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BREAK FREE of Legacy HR Data Paralysis

Recently we’ve been working with a handful of clients that have chosen to, or are at least exploring the option of, migrating legacy HR/employee data from systems they aim for us to replace or significantly enhance. In certain circumstances, these systems are older and quite antiquated.

 

As we dive deeper into conversations with clients, begin the mapping of data from one system to the next, or simply explore the past reasoning for not taking action, a prevailing theme appears. We think of it as legacy data paralysis: continuing down a knowingly flawed path instead of biting the bullet and taking action to remedy the situation.

As HR continues to move toward a cost driven, efficiency focused model, such as leveraging the advantages of a HR Shared Service Center, the ability to produce the proper analytics to measure and fine tune HR organizations grows in importance.  Put simply, where are we spending our time, how are we using our resources, and are we doing so in a progressively efficient manner that can show results to budgetary sponsors/stakeholders with detailed reporting?

 

What we’re frequently finding are clients caught in a state of paralysis, using older systems with flawed data, inconsistent input, and systems themselves so antiquated that they cannot produce the needed granular analytics.  They’ve become reliant on the minimal output of business intelligence they’re painfully generating currently, but they know they need far better output achieved with far less effort.  Sometimes clients who aren’t using any data tracking mechanism are in a better place to take action than those who have been doing it in a flawed manner for so long.

 

I genuinely feel for these folks who are asked to squeeze a dollar’s worth of analytics out of a nickel’s worth of system.  We’re seeing firsthand the volume of years of freeform data input, which is highly resource intensive and must be cleansed for migration to a new system.  Assuming this data is vitally important to historical trend generation, they become paralyzed, believing the cleansing of this data will be prohibitively difficult.  Still, they continue to generate “dirty data” that’s exponentially nearly impossible to report on instead of beginning to use a system that will offer detailed reporting capabilities.

 

The overriding question here is,” When do you make the decision to quit excising a broken, resource intensive system, model, and/or method of gathering and reporting on data that isn’t working in the first place and accept that not only is change inevitable, but the longer one waits, the more work it will be in the future?”  Our history with data migration, analytics generation, and just plain 18 years of case management experience tells us, THE SOONER THE BETTER!

 

 

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