others, this can lead to data silos. Sometimes, this may be caused by the perception that the data is sensitive or confidential and should not be shared with others. However, such an approach can result in teams working with incomplete or inaccurate business info, leading to suboptimal decision-making.
Additionally, data silos can be a result of regulatory or compliance requirements. Some records may need to be stored in a particular way or kept confidential due to privacy laws or industry regulations. It can result in data being siloed within specific systems or teams to comply with those requirements.
Also, in some cases, the IT department may not keep pace with the organization’s growth. New teams must be formed when the business enters new markets to handle the increased workload efficiently. If the technologies they use are not included in the overall infrastructure, new data silos may appear.
Challenges That Data Silos Bring
Data silos not only make it harder for organizations to access different pieces of information. They also bring more global challenges, such as:
- Duplication of efforts. Different departments may be collecting the same data without realizing it, which can lead to inefficiencies and redundant work;
- Inconsistent data analysis. Without a centralized data management system, different departments may analyze data using their own methods or tools, which can result in inconsistencies in the analysis;
- Lack of coordination. Data silos can lead to a lack of coordination between departments, as they may not be aware of what their colleagues are doing or how their work fits into the bigger picture. It can result in lousy decision-making;
- Inaccurate or incomplete data. When data is stored in silos, it may not be complete or accurate, as different departments may be working with different versions of the data or may not have access to all the information they need;
- Security and privacy risks. Storing data in separate systems can increase the risk of data breaches or other security issues, as each of them may have its own vulnerabilities that need to be managed.
These potential consequences of data silos’ appearance require eliminating them as quickly as possible. The issue is especially important when modernizing legacy systems. The lack of a centralized approach to data management after many years of using such software may lead to multiple data silos.
How to Eliminate Data Silos
Implement a centralized data management system. By creating a centralized data repository, organizations can ensure that business data is stored in a consistent and accessible way, making it easier to share and analyze. For this purpose, you can use, for example, a data warehouse or data lake depending on what better suits your needs.
Use data integration tools. Data integration tools can help organizations combine data from different sources and systems, allowing for more comprehensive analysis and better decision-making. For a small organization, a bunch of scripts written with Python, SQL, and other languages may be enough to do the trick. However, in some cases, a more sophisticated toolset may be needed. For example, ETL (Extract, Transform, Load) tools enable vast automation possibilities. Such software can extract raw business data from multiple data sources across your organization, transform it into a consistent format, and load it into a centralized database.
Standardize data formats and definitions. Organizations need to establish standardized formats and definitions for fields that data applications use. It helps to ensure that business data can be effectively shared and analyzed across departments.
Application programming interfaces, commonly known as APIs, facilitate communication between different systems and applications by using a shared language. By doing so, they enable disparate systems to function as a cohesive whole which enables real-time sharing of data.
Encourage cross-departmental collaboration. By fostering a culture of collaboration and communication across departments, organizations can encourage data sharing and break down silos.
Invest in training and education. To guarantee that all employees understand the importance of data sharing and collaboration, organizations should provide training and education on data management and analysis.
Efficient data management is tricky sometimes. The intention not to lose a single piece of information your business generates is natural. However, the more complex your organization’s structure is, the more difficult data engineering challenges developers will have to face. Miss one data source by not including it in a centralized system, and boom! Here’s a new data silo with all the disadvantages you can imagine. The good news is that by following best practices, software development companies can ensure data transparency across business organizations of any size and complexity.
Contact us if you want to build a brand new software solution with no data silos or ensure that your legacy system won’t have any after modernization.