How Dis-Integrated Data Can Derail Your ERP Initiative, Part 2 The Multimillion-Dollar Mistake You Don't Want to Make
por David Gleason
Originally published junio 26, 2007
In Part 1 of this series, the subject of data quality and customer data integration in enterprise resource planning (ERP) initiatives was discussed through a series of e-mail correspondences at Wexford Widgets, a fictional company. This article is a continuation of those correspondences, through which we’ll address the complexities that Wexford faces in going live with their ERP system without a true data quality improvement plan in place, as well as explore best practices for integrating customer data integration (CDI) into any ERP implementation.
In Wexford’s case, the ERP rollout was conducted in stages, with the system being rolled out at different times to different manufacturing facilities. During this parallel run period, Wexford needed to keep data in sync between the new ERP application and the appropriate legacy application. They elected to do this via double entry, hiring temporary staff to re-enter customer information from one system into another. This is a common approach to support parallel runs, and is done by many companies. However, the problem with parallel entry is that it is very error-prone and expensive. But for many companies, it’s the only way to ensure that customer information entered or updated in one system gets accurately reflected in the other system.
A customer data integration solution can provide a customer hub, which serves as a central integration point for customer data across applications. When customer master file data is created or updated in one system, the customer hub processes those updates and notifies other systems of the new/updated customer information. This process can be seamless and automated, removing the potential for human error created by the dual-entry process.
One of the traps that many organizations fall into is believing that because their existing data has been good enough to run the business so far, it will be of sufficient quality for the ERP system. Each old system had a limited scope, and the fact that it contained redundant or inconsistent data (as compared to what data in other systems reflects) didn’t affect the operation of that single system. Had Wexford looked at the functioning of these systems as a whole, they would have seen that they had data issues. But it can be very difficult to spot these data issues when the data is locked up in individual applications. By linking the business processes into a comprehensive ERP application, Wexford shined a bright light on the latent data issues. By implementing a CDI solution, organizations like Wexford can allow their ERP investments to realize their full potential.
On a similar note, organizations also need to take into account their goals when choosing a CDI solution. It’s not just a matter of picking one solution over another or which one will better yield accurate results. Processing performance for matching engines is a key issue and determining how quickly your system runs will assist in the amount of value you’ll receive. There are different methods of matching engines in CDI implementations. There’s deterministic matching, which is more cut and dry and faster for processes. Conversely, probabilistic matching permits higher levels of variance between records and might hold additional likely matches. It’s safe to say most organizations choose to blend both options when facing an implementation – but when considering a CDI vendor, I would advise their CDI solution be tested and evaluated against any unique and future requirements the organization may face.
Some things to keep in mind when evaluating the right CDI solution will be to test as much data as possible and compare the level of results. You’ll also need to make sure your employees can easily navigate the system, so an easy-to-search interface is necessary. Organizations must think into the future when carefully planning their CDI solutions and understandably so, choosing the right matching engine is just a part of the overall solution. As long as organizations think about their current and future requirements as well as the rate and speed of their process performance, they’re more than likely to partner with a CDI vendor that will best suit their needs and give them the ROI they expected.
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