keyboardIn an ideal world we would have access to all the data we need in order to make the right business decision(s). We now live in the world of the internet of things (IoT) and big data. How do we turn all that information to benefit us?

Lets look at the most important part of any business. Its people. When it comes to employee data almost beyond dispute payroll is possibly the most up to-date “true” source employee information. Lets face it, employees want to ensure they are paid correctly so they will make sure the payroll department is updated on any relevant information.

Beyond payroll we can then look to augment data from other systems such as talent management (like a Workday or SuccessFactors), rostering or scheduling (such as EmpLive  or TimeTarget), recruitment,  onboarding, work health and safety (WHS) and any other employee related systems.

Now depending on what you are trying to achieve you may also want to overlay finance, procurement, industry trends and benchmarks etc. And then don’t forget that you need to “normalise” the data to ensure consistency … so that it remains true.

So depending on the breadth and depth (not to mention historical information across all systems) of your information you could end up with a lot of data to sieve through.

Too little (or the wrong) data and you probably will end up drawing the wrong conclusions. With too much data you could also end up in a similar situation (Can’t see the forest for the trees!).

Before starting down the path of any data project you need to plan what you may want to achieve in the future, even though you may not want to analyse to that extent initially.

Big data is about analysing large volumes of data from multiple disparate (in some cases) sources. What I call FAT data is when all data (even if irrelevant) is gathered. My preference would be always to plan for the future but live in the now. Have contingencies in place, foresight and strategic … not overly restrictive. Gather phat data.

Now when we talk analysis the are several things we also need to consider. What are the options?

There is straight reporting including visual reporting which can just as easily be manipulated in pivots in MsExcel (possibly the most common payroll and HR system in the world) then there is true analytics utilising historical and industry benchmarks and comparative (and predictive) data something like an Anaplan, Adaptive Insights, Tibco, TM1, SAS …. which can be used to help manipulate and gain insights from.

But is the data enough? The simple answer is … sometimes. It really depends on what you are trying to analyse (obviously!). As an example, if you are looking to measure an employee’s performance on an open cut mine site you may want to consider weather patterns (it was much hotter this year) and taking into account there were more public holidays over that same period this year. It could also be because of a new team member who needed more help.

A data analyst I have a lot of respect for once said to me the danger in giving numbers to him and his team was they didn’t have the context. Quite often it’s the context that makes  all the difference.

So when looking at the numbers don’t forget to look through … and beyond the numbers.

 

First published on Linkedin: https://www.linkedin.com/pulse/beyond-numbers-data-analysis-stefano-stef-masiello-

 

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