In the beginning there was data.
We collected it in tables or spreadsheets that later became databases. We complied lists and stuck values against them, and very many of us, in the people profession, still live in this world. There is no shame in living here, because without this data, nothing else could be.
Because we saw data, that it was good.
We pulled reports on things that have happened, decided what went well and what didn’t and tried to learn a lesson from it all.
The data gods said:
“Let data be big”
It wasn’t that long ago that the data gods have bestowed the gift of big data upon us, and very many of us are still not sure what to do with this gift. It may or may not make you feel better that many organisations are not sure what to do with big data. Not just in the people arena, but in every arena.
But we collect it. That’s a great thing, so long as we collect it well. In the people profession, the advent of integrated LMSs (systems that integrate learning and talent and compensation and appraisals and 360s etc.) made it a lot easier to collect all this data.
Since then, we reported more and better, we drew some connections between all the different parts of the human system that makes our organisations happen. We could follow an employee lifecycle from pre-recruitment through to exit.
We benchmarked, we realised that people interactions are complex and multi-faceted and we had a dataset for each facet.
And we saw big data, that is was good.
Confusing, but good.
“Let there be Analytics”
Let’s be honest, big data reports made our lives harder. While other departments, like finance, sales or marketing could draw direct causalities between instance X and result Y, we couldn’t because people interactions are too messy to be causal. We needed to dare and approach the rest of the organisation to understand the impact (real impact) people make.
This is where we (and I) are stepping into a bit of an unknown territory. In data and big data, I can definitely tell you what our profession does, I can’t say that now because I’ve not seen many organisations who have taken people analytics to where it could go. Organisations are starting to experiment, but hesitantly, slowly and overly cautiously.
People analytics is – quite possibly – one of the riskiest analytics projects to take on, because it will require all parts of the organisation to open their books up and share.
ERP is not always the solution
A lot of my data mongering counterparts often say it’s because people data is not collected in the same way across organisations (particularly if they are big or multinational). They think that standardising processes and data across the board is part of the solution, much in the style of implementing Enterprise Resource Planning (ERP) systems.
My personal belief is that ERPs can be a hindrance more than a help. They take a very long time to implement and hardly ever create the simplistic, uniformed utopia it aspires to be. This is simply because reality is different in every location, in every office, in every team.
Having worked in quite a few organisations struggling with these very issues, I strongly believe that allowing variety of tools with output that meet certain guidelines can be smoother, more elegant and overall cheaper than forcing a single process/data logic.
To add a bit of complexity to all this, analytics pioneers has been making bold steps into the subjective world of understanding what value actually is, considering that the return we are looking could be measured completely differently than expected.
Given variety and complexity, an ERP will struggle to make assumptions and discounts and will not be able to factor in the subjective. You need a few humans to man your analytics desks: Data DJs who can see patterns where no one else can. These patterns will lead you to the Garden of Eden – predictive analytics.
You know how I bang on about knowing where your skill gaps are before you hit the wall of incompetence? That’s predictive analytics.
This is only one example of how predictive analytics can work in the human sphere. It is probably the only example where predictive analytics can live entirely in human big data realm.
Analytics allows us to boldly forge relationships between the multitude of data facets. For example:
- Between customer retention figures to people-skills learning
- Between income with perceived changes in leadership skills/styles
- Between employee satisfaction and turnover/EBITDA
- Between remuneration and turnover/EBITDA
The longer period of time you can report on, the deeper and more interesting these relationships will turn out to be.
Once we have an understanding of these, we can ask take it further with predictive analytics:
- Model/scenario costs and implications of recruitment drives (or redundancies)
- Model/scenario impact of capability of skills loss
- Model/scenario costs and impact of training permanent staff vs. hiring short-term consultants
- Predict skill gaps and succession gaps before they become gaps
- Predict mass exodus of staff (that are staff initiated)
And so so much more.
And we will see people analytics, that it is good.
And we will be braver in helping people data evolve.
What do you think? Are we stuck at Big Data phase? Are ERPs more hassle than they are worth? Can analytics be predictive? Let us know! Comment below.
If you would like to learn more about what you can do with your data, get in touch.
Graphic borrowed from DeviantArt.com.