Mergers and acquisitions are long, in-depth processes that span most divisions of a business. Nearly every facet of the originating companies is examined to eliminate redundancies, streamline operations, and reduce costs.
With the laundry list of items that needs to be evaluated during an acquisition, it’s easy to overlook the actual product offerings that the resulting company has. The ability to keep your data clean plays a huge role in the ultimate success or failure of the overall transaction, and it falls on the IT staff to ensure multiple systems are integrated properly.
However, how much does your IT team know about your PLM setup? Engineers are the ones who spend most of their time in the PLM—but your bottom line has the most to lose from a poorly managed PLM merge.
So what should you be looking for when merging and connecting data and systems during an acquisition? How can you best build out one system out of two completely different originating points?
Breaking Down Silos
Siloed information is problematic for any business or industry. When you merge two systems, you inherently create at least two silos that need to be broken down, with the data contained within systems needing to be consolidated, reconciled, and aggregated.
The new company has three choices when it comes to compiling parts data: merging the two systems, keeping the two systems separate but connected through another data management system, or a hybrid of the two approaches.
Whichever one you select, you should keep in mind the intended end result: to reduce duplication and complexity within the data itself. Which process is right for one company may not be right for another, so it falls upon the company doing the acquisition to research both datasets and make a determination as to which is the best option.
Avoiding Duplication
Many acquisitions occur because two companies are buying the same parts with different product numbers from different suppliers. The resulting new company then falls victim to having multiple sets of similar products, creating confusion both within the company and with its customers.
One way to avoid this type of duplication is through the use of our proprietary software, Design For Retrieval (DFR). By filtering your data with DFR, you can quickly identify redundant parts and products, create a clear naming convention, and optimize part descriptions and attributes.
Creating A Process
As with many data management tasks, having a set process for governing data integrity through an acquisition can avoid many of the headaches experienced down the road. Fortunately, the process for data cleansing during an acquisition isn’t all that different from a standard data management procedure.
- Clean your original data. Before you begin merging in new records from the acquired company, you should make sure that your original data is as accurate as possible. DFR will help to ensure that your source data has standard naming and styling conventions and descriptions and won’t cause issues with the data to be added.
- Merge in the new data and cleanse. You're now ready to add the product data from the second company to create a single source of truth for all product data—a product/parts master. In some cases, this can be as simple as an automated import, while in others, it can be a labor-intensive, manual process. But you'll need to ensure the new data is also cleansed and normalized according to the same stylistic guidelines as the legacy data.
- Evaluate duplications. If you’ve done the first two steps, this should be the natural result of cleaning the new dataset. You may wind up with very few redundancies, or you may have many. Either way, you should determine which of the duplicates best fits your needs moving forward and remove the lesser option. What results should be an accurate, efficient portrait of your new parts data—leading to better pricing with higher purchase volumes.
- Protect your new dataset. Without ongoing maintenance, all the time and effort that went into merging the two datasets into one can decay quickly—you can avoid this by implementing a data governance strategy. Once a governance structure is in place, it’s crucial that you run regular maintenance and have standards for new product entries to ensure that your final data remains as clean as possible.
Don’t Let Bad Data Undo Your Acquisition
With all the moving parts of an acquisition, there are lots of opportunities for the resulting business to experience problems. Your product data can be one of them, but it’s one of the few elements of the process that is entirely within your control.
Contact us today to learn more about how we can help your PLM survive, and thrive, in your next acquisition.
