What are Data Silos?

Data silos are isolated data accessible to only one group in an organization. Let’s understand the concept of data silos and why they are problematic. 

Define Data Silos

Data silos meaning can be referred to as a repository of data that is controlled by one department or a business unit in an organization. Siloed data is often incompatible with other data sets, making it difficult or not at all accessible to other groups or units in the same organization.

For instance, marketing, sales, HR, or other departments need different data to do their work. These departments usually store their data in separate locations known as data or information silos. With the increase in data assets, the silos also grow. 

The roots of data silos can be technical, organizational, or cultural. They tend to arise in larger companies because separate organizational units may operate independently as per their goals, priorities, and budgets. 

Although data silos may seem harmless, they end up negatively affecting the overall performance of an organization. It creates barriers to data sharing and collaboration among different departments. It also impacts on the quality of the data. Data silos usually happen if there is no well-planned data management strategy. 

Why Do Data Silos Occur?

According to the siloed data meaning, they cannot be termed as healthy data. Data is only healthy when it is accessible and easily understood across your organization. But, if the data cannot be used timely or cannot be trusted may not be adding any value to your organization’s decision-making. 

Data silos may occur naturally in an organization over time as each department stores data for its purpose. Here are the common causes that lead to data silos:

• Siloed Organizational Structure

The main reason that leads to data silos is an organization’s structure and their management styles. Whether it is a large company with different subsidiaries or smaller firms, if the organization follows a decentralized structure, then silos form when units operate independently. 

Before big data and cloud advancements, individual departments autonomously handled their data. This practice wasn’t frowned upon as it helped different business units create and manage their data according to their policies, procedures, and goals. This led to teams developing their ways of working and analyzing data, resulting in the formation of silos around the data collection and storage methods. 

• Company Culture and Principles

Company culture can often lead to departmental isolation. In an organization, if the marketing and sales departments have their physically separate areas with their own process of working and goals, then each department usually considers them a separate business unit. 

This culture of separation is also reflected in terms of data management. Even if both the marketing and sales teams work together with customer data, the culture of the company may encourage them to keep the data separate. Since the concept of company-wide data sharing is relatively new, it leaves departments uninterested in consolidating their data. 

• IT and Technology Deployment

Technology is a double-edged sword that leads to the creation of siloed systems. In an organization, different departments may rely on tools and data systems like CRM or unique accounting systems. 

These legacy systems were not designed for easy data sharing. Each system stores and manages data in proprietary ways, hindering easy exchange of data between departments. Moreover, decentralized IT procurement allows different technologies to enter the ecosystem, resulting in incompatible databases and applications. 

Additionally, the broad spectrum of available data platforms, including relational databases, specialized cloud services, and big data platforms elevate the issue by accommodating specific business requirements. These perpetuate data silos in the process. 

• Business Growth and Acquisitions 

Another factor that may impact an organization and lead to the development of data silos is new acquisitions or the growth of a business. As a company example, it would have additional business units added or created. Mergers and acquisitions bring silos into an organization, impacting the organization negatively in the long run.

Why Are Data Silos Problematic?

Data silos essentially disrupt business operations and data analytics initiatives that support them. These silos data limit the ability of an organization to manage the business process and make informed decisions. 

Let’s understand the different reasons why data silo systems hurt businesses:

• Limits Data View

Data silos hinder the sharing of relevant data across different departments in an organization. Since each department has different methods for data analysis, it limits their views and stops them from identifying enterprise-wide inefficiencies with the limited view of data. 

Due to this, important business decisions and strategies cannot be made on the available data, leading to flawed decision-making.

• Threaten Data Integrity

Siloed data can lead to the same information being stored in different databases, creating inconsistencies between data of different departments. As the data ages, it becomes less accurate and therefore, less useful. 

Additionally, many data silos are not in alignment with data others. For instance, a sales team may format customer data differently than other departments. If there is any error in the data, it may not be identified and fixed in one silo as compared to other silos in different departments. This inconsistency in data can impact its quality, and accuracy and create integrity issues that can affect both operational and analytical applications. 

• Resource Wastage

With data silos, the same information may be stored in different systems. When users download data into their group or personal storage, the resources are impacted. Moreover, this process leads to data duplication with each download being a redundant copy of the existing data.

In many cases, the systems are deployed and managed separately by different business units instead of the data management team. This further increases the spending cost, leading to inefficient use of IT resources. 

• Discourage Collaborative Work

Isolated data sets in silos significantly diminish the opportunities for data sharing and collaboration between users of different departments. Whereas data-driven organizations embrace collaboration as a power tool, this becomes a hindrance when there are data silos in a company. 

For an organization to experience business growth, it needs to consistently leverage data insights and make collaboration a focal point. This can only happen when data is accessible and shared across the organization. However, when data cannot be completely shared, it impacts the ability to collaborate.

