What Are Data Silos and How Can You Avoid Them?

Written by: Jeff Zemsky
11/28/2023

Read Time: 5 min

Editor's note: This blog was originally published in October 2022 and was updated with new information in November 2023.

What are data silos?

If the word "silo" brings to mind the large, looming towers on farms that seal grain off from the rest of the world, you are not too far off from understanding what a data silo is. Although more abstract than farm silos, they are isolated repositories of data owned by one team that are inaccessible by the rest of the organization. The data is held in disparate systems that are often incompatible with other sets of data. This makes it difficult for teams across the organization to access other data and collaborate, thwarting the potential for a streamlined product lifecycle process. 

Data silos vs. Information silos  

Information silos and data silos are sometimes used interchangeably, but there is a subtle difference. Where data silos are mainly a challenge with technology – data is inaccessible due to the incompatibility of systems – information silos are a more deliberate attempt to keep data isolated. Different teams may feel that data needs to remain within their silo due to a variety of reasons such as fear of security risk or belief that the information access is not useful.

Why are data silos a problem?

Enterprise-wide collaboration requires transparency and connectivity. Data silos not only isolate information but create significant friction for innovation and teamwork. Decisions – both minor and major – are delayed while each function checks for possible impact to their work. Not to mention how compatibility, quality control, and customer satisfaction are all up-hill battles when operating with data silos.

What are the disadvantages of data silos?

Duplicate data platforms and processes

It may seem obvious, but teams across the product development process often share the same data. But when data sets are inaccessible and incompatible, teams will begin to duplicate them in their own systems. This not only stifles collaboration but risks teams having inconsistent, outdated, inaccurate data that creates issues for productivity down the line. Duplicated data also eats up valuable data storage, costing the organization the extra IT resources needed to hold them.

Less collaboration between end users

If a design engineer and a manufacturing engineer are utilizing two different data management systems for their work processes, they inevitably hit a wall in collaboration when they need to begin sharing data. If the design engineer makes any changes, those changes won’t get communicated downstream, which would cause a litany of issues including lower product quality and slower time to market.  

Productivity plateaus

Isolated data will ultimately hinder productivity. Users in teams seeking data contained in another silo will need to seek, request, then be authorized to obtain that data before they can manage it. The bureaucratic nature of a simple task slows down productivity and eats into time that could be spent on utilizing the data.  

Security and compliance risks

There is a high risk of data leaks and breaches when data management is left to individualized teams. Since the organization has no centralized system, there is no specialized team that can handle overarching security, and there is no established course of action for response to threats. Data silos also put the company in danger of failing to comply with data privacy and protection laws. 

Incomplete data sets

Organizations are rife with incomplete data sets as a result of data silos. This means if one department collects data relevant to another's operations but does not share it, the second department is then using an incomplete data set. Such behavior also begets data redundancy, and bad data governance.

Inconsistent data

Data silos generate data redundancies which leads to inconsistent data in many ways. Different departments may be operating with different versions of the same data due to versioning or a lack of standardization. Similarly, they may be using different data definitions or introducing errors through manual data entries. This also calls the integrity of the data into question, where data silos often do not have sufficient resources to validate and reconcile.

Silo mentalities in departments

A silo mentality is when different teams within an organization operate in isolation with limited communication and collaboration across functional boundaries. The silo mentality in departments is often a consequence of data silos as the two concepts are interconnected. The limited connection among departments creates communication barriers and diverging priorities. Ultimately this fosters a competitive environment, leading departments to protect and defend their data instead of sharing it to work towards a common goal.

How do data silos occur? 

Legacy (old) IT infrastructure

As technology evolves at unimaginable rates, some organizations are left behind with their traditional systems. Legacy IT infrastructure are outdated systems that lack connectivity and the ability to integrate with newer technologies. Companies will often rely on them because they’re familiar, convenient, customized, or technically still work (so no need to fix them!). Like a slow leaking pipe, the problems this infrastructure is causing often go unnoticed by manufacturers until it’s too late. In this case, these aging technologies have resulted in isolation and data silos between teams due to their disconnection.

IT strategy and technology deployment

While IT strategies and technology deployments are meant to improve organizational efficiency, they can be big players in creating data silos. Customized technology solutions may meet specific needs for an environment, but often aren’t integrated fully into the existing data tools. This also occurs when working among legacy systems that don’t mesh well with new data solutions. Lastly, if the onboarding for new systems is insufficient, organizations may find employees reverting to the old, more familiar technologies.

