The rise in People Analytics, both in terms of the subject and the technology, has been phenomenal, especially in the last 12 to 18 months. What was once almost an afterthought has now become front and centre in the HR and resourcing domain, and also an agenda item for the wider business. A combination of new and better technology, new data sources, new ways of looking at existing data, better visualisation and a significant all round improvement in the experience of working with data has driven truly new levels of insight. This in turn has provided new evidence to back up or debunk previously unsubstantiated assumptions, or led to completely new discoveries that are then packaged up into case studies and shared widely across the domain, via the social web and conferences.
This represents a huge shift in the data landscape and the potential hidden therin. Only 5 years ago, we lived largely in an era dominated by narrow sets of structured data and 'reporting', some of which was very good, but broadly speaking (on the people or 'human' front anyway) was pretty average to poor. Today, we live in a world where profiling an individual – whether it be for recruitment, assessment, development, management or exit – can not only incoporate new and previously unconsidered, or alternative, "consumer" data sets: health and fitness, location, even voice and movement for example. These data sets also consist of unstructured data, or content such as social updates, likes and choices, and email content. The list is extensive and ever-growing.
We not only measure numbers, we now measure words, phrases, movement, conversations, networks and a whole host of peripheral indicators. When taken in isolation, the insight they can provide is limited, but crunched together with the other sources in a data model, those all-important patterns begin to appear. Software solutions exist in the marketplace that can gobble up huge amounts of disparate people data – in some cases, including paper records, phone transcripts and video/audio – and blend it all together to deliver really interesting and powerful insights.
However, there's a problem. These points of insight still represent a series of dots – individual points along the way in the employment life cycle. Compared to the way we manage the rest of our lives as consumers, the whole employment journey - end to end, applicant back to applicant - is an appallingly disjointed and poorly managed affair. And nowhere is this more evident and relevant than in terms of data management. That journey consists of a series of technical and human interventions that are at best mildly engaging, at worst, almost impossible to negotiate. And each one demands increasing amounts of personal data.
In theory, each step along the path provides more and more insight. In theory. There is no doubt that in some cases, where organisations have improved their point by point solutions, there have been some incredible wins and stories. But the challenge we face is that the data landscape, especially for that end-to-end employment journey, is still a series of disjointed and unconnected dots. The ultimate value, the People Analytics nirvana, for the organisation AND the individual (lets not forget You and Me; it's our data after all), must surely only be visible and achievable through the proper and seamless joining up of these rapidly-growing point solutions and data sets across the entire lifecycle. A single, rich, contextual view of the individual that is enriched with every step.
So why don't we have this yet? What’s getting in the way? 2 things:
Technology - the architecture of the employment journey landscape is a hotchpotch of enterprise systems covered in limpet-like niche or best-of-breed solutions. Sometimes this is executed well, and progress is definitely being made. But again, largely, there is no fluidity of data and insight for the individual in the journey. Things are changing, but it’s far from seamless and, with Fosway reporting that over 50% of HRIS applications are still on-premise, it’s easy to see that shifting that landscape quickly will be hard.
Denial - it’s funny, but when someone comes up with an new approach or technology that delivers measurable insight leading to increased engagement/revenue for the organisation from customers, we are all over it. Yet when it comes to our people - candidates or employees - we seem to back away with caution. I believe it is because the more we know about and can quantify human beings, the more that insight is pointing a challenging finger at the fundamentals of how we lead and manage people at work. This is incredibly uncomfortable for some organisations and their leaders. Shifting traditional mindsets and behaviours around these things is really difficult, especially where it challenges existing, well ingrained but ultimately limiting beliefs about management and leadership. I anticipate this will get a lot harder before it gets better.
A final thought - I can't help but notice the irony in the fact that, just as we are using data science to break new and incredible ground in understand the human being, we are simultaneously working our hardest to use the same science to eliminate most of them from the workplace altogether.
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