Does Your Supply Chain Have Dirty Data?

Dirty Data


Dirty data simply refers to any data containing incorrect information. When we are talking about how dirty data affects computer algorithms, we generally think of viruses, ransomware and other types of cyberattacks. While this may be true, (the years of 2020-present are a good example of that uptick in activity) it isn't always the case for dirty data.

In fact, dirty data is something that businesses and their supply chains deal with on a daily basis. And most of the time, the real threat comes from errors due to missing, duplicate, incorrect, outdated, or corrupted data files as a result of some conflicting rules in your data management system, such as an ERP.


Performing a Supply Chain Data Cleanse

It should go without saying, but supply chains need to occasionally have their various databases cleaned up and data cleansed.

However, to maintain the ins and outs of various daily operations, this is something that really must be handled strategically and preferably performed in multiple stages. Doing a complete overhaul or data cleanse as it is commonly referred to can put stress on your processes and create additional issues if not handled properly. 

But when it is, it can not only help you to better streamline your operations, it can also help you forecast various things such as the ebb and flow of demand, fill missing critical tasks, analyze employee performance and the list goes on.

Having said that, a full data cleansing could end up shifting the focus of your IT team if anything major shows up, so it is always best to be prepared and to have an understanding of what the data cleanse process ideally looks like before diving in.


Find the issues

Understanding that the issue with your data may have likely occurred from various internal errors over time will help you to identify and clean the data much more efficiently.

Of course, you should look for dirty data originating from anywhere if it affects your supply chain integrity. By giving yourself a general idea of what may be the easiest to spot first, you avoid a lot of undue stress while also covering a lot of ground quickly.

Instead of overwhelming your supply chain management team, simply break the cleaning process down into smaller steps that allow for a calm, yet meticulous pace.

Team management is essential to getting to the root of the dirty data problem. Management should be cautious not to panic and shift the focus from daily operations to data cleansing as this is more likely to cause more problems than it solves. Instead, prioritize data sets that need to be evaluated. Depending upon the type of data that needs to be cleaned, verified, or updated, assign the task to the appropriate staff.

When your supply chain management team works together in a cohesive manner under one common goal, you will undoubtedly make better progress at cleaning up your data.


Multiple eyes solve multiple problems

Perhaps the best way to clean dirty data from your supply chain is to have multiple IT eyes focusing on the problem.

Too often, supply chains identify that they have an issue with the ERP data and assign one person or one team to find and fix the issues. Apart from this taking an extraordinary amount of time, it also opens the door for issues to arise due to too much strain being placed upon the IT team.

For the best results, have multiple teams handling smaller, more manageable data cleanup tasks. Only those, even in the IT department, who have knowledge of your ERP should be involved in the cleaning process.


Check your algorithms then let them work

Algorithms that handle correctly entered data are essential to the operations and distribution of that data throughout the entirety of your supply chain. Human interference with algorithms is one of a couple of main reasons that data within these programs become corrupted. Having fewer hands on deck means that there will be less potential for data corruption.

Additionally, have IT verify that any of the software processes they manage for the input of data across the supply chain is properly maintained and managed. Algorithms can range from providing data to leveraging, quantity discounts, and cost management so when it comes to these Information Technology areas of your supply chain... trust your IT guys and don't be afraid to lean on them so long as that is an area of their field that they have been trained to oversee.

Cleaning the dirty data within your database so that the algorithms have the correct data to use and distribute involves removing or "scrubbing" any duplicate information from the database, verifying that information such as bulk agreements, client information, and delivery destinations are correct is crucial for your algorithm to perform in the way you expect it to perform. These algorithms have specific tasks that they are programmed to handle, but they cannot mentally compute or recognize certain information as useless unless it is programmed to do so.


Verify, Clean, Repeat

Cleaning up your supply chain's dirty data isn't a "set it and forget it" ordeal. It is an ongoing effort and if done correctly and maintained well over time, there will never be much of a need for a full-on data purge.

Daily operations and verifications of data is the quickest and simplest way in which to reduce ongoing issues within the database. Simple things such as checking the spelling of all information, counting the inventory being delivered, and then counting again, and taking the time to record all of this data just as carefully with whatever software system you utilize can go a tremendously long way in helping you combat bad data from piling up.


AI integration

Larger companies have found that certain operations within the supply chain have benefited from Artificial Intelligence (AI) software integration. When used properly, going the route of AI brings with it a wide variety of benefits that is deserving of its own article in the future.

With AI, the biggest benefit around this topic would have to be that it removes the need for routine manual data cleansing by making decisions and creating intelligent data categories from which to operate with little to no human interaction aside from the initial input of data.