Company growth

Data silos can happen anywhere, but they commonly occur in large organizations that have gone through rapid growth or acquisitions. To prioritize efficient workflow through this growth, groups of people will naturally section off into specialized teams. The problem arises when these teams also develop their own form of data management due to the organization lacking a centralized platform. Soon, teams that need to communicate and share data across the product lifecycle are slowed down by their disparate systems.

Organizational structure

Hierarchical organizational structures often lead to data silos, where each unit functions autonomously by collecting and managing its own data without a comprehensive view of the organization's data landscape. Once again, companies are faced with different departments using disparate data systems that are not connected, consequently deterring the flow of information across the enterprise.

Corporate culture and principles

When there's a strong emphasis on individual departmental success over organizational goals, employees may prioritize the protection of their department’s data and resources. A corporate culture that does not prioritize open communication and collaboration will struggle to achieve success in data management.

How do you identify data silos?

Start by examining the data architecture and practices for instances where information is restricted from other departments and systems. Typical signs of data silos include disparate or autonomous databases, limited data sharing between departments, redundant data collection, and inconsistent data formats or definitions. Also, if it is difficult to a comprehensive, real-time view of organizational operations, then you probably have data silos. Consider conducting thorough data audits and engaging in cross-department communications to better address data silos.

4 ways to break down data silos

Consolidate your data management systems

To break down data silos, there needs to be a consolidation of the various data management systems in use. Considering the opinions of users is an opportunity to build a digital thread that people will feel positive about and benefit the organization as a whole. 

Set a governance model for data collaboration

Establishing a data governance model will help to prevent any further data silos from cropping up again and can help promote collaboration. The framework outlines how data in your organization is collected, stored, and utilized. The rules and processes ensure privacy and compliance, minimizing security risks as well.

Leverage integration

Similar to consolidating the data management systems, it’s important to connect to other product development and business systems so that your entire ecosystem is working from the same up to date product information. No longer will users be burdened with redundant and error prone data entry tasks.   

A cultural shift

If users across the organization are resistant to change and don’t want to move from their current systems, then it will make the transition to a digital thread a challenge rather than an opportunity. This can be alleviated by involving them in the process. Garner employees’ opinions on what a consolidated data management system should look like. Once a solution is selected be sure to provide the proper education and training on the new system. 

Eliminating data silos with a digital thread

So now you know what a data silo is and the problems they cause, you’re probably wondering how to best break them down. Breaking down data silos must involve building a digital thread across the product lifecycle. A digital thread enables universal access to data, allowing for the consistency and collaboration needed. To achieve this, it’s important to establish product lifecycle management (PLM) as the foundation of your digital thread in order to create a seamless flow of information between essential systems. ThingWorx Navigate is a powerful PLM tool that seamlessly integrates data onto a single platform. The software allows for an easy transition onto the digital thread through easy access to product data, the democratization of data, and the flexible deployment options. 

Windchill is PTC’s PLM solution that provides comprehensive, out-of-the-box functionality that enables easy integration with other enterprise systems like IoT & ERP. The software is optimized to support the extended enterprise with a high degree of automation and interoperability for cross-discipline configuration management including secure collaboration, streamlined upgrades, and modern architecture to manage data at scale. This can help to lead users to manage more projects with faster lead time and lower costs of non-quality. 

Conclusion

Data silos kill productivity, introduce unnecessary errors, isolate users, and put data privacy at risk. Building a PLM-enabled digital thread is crucial to allowing the democratization of data and greater collaboration between end users. Your organization can see benefits such as a streamlined workflow and increased product quality after breaking down data silos with a digital thread.

Don't Silo Data, Connect It

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Tags: Product Lifecycle Management (PLM) Windchill Digital Thread Digital Transformation Enterprise Collaboration

About the Author

Jeff Zemsky

Jeff is the VP for Windchill Digital Thread. His team leads Navigate, Visualization, Windchill UI and Digital Product Traceability. Prior to joining PTC, Jeff spent 16 years implementing and using PLM, CAD and CAE at Industrial, High Tech & Consumer Products companies including leading the first Windchill PDMLink implementation in 2002. He was active in the PTC/USER community serving as Chair for the Windchill Solutions committee and on the Board of Directors for PTC/USER helping to bring voice of customer input together and create a community where people could network for tools and processes. Jeff attended Rensselaer Polytechnic Institute and Lehigh University.