Provide Insight, Not Spreadsheets!

‘I didn’t go into HR to crunch numbers’.

I just can imagine this comment from HR professionals who are alarmed at the growing requirement for the analysis of HR data.

HR is already an umbrella term for people doing a very wide range of work. However, the growth in importance of analytics has led to the emergence of the need for skills which have not previously been considered.

There has been much debate about whether HR analytics work is best performed by HR people who have learnt to do analysis or by analysts with a grounding in HR.  The author is an HR person who was ‘re-programmed’ to enable him to carry out analysis. Most people who go into HR do not do so with the intention of grappling with statistics. Yet, as a recent survey pointed out: “In the not-too-distant future, it will become impossible to make any HR decisions without analytics. Indeed, analytics capabilities will be a fundamental requirement for the effective HR business partner”[1].

The CIPD identifies three main levels of HR analytics capability[2]:

Level 1 – basic analytics:

The use of descriptive data to illustrate a particular aspect of HR, e.g. absence or annual leave data.  This involves no analysis, except for showing changes in the data over time (i.e. trends);

Level 2 – multidimensional data:

Combining (‘mashing’) different data sets, or types of data, to investigate a specific idea;

Level 3 – predictive analytics:

Using HR data to predict future trends.  This is the most valuable type of analytics, but is the most difficult to achieve, and the least often undertaken.

Most organisations with an HR department are used to seeing Level 1 data, which are largely historical.  In the Deloitte 2016 Global Human Capital Trends survey[3], while 82% of HR respondents viewed ‘people analytics’ as important or very important, only 8% described their organisations as fully capable of developing predictive models. 55% of organisations were rated as ‘weak’ at using HR data to predict workforce performance.  The CIPD ‘HR Outlook’ survey for winter 2016-17[4] showed that only 4% of organisations overall use predictive analytics.

Predictive analytics can sometimes identify the precious ‘unknown unknowns’. These are those trends or problems which no one notices until they suddenly appear out of the woodwork.  Developing the ability to see ‘over the horizon’ in this way and prevent the organisation from being caught out would enhance HR’s reputation and help show its true value to the business.

The ‘so what’ test:

Any analysis has to pass the ‘so what’ test, by addressing a real-world issue and clearly showing its significance rather than just presenting raw data for its own sake.  Data without analysis are just numbers, and we can take a lesson here from the intelligence world.  If you dish up raw intelligence to your customers, i.e. just data with no analysis, you are likely to get one of two outcomes.  Either the customer does nothing with it (because they don’t have the time to work out what it means), or they draw their own conclusions from it.  Either way, this amounts to failing the ‘so what’ test.

Don’t leave them wondering ‘so what?’  The lessons to be learnt from any analysis should be made obvious to the recipient. Is it good news, or bad?  Should they be doing something about it, or should someone else?  Provide insight, not spreadsheets!

No one is expecting rocket science. However, developing basic analytical skills would be a major step forward in enabling the HR function to do itself justice.

Richard Scott is the Chief researcher at HR in Flow Ltd.

For more information contact strategy@hrinflow.com

Telephone: +44 (0)1280 823 702

www.hrinflow.com


 

[1] Global Human Capital Trends 2016, The new organization: Different by Design, Deloitte University Press

[2] www.cipd.co.uk/knowledge/strategy/analytics/factsheet 26 July 2016

[3] Global Human Capital Trends 2016, The new organization: Different by Design, Deloitte University Press

[4] ‘HR Outlook, Views of our Profession, Winter 2016-17’, CIPD



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