Current trends right now, due to both technology and the Covid crisis, have brought terms like “the Fourth Industrial Revolution”, “artificial intelligence”, “digitisation” and “the Internet of Things” strongly to the fore.

By Shaun Barnes, executive director at 21st Century

Sadly, for many these remain just that – words and terms full of promise but still short on immediate practicality and use.

While our smartphones, tablets and laptops have many applications that already utilise these concepts, how do we apply them in our daily work-life and in our workplace in ways other than those offered by large and expensive Enterprise Resource Planning software platforms?

This is an issue that plagues many companies – how to extract and interpret the masses of staff data each company actually possesses. Make no mistake, data mining does not carry that name for no reason.

It is just like resource mining, in that if it is not correctly placed and executed, it can become a large waste of time and money.

Which is why so much data and information that can guide us in how we operate, staff, train and resource actually slips us by every day.

The other problem with data is what it actually tells us. If my healthcare device tells me what my heart rate is, I need another data point to indicate what the true value of that heart rate information is.

Benchmark heart rate statistics for my age and height will soon place my own heart rate into perspective and will tell me whether my own data point is good or bad. Which will guide me as to how much fitter I need to become. Without that benchmark, my own data in isolation is not of much use and does not provide a required performance target.

So we find ourselves with a horde of information within our organisations, but not much to do with it.

Are our divisions and departments understaffed or overstaffed? Are our team sizes too big to be effective? Or too small so that we need additional supervisory and management positions that come at an added cost? Do we have too many support staff in terms of their ratio to operational staff?

Because as we all know, operational staff generate our revenue while too many support staff, as necessary as they are, just consume it.

Our own data points on all these questions are not very useful in isolation. It is only when we can compare ourselves to data points from organisations that are similar to ours in terms of size and industry that we will get a true reflection of where we are.

And that is difficult – industry competitors are not known for freely sharing this information between each other. At both leadership and departmental levels, there isn’t always a lot of information available about what works and what doesn’t.

Another useful way of using internal data that is often overlooked by organisations is the ‘best in practice” internal benchmark.

This needs to be used selectively, though, and only where you can compare apples with apples. It is not very useful to compare service divisions such as Finance and Human Resources, as there are often differences in delivery and processes that render such internal benchmarking biased and not useful.

But where an organisation has multiple operations of a similar nature, such as factories or mines, it is useful to examine the staffing and productivity data and establish what sets your best performing operation apart from the rest. That can then be used as the “alternative data point” that drives your comparisons. All other operations should be targeted with matching the results of the top internal entity.

In a time when many companies are having to consider retrenchments or staff reductions as a matter of commercial survival, it is vital that such actions are undertaken in an ethical way (due to the socio-economic situation we have in the country) as well as being legally and morally defensible.

No responsible company wants to add unnecessarily to the country’s number of unemployed.

Companies are also obliged by the Labour Relations Act to demonstrate how they arrived at any staff reductions or re-deployment actions that need to be taken.

Using your workforce data properly is thus a vital part of satisfying both these requirements. But this is somewhat more difficult in practice than many of the technological trend reports we are faced with at present.

There are a few simple rules to follow when using workforce data:

* Make sure you are comparing apples with apples, whether using internal or external benchmarking. Ensure you have considered all the variables that need to be considered before proceeding. Because you can be sure that someone will point them out later on. It is no use comparing an open-pit mine with an underground operation or an automated factory with a manual labour process.

* Make sure you consult with the areas you wish to benchmark; they will be the best source of the information and variables you might miss.

* Ensure that you extract up-to-date and accurate data. While this might sound simplistic, it is surprising how often our data is incorrectly calculated or is missing an element that would change the entire hypothesis.

* If you are benchmarking externally and using a service provider, make sure they have an appropriate data set that suits your organisation and also adheres to the “apples with apples” rule. If a service provider cannot match you with their database, rather do not attempt a ‘Plan B’ with benchmarks that are more removed.

Following these simple rules will help you to start making use of the workforce data you have. While benchmarking and workforce data analytics is never an easy process, putting steps such as these in place will ensure that you make a start to extracting the full value which modern workforce analytics and data can offer.”