![]() ![]() Are you performing more unplanned than planned maintenance?.Do your maintenance workers spend more time looking for needed parts and information than performing actual maintenance?.Do you have problems finding the correct repair materials when you need them?.Do you have problems with work orders being written to the wrong equipment?.While certainly not exhaustive (there are a ton of creative ways problems with your master data might be gumming up the works), these are common problems we see when your master data contains errors, duplications, omissions, or misinformation. We’ve put together a list of questions to help you determine the health of your organization’s master data. How to Diagnose a Master Data Problem in Your Organization First, though, you must take a clear-eyed look at the current state of your organization’s master data. The good news is that there are strategies and solutions to stick to your master data resolution. The truth is that even if you’ve done the master data project right this spring, you’re going to be right back where you were if you rely on traditional methods. This is keeping up that spring cleaning into the summer and beyond. But the value isn’t there unless you’re dedicated to sustaining the data-you acquire new data, the data that you generate continues to evolve, you acquire assets and change others, and you implement new systems. Getting your records straight once will have some benefits in the short term. The biggest challenge, though, is that clean data isn’t anywhere near the end of the project. This is a line-by-line job that’s time consuming, labor-intensive, and still error-prone. Even working with outside specialists and experts to acquire and implement a taxonomy is…taxing.Įven if you get this far without abandoning the project, you still have to consolidate and cleanse all of your data. Building out a well-defined taxonomy (the dictionary of data structures, rules, etc.), the foundation of a good master data project, is beyond the scope of most organizations. These roadblocks are inherent to any organization, and they start to sketch out a picture of why traditional, discrete, one-time master data projects fail. ![]() ![]() Not to mention the fact that master data is inherently scattered across your organization, contained within different departments, systems of record, and formats. In addition, different departments and individuals in your organization are probably using similar, but not quite the same, terms to describe the same thing (reciprocating pump vs recip. Lurking under that definition, though, are many challenges, including what master data isn’t (it’s not transactional data, meta data, or log data). Put simply, your organization’s master data is a set of terms and rules that define and describe your business objects. Let’s back up for a moment first, though, and consider what exactly master data is. It’s no different for most traditional master data management (MDM) solutions. We all blaze into the new year with the best of intentions, but our resolve normally wanes within a few weeks (hello February). What should be on the to-do list for your organization? Data cleansing. It’s just about spring, which means it’s time to clean house. ![]()
0 Comments
Leave a Reply. |