• Silo Mentality in Departments

Data silos contribute to organizational silos, that is departments and business units that guard their data closely. These departments may be reluctant to share their data with others and may also resist data governance programs that focus on breaking data silos. 

Without adequate data governance, its availability, usability, security, and integrity would be impacted. This would also lead to inconsistent and redundant data across all the systems in the organization.  

• Data Security and Compliance Issues

Different data silos tend to have different storage space. Some silos may be stored using online business tools like Google Drive or some may be stored by individual users in Excel spreadsheets. 

This increases the risk of data security and privacy if the organization does not have proper control measures in place. Silos also complicate the efforts to comply with data privacy and protection laws, creating more hindrance in the organization’s performance. 

• Hidden Costs

The financial costs of data silos may differ depending on the organization and its usage. The negative impact of data silos is more deep-rooted when it comes to hidden costs. It can directly or indirectly affect the overall organization by reducing its productivity, missing business opportunities, or making business management less effective. 

It can also lower the quality of customer services and reduce the trust in data, limiting its use and its benefits for the organization. These can increase the IT and data management expenses significantly. 

How Do You Identify Data Silos?

The foremost step to reduce or eliminate data silos in an organization is to know how you can identify it. Data silos are usually hard to detect because of their disconnected nature. In an ideal business scenario, IT and data management teams should create an inventory of all the systems used in their organization. They also should regularly update this list when new systems are added. 

However, finding the data silos is more often than not challenging, especially in large organizations with many business units that operate autonomously. Following are some of the signs you should keep in mind that can point you toward data silos:

• Inconsistencies among data being reported across different departments
• End users encounter that data sets are out of date or incomplete
• Unforeseen IT expenses emerging beyond the budget 
• Executives complaining about lack of data for certain business operations
• Challenges faced by business intelligence (BI) and data science teams in locating or retrieving data

Strategies to Encounter Data Silos

Eliminating data silos is crucial for an organization to manage and use data more effectively. It can improve the performance of the overall business and help in lowering IT and data management costs. 

As an organization, you can implement the following strategies to remove silos and connect data assets to support business operations:

• Change Management

To put a complete stop to data silos, changing the organizational culture is the key. A step towards the change can be executed by implementing a data strategy development process or by undertaking data governance initiatives. 

Communicate the advantages of data sharing and data integrity and highlight the negative impact of data silos so that the whole organization can embrace the initiative. Alternatively, you can also implement complete change management programs to make positive cultural changes and ensure that every department adopts them. 

• Integrate Data

Integrating data silos with other systems is the most effective and simple way to break them down. A popular form of data integration is by using extract, transform, and load (ETL) tools, which help in extracting data from the source systems, combining it, and loading it into the target system or application. 

Cloud-based ETL is another example through which you can integrate data efficiently. This ETL format leverages the cloud provider’s infrastructure including the data warehouse or ETL tools designed to work efficiently in their environment. ETL breaks down the silos due to which data can be gathered from multiple sources for it to be analyzed in a central location. 

Another method can be through the process of scripting. Here organizations can task IT to write scripts in SQL, Python, or other scripting languages to move data from siloed sources into a data warehouse. 

However, the downside of this method is that it can lead to complexity. When there are changes in the data source, it would require the script to be updated time and again, making it costly and time-consuming.

• Centralize Data

The ideal way to eliminate the data silo problem is by pooling all corporate data into a cloud-based data warehouse or data lake. Doing this creates a central repository of data optimized for efficient use and analysis. 

These data lakes and warehouses can store a significant amount of data in different forms. Therefore, data from different sources can be combined and stored together, making it easily accessible to individuals or groups to balance business needs against privacy and security. 

• Establish Data Management and Governance

With centralized and integrated data in place, you also create the scope to centralize data access and its control with a proper data governance framework. By doing so, you are not only eliminating existing silos but also preventing new ones from being created. 

Moreover, robust data access policies facilitate self-service analysis. So, business users with permission can easily access and use the required data without unnecessary delay or headaches. 

Future of Data Storage

A decentralized approach to data storage leads to the formation of silos. By shifting to cloud data solutions, your organization can centralize data from various sources and make it easily accessible for different individuals and departments. 

Moreover, by incorporating an ETL process, irrelevant and duplicated data can be eliminated. This can enhance the quality of data and lead to accurate analysis. With a cloud solution, different units of an organization can work collaboratively with fresh and clean data that is accessible with a 360-degree view of the organization. 

Conclusion

Thus, data silos negatively impact the performance and productivity of the organization as a whole. It creates barriers and hinders easy access to data. Due to this, it is critical to identify data silos and eliminate them in order for the organization to make data-driven decisions and utilize new opportunities for growth and development.