Confessions of a benefits manager: Candid helps with data audit


Data is misery. You would think with a central HR system like Choreday, we would have all the HR data we need. Well, think again. For some of our locations, the HR system is connected via payroll so naturally the salary and allowance data is correct; if not, people would soon shout that their pay is wrong. The problem comes in the locations where we do not have that link, usually acquisitions of some sort.

And then there are benefits. While we record all benefits plans on a central benefits platform, purchased at vast expense from Smarmy Consulting, data on individual benefits is sketchy. Choreday records where someone has a car allowance, but if they have a company car, that is captured only on a seemingly random basis. Insured benefits are not captured anywhere by employee, and do not even ask me about pensions. That would be fine if we adopted plans for all staff on a consistent basis, but with all the acquisitions we do there are always a whole bunch of individuals with ‘special’ arrangements.

Total reward statements

Big Bad Boss is still harping on about getting everything straight so we can start putting everything on our total reward statements. My eyes roll. We have been talking about putting benefits on total reward statements since the beginning of time. Every time we have a go at it, we realise the data is too much of a mess and take them off again. So now, he wants to kick off a new project: The Great HR Data Audit.

Don’t get me wrong, I like the idea of clean data; it is a bold and noble aspiration. I just do not think it will work here. Luckily, for once, I am not put in charge of the project, but my name is put against reward data which, let’s face it, is most of the data we hold on anyone. We have borrowed a proper project manager, Pam, from the IT team, and she has created a bunch of fancy charts with tables for us to fill in with key dates and dependencies. I do not know how I am going to get my work done with all this new data to fill in. Pam sets up a weekly meeting to go through the checklist. Oh good, another meeting.

Cleaning data

We have a new site in Belgium which was never integrated properly, and I know its HR data is already causing issues. Let’s start there, I suggest, hoping to bite off a small, manageable chunk. How wrong could I be? The best way to check contract data is, er, to look at the contracts. Sadly, the digital HR records for that site are as bad as the HR system we are trying to check. There are multiple versions of each contract, and we just hope that the most recently dated is the right one. It isn’t. The second to last most closely matches what is in payroll and vaguely matches what we have on the system. It is a giant puddle of data mess.

Just when I think we have got Belgium clean, I spot a new error. Salaries are input to Workday based on their 12-month salary, but like many other countries, Belgium has an extra part of ‘base’ pay at year end and another in the summer. Calculating employees’ bonus on the 12-month salary for this location will be wrong when their contracts say it should be paid out based on their target multiplied 13.92 times the monthly salary.  HR operations gets in a bit of a strop about this because it only works to show the regular 12-month salary for calculations working on an hourly basis like overtime. In fact, we are both right. It is just a matter of getting the system to reflect the right number for both calculations. Having spotted the error, I leave it to the systems folks to figure out the solution.

Finally, when Belgium is ‘clean’ we move onto the next acquisition with dirty data. Pam is like a little rottweiler, chasing deliverables against the deadlines we simply made up at the start. We did not know how long each site would take and now we are getting beaten up for estimating incorrectly. Sigh. Still, it is nice to have Pam as an excuse to keep pinging the local HR managers to clean up their act.

Big Bad Boss is hounding me too. I am beginning to wonder if there is more behind this project than just total reward statements. There is far too much energy and attention on our results. I mentally tick off the possibilities. A redundancy programme. No, we do them all the time, almost annually in fact, and we do not normally worry about data accuracy for that. No, the only thing I can think is a divestiture. I am guessing we are getting our records tidy ready for a data room.

Selling acquisitions?

Sure enough, I hear a rumour that are selling off one of the recent acquisitions, which include the locations with the most rubbish data. I do wonder what our Higher Beings are thinking of buying and re-selling companies as if on a whim. I have seen the legal costs associated with these deals, so we can only be losing money across both transactions. It is a bit like the large-scale redundancy programmes we run every year or so. We pay a bunch of money to let people go, then pay a load more in recruitment and onboarding to fill the roles again later.

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A colleague in finance once explained that we can charge reorganisation costs as ‘extraordinary costs’, which makes our margins look better. Could they be doing the same thing with buying and selling companies? Do we take their revenue and dump all the costs in ‘extraordinary’ when we decide to sell. It would not surprise me.

Next time…Candid talks to head-hunters.