Managing and maintaining quality data is one of the never-ending battles of retail, and few times are as critical for winning that battle as when you implement a new ERP system. Yet few retailers think about the state of their data ahead of time. Once the implementation project begins, the effort to clean it up adds time and cost to the project.
One of the biggest impacts a retailer can have on the efficiency and speed of a new ERP implementation is cleaning up data before the project kicks off. You know your data in your current system, so clean it up there.
Understand where your data is going
There are two approaches to data clean-up: Extract everything and put it in a spreadsheet to clean and re-import, or have a developer write scripts to update the data within the ERP. The “right” approach depends on your in-house expertise and comfort level.
Whichever approach and whatever your new ERP system looks like, there are important tasks that can be accomplished to improve quality, such as ensuring consistency in upper and lowercase letters, that everything is spelled correctly, and fields like addresses and descriptions are complete.
To get your data in good shape, you need to know what it should look like in the new ERP system and downstream systems. What are the field lengths? Do you need to remove special characters? Have you purged duplicate customers, vendors and items so you’re not migrating bad data?
There’s a tendency to convert everything and deal with it later, but that can cause big complications down the road. Purge the records and data you don’t need, or convert only the necessary data. Convert older data early in the project; convert it in a later phase; or keep the old system for a period of time in the event you need to access older data.
Look for orphan records and delete them. Someone might have created a shipment against an order but did not receive anything on the shipment and did not delete it. You can also consider changing delete parameters to help delete data you do not want to convert, or use scripts to identify the orphan data, validate it and delete the records.
Then test, test and test: Start in a test environment and then graduate to running a small number of records in production to make sure everything works as expected. And of course, always have a back-up you can restore.
Maintain a clean house
Cleaning up data is a big undertaking and it’s important to maintain the quality. Train users thoroughly on the new system, put gates in place to keep users from creating bad data, and set up rules to safeguard the data as much as possible.
Data is a huge asset. It is used for communications with customers, suppliers and employees. Reporting and analytics only works when the data is correct, complete, free of unneeded records and clean.
-ArMand Nelson, Director of Retail Strategy, BTM Global