Supply Chain Data Silos and How to Avoid Them

Martin Pahulje - August 19, 2021

As our technical capabilities grow, we are able to collect and analyze more data than ever before. This is enabling companies to process more information to be used for more accurate decision-making and problem-solving. While this provides significant business benefits, it is somewhat of a double-edged sword. The more data collected, companies can more readily identify and resolve bottlenecks, but it is also more likely that data silos develop. Once this occurs, data collection and analysis become futile, and your company is left without the information needed to continue operating successfully. Fortunately, there are ways to continue data collection while avoiding silos.

Why Do Data Silos Occur?

Let's begin with the basics. A data silo is a buildup of data that may or may not have been analyzed but has been collected. This means that while the data has been collected, it is either inaccessible or has not been reviewed. This is problematic for several reasons. When data goes unshared or unreviewed, it can create lost opportunities, lost revenue, and even expensive fees that will impact your bottom line. In addition to these losses, without the proper information, companies can inadvertently make poor business decisions resulting in further damages.

Data silos are impacting all sectors, causing devastating damage to companies. Particularly for logistics and transportation companies that rely on this data, it is critical that this be avoided. Thanks to historical data, we can see how data silos affect business, which is why we know that they need to be corrected. There are three primary reasons that data silos occur:

Lack of a shared network. A significant reason they occur is that companies fail to manage a shared network in which they can communicate between departments. Numerous departments rely on specific information to function, and this information is kept in overlapping, and often inconsistent records, resulting in a significant drag on efficiency. Without a cohesive shared data network, departments make contradicting decisions, and data is wasted and neglected, creating data silos. As more data is collected and left idle, data continues to backup, worsening the silo.
Failure to scale to company growth. When a company grows quickly and fail to scale their infrastructure accordingly, departments collect data in an ad hoc fashion. Without proper scaling, this overwhelms infrastructure, creating a silo. Not only does this slow down data processing, but it can also create a significant backlog for data managers and IT.
Poor organizational structure. Restrictive access control systems can contribute to the burden of sharing data. While it is essential to protect your information, data must be accessible within the organization for it to be shared and reviewed to prevent a silo. If team members cannot work together to move data, it will inevitably get caught up in a silo.

Data silos can be detrimental to your organization, and they are challenging to recover from. To avoid the losses produced by data silos, your organization must be proactive and work to avoid them.

How to Avoid Data Silos

Collect Data

Many believe that data silos occur due to collecting too much data, but this is not true. In reality, collecting data allows your company's decision-makers to understand your industry and business better. It is incredibly critical that you collect data during all manufacturing, packing, transportation, and distribution processes. This data will provide you with holistic oversight of your organization, ensuring that you identify any bottlenecks and determine solutions to each problem. More data means more knowledge, therefore enabling decision-makers to assess vulnerable areas and strengthen them.

Additionally, more data between departments can reduce miscommunication. In turn, this means that data will not be duplicated or contradicted. So, while data collection is crucial, you must be collecting the right data. Too much inaccurate or replicated data is what builds up in the silo, but with the right data, it will help you identify and solve problems such as a potential silo.

Once data is collected, you must analyze the data and distribute findings to the appropriate channels. If this is not done, the data collection is wasted, causing the silo you worked to avoid. A great way to ensure delivery to the proper channels is through using a supply chain management platform.

Application Integration

Another excellent way to avoid data silos is through application integration. This is the process of enabling independently designed applications to work together cohesively. A significant contributor to data silos is the replication or contradiction of data sets, often caused by multiple sources of information. Once you integrate your companies’ existing applications, your systems will verify that data is accurate and original, reducing the amount of unnecessary data that would cause a silo.

One method of application integration is to manually program interactions between two or more applications. This is often done using middleware, which sits between the front-end request and the back-end resource and is typically utilized when a company has dozens of applications.

Data Integration

An alternative source of application integration and an equally beneficial avoidance of silos is data integration. This is the process of replicating data from various databases, platforms, and other sources into one single centralized repository, such as a data warehouse. In this data warehouse, all data will be shared and analyzed across the company. This method is highly beneficial in making all of your data accessible for analytics and BI systems while preventing data silos. Because all data is processed through one system, it eliminates the possibility for inaccurate or replicated data, ensuring that a silo does not form around idle information.

Cloud solutions are instrumental in data integration, allowing you to centralize all data without purchasing or managing a physical data warehouse. Additionally, cloud data storage and processing tend to be faster and less expensive than traditional data management methods. This means that while you are avoiding silos, you are optimizing how you store and analyze data.

Facing your Silo Problem

Data silos are a dilemma faced by organizations of all industries but can be particularly damaging to those in the logistics and transportation sector. By addressing and avoiding data silos, you are setting your company up for success. Once you eliminate bottlenecks and silos in your supply chain, you can ensure that customer needs are met and the right decisions are made. Overall, by eliminating data silos, you can increase efficiency and strengthen your company's bottom line.

While not all organizations can afford to automate their supply chain fully, there are still practices you can implement to avoid data silos. Through collecting data, improving communication between departments, and integrating data, data silos will be a problem of the past. Data is an instrumental tool at our disposal if handled correctly. To lead your company to success, ensure that you maximize the value of the data you collect by avoiding silos.

If you want to learn more get your Guide "Why the cloud is the future of manufacturing"

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