How HR can retain its relevance as we enter the next decade
Want to take your analytics capability to the next level? Here’s a tried and tested approach. Lead with the strategic objectives, define your roadmap, identify the people-related factors, get senior buy-in, showcase early results, and manage the business change. Think of it as a journey where the team you build will share the same steps on your route.
This article draws on highlights from our “Harnessing the power of people analytics” workshop for the CIPD.
HR Analytics is becoming empowered by a blend of new data and more readily available technology solutions. This blend is enabling a range of analytics; descriptive analytics, analytics for insight and analytics for systems and analytics for change.
Are these really different? Let us explain how.
Descriptive analytics is about understanding what has happened from your core data. Typically, this is synonymous with reporting: what is the gender balance at different levels of an organisation? How far from their workplaces do our employees live?
Prescriptive and predictive analytics is about understanding why events have happened and identifying their leading drivers to see things coming: what is the relationship between gender balance and performance? What are the strongest influences on attrition rates? (hint: it’s often distance to commute)
Systems-based analytics or scenario analysis, explores the impact and inter-dependencies between many-to-many connections: descriptively (e.g. which gender does what activities), for insight (e.g. how fragmented is each person’s work location?) and for change (if these elements of work are digitised, how many people in each location are affected?)
Analytics for change take all the above techniques and combine them to get insight not only into what the organisation is today, but what it can be tomorrow. For example, what will be the impact on headcount, activities and skills if the organisation offers new flexible contracts to parents returning to work? This last is the area in which HR professionals can create the strongest differentiator because the analysis needs to cover things that do not yet exist and are still to be created.
Exciting new work is being done at each of these stages. The innovations can come from new sources of data, from greater frequency of data and new supporting structures or solutions. Each of these offer opportunities. You can also deliver more value in a shorter timeframe from all of these with a structured journey, or roadmap.
James Gardner and I will work through a good structure for an HR analytics journey and give you examples along the way. This infographic shows you a view of the process:
And here are some examples of the ground-breaking work being done:
- New sources of data: people’s skills and personality traits are being mapped by tools from Aon-Cute, Arctic Shores, Ixly and Pymetrics – and this can screen future recruits or possible future roles. Companies describe clusters of skills seen in high performers and get insight into the size of impact on sales and profit. Description and insight, for optimisation.
- Greater frequency or temporal data: EBay Europe reported using daily feedback on good day/bad day at work to find differences between areas, and changes over time to alert managers to problems. Similarly, a major European bank looked at what led to high performance: was it educational background, grade, age, promotion, training, bonuses or the number of sales visits had the greatest impact on sales. Description and insight.
- New structures of analytics: linking people to activities let an automotive service company analyse activity cost; linking people to skills allowed the European Broadcasting Union to analyse skills gaps; linking activities to roles let a European telco redesign its organisation structures and FTEs.
The first three exciting innovations have great potential for insight and value in recruitment and operational applications of HR insight. They all generate value particularly in a relatively stable world. In a known situation, if you understand how the model works, you can profit by optimising it.
We would argue that the fourth innovation is happening now and is particularly interesting in a rapidly changing world. Business models are being challenged and rewritten every day. In this changing world, analytics has to clearly support decision-making to achieve organisational change.
- Analytics for change: driven by enough data to understand:
- The situation: key movements from the organisation’s current ‘as-is’ state, such as how products, work activities or systems are going to change.
- The linkages: what connects through to other aspects (for example, people impact, activity impact or skills impact), so that the organisation can identify risks.
- The options: to test and select actions.
Change analytics has to balance data gathering with decisions on when to move to action.
When we think about people analytics, we are certainly interested by the impact of new data, greater frequency and new analytical methods in the ‘as-is’ model – and we think that the best analytics will combine this with methods not just for optimising, but for re-designing the existing organisation.
Some favourite bloggers that we would highlight in this area:
Andrew Marritt: http://www.organizationview.com/blog_main/
David Green: https://www.davidrgreen.com/blog/
Robert Newry: https://www.arcticshores.com/blog/
Max Blumberg and Mark Lawrence: https://www.linkedin.com/pulse/call-arms-future-peoples-analytics-max-blumberg/?articleId=7597439549596849549&irgwc=1
Rupert Morrison and others: https://www.orgvue.com/resources/