When clients come to us looking for services, they are often focused on metrics like system availability, adherence to implementation timelines, and file feed frequency to vendors. Don’t get me wrong, these are important and we hold ourselves to a high bar. But in 22 years of business, no one has ever asked about what we do to ensure data accuracy. In fact, our best efforts to get clean data frustrate people who don’t understand why we just can’t make do with what we have.
You’ve certainly heard the phrase “bad data in, bad data out”. It’s a pretty straight line from the quality of data to your employee’s experience. When bad data gets kicked down the road in the process, it becomes ever more costly to address, taking more time, more effort and more resources to correct. The best time to “fix” the data is before it gets saved to a system, as it is being entered. System availability and files to vendors become less important if you only have bad data to feed them.
Data quality starts with the source file. How good are the systems you consider “systems of record”? Usually this is an HRIS or payroll system and some pieces of information are VERY accurate. Others…well you know which ones are a little sketchy. By all means, pass the data you know is good and is well maintained. But be honest about the suspect data which is incomplete, defaulted, or erratically maintained. Take a long hard look and decided if the data is really important. If it is, then either take the time to get it in a good place, or work with your vendor to identify a path to clean it up and keep it clean. (Data Audit) Both the data source and system of record are important concepts so you know who is responsible for maintaining and sharing the most accurate version of data. Dependent data is notoriously incomplete and inaccurate. Unless a company has devoted concerted efforts to collect and maintain it, we recommend using an approach that gets it validated. It only needs to be done once. But that one-time effort is critical.
Once data is in a system, then new data being collected also needs to be accurate and complete. You might think this is an obvious function of any computer system, but we’ve seen a lot of data from other systems and…we can only say…verify what is being checked. Robust systems have the ability to alter what is checked and how it is checked based on the requirements of both your products and your vendors.
Your data is only as good as the worst piece.
Data Quality Matters.