Rubbish in, rubbish out.
We’ve all likely heard this in the world of data and systems. Rubbish data is much worse than no data.
In my role HR Director at The Palladium Group, I pressed upon value of data, and even more quality data. Much to many of my colleagues annoyance when I was constantly chasing the review of data in our HRIS. But it was very obvious when running analysis where it was incomplete or just outright wrong.
Senior leaders quickly blamed the messenger of the person analysing the data, as did the rest of the business when they were trying to provide feedback or make decisions with it and there were things missing or looking very peculiar.
So what needed to happen?
First we needed to make very clear that missing data or errors were down to the person inputting the information, aka hiring managers.
We needed to take ownership of the fact that our systems limited our managers ability to onboard fast, which they often needed to, with incomplete records, so “hold” data was entered, but there was no mechanism to remind them to go back in and correct. So as is to be imagined, when operations is front of mind HR records get forgotten. So we needed onto build a process that either elongated the pre-onboarding to get data in, or have automated reminders to complete hold data. This was restricted due to the system in place…so much more challenging to implement.
And then I implemented a periodical data review. Aligned with the employee engagement surveys. This is where I became a pain in the regional HR teams backside. As part of my role, I reviewed the data, making very clear any and all changes were to be made directly in the HRIS, that each regional HR team is responsible for auditing their regions data.
Why was I doing it?
Because I needed to ensure that the global employee engagement data and results that went out were as correct as possible. And of course, that fact that having good clean data enables you to make better business decisions – that’s what it’s for after all, isn’t it?!?
So what can you do, to move from reactive to strategic?
- ￼Why: Clarify why you’re collecting data and the impact of not getting it right
- What: In order to achieve your why, define what data needs to be collected; reverse engineer from your why so you can identify as many of the required data points as possible. Note: this may grow or shrink as your analysis gets smarter
- How: Where is your data coming from, that is, who’s entering it, in what system, even how many touch points is has both system and human
- Communicate: Share the Why, What and How with the people entering the data so the can understand importance and impact.
- Monitor/Audit: It’s easy to make the mistake that because you’ve done steps 1 to 4 errors will cease to exist. So make sure you have a monitoring and audit process to verify your data is top notch
Your quality data will now let you make the business decision you set out to.