We have all become familiar with the “Highly Reliable Organization”, which are those companies that avoid the negative outcomes from Black Swan events. They do not succumb to circumstances beyond their control because they are organized in a way that allows them to power through.
Can you say the same about your data? Do you trust 100% of the data that come from your HR/Payroll system? Is it 90%? 80%? At what threshold do you determine that it is unacceptable? If you do not have “highly reliable data”, what does that say about your current system?
You probably don’t trust the reliability of your data when you have found yourself in one of these situations:
- You can’t get data from your system in a timely fashion because it has to be “massaged” before it gets to you.
- You have to manually manage the data on a 3rd party vendor system because the data from your HR System “just isn’t right”.
- You hear about incorrect data from a customer (employee, business unit, vendor, etc.) after the fact and when there is a problem.
Now that we’re in the midst of the second great HR systems migration (to cloud-based systems), extracting data properly is critical for the new software project’s success.
What’s the Cause of Unreliable Data?
Unreliable data can stem from several different sources: bad process, poor system configuration, data entry errors, poorly written interfaces, or other causes. The most likely and overlooked cause of the unreliable data is the original source. Many HR/Payroll systems have been replaced or upgraded in the last decade and the converted data came from legacy systems. The source data was a mess – incorrect, inconsistent and inflexible. But what happened in between that messy legacy data and what is in your current system?
Most data conversions are seen as transactional with the singular goal of moving data out of one system and into another. There are usually two parties involved in the data conversion – a programmer from IT to extract the data and the HR staff or HCM analyst to “scrub” it. The programmer knows how to extract the data from the existing system. Their role and expertise is to know how the data sits in the various tables in the system. But do they know the data, what it means and how it’s used?
If the person extracting the data doesn’t understand its use, then they are less likely to extract the data properly. The HR staff and HCM analyst are usually tapped to be the link from old system to new. However, they usually do not have the knowledge, capacity and expertise to cull massive amounts of data into the correct data to populate the new system. It is not just overwhelming, but an unrealistic expectation.
There is so much data representing so much history. Unless there was a concerted and focused effort to cleanse the data before it was transferred into the target system, the data will remain dirty in a new system. Cut and paste just doesn’t work. When you are migrating to a cloud based HR system, keep the well-known Winston Churchill quote in mind: “Those who fail to learn from history are destined to repeat it”.
What’s the Solution?
The HR professionals who are responsible for this HR system implementation are stretched thin. During an implementation, they are being asked to manage the existing legacy system, learn and implement a brand new system and continue performing their daily work. Stop me if this sounds familiar: “oh, by the way, can you please validate all 400 data points and historical information for 5,000+ employees while you are juggling everything else?”
In my 20 years of experience, the most successful implementations focused on one thing done well – analysis. In addition to a proper Business Needs analysis and testing, an analysis should be conducted on all data exported from the legacy system being imported into the new system.
A lack of understanding of the existing data will surely lead to a lack of integrity and confidence in the data in the new or existing systems. HR and IT don’t always communicate on the same level, so this “interpreter analyst” should be able to speak the language of HR to the IT resources, something that HR often cannot do. And, they should be able to translate IT language to the HR resources to communicate how the data should be interpreted and structured.
On several implementations I have worked on, the technical resources were given a conversion specification with over 100 data points and told to extract the data. Without the detailed knowledge of what each data point represents, they will populate it with whatever data is available. I have seen transaction log date substituted for effective date, job code inserted instead of position code, and hire date confused with adjusted (alternate or rehire) date. I have seen technical resources confuse historical data for static data, resulting in very messy employee data.
My experience has been that technical people want to fill any blank with data, even if that doesn’t exist or isn’t pertinent. As innocuous as that sounds, that may result in duplicate data being inserted into several different fields, which can then lead to a maintenance nightmare in the target system.
READ: The Data Drop
Rather than rely on the IT staff that may not understand the data or the overstretched resources that may not be able to communicate with the IT staff, invest in a successful implementation by engaging an “interpreter analyst” to support the process of conversion.
This may be your existing consultant, a temporary additional consultant or an internal resource. Inserting a critical thinker with experience and analytical skills into the conversion process between IT and HR will help to ensure that you do not end up with a “garbage in, garbage out” scenario. Be sure that your new system contains “Highly Reliable Data”.
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