Analytics Nirvana – Linking Up The Dots - Analytics in HR

Analytics Nirvana – Linking Up The Dots

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...

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…

Click here to continue reading Gareth Jones’s article.

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