As outlined in this blog by our colleagues Iain Hubert and Nigel Lewis, there are a number of reasons why big data and analytics are vital to the to the future of HR functions.
“Today’s human resources departments need to move beyond the traditional soft HR agenda into helping their businesses create sustainable competitive advantage. This means not only becoming more effective and efficient in the ways that they work, but also understanding the impact of everything that they do in the business and as a result being able to pull some of the levers that will improve the overall performance of the firm.”
The advantages of embracing such an analytical and scientific approach are numerous. A survey by MIT and IBM reported that companies with a high level of HR analytics had:
- 8% higher sales growth
- 24% higher net operating income
- 58% higher sales per employee
To understand how the application of data processing and analytical techniques to the data, we classify HR analytics in three key areas – see figure 1 below.
Figure 1: For Capgemini HR analytics breaks down into three key areas
What is driving the need to embrace HR analytics now?
The questions above are not new, so what makes now the right moment the HR organisation to become more data driven?
We believe the answer is in the rise of the Big Data technologies and the associated abundance of data and methodologies.
Indeed, this is clearly evident with the profusion of HR data easily accessed both internally in organisations, e.g. ERP systems or enterprise data warehouses, and externally, e.g. online CV repositories, social media etc.
Moreover, Big Data technologies are presenting the opportunity to store, analyze and take decisions with higher speeds and lower costs than ever before.
For example, Capgemini has designed solutions that in real time match internal or external CVs with available job postings: using algorithms based on natural language processing, these solutions are able to measure the distance of the keywords between the job description and the CVs – matching the best candidates. Trials have shown a precision that can match or surpass human ‘eyeballing’.
And it is worth noting that a variety of data sources can be used e.g. geographical data – to consider the distance between the candidate’s home location and the office, and weather data – for seasonal jobs.
Another example consists of using internal data about someone’s work profile, achievements and results (sales, costs etc) to identify what makes a high performing employee, identifying common traits relevant to specific job positions, which in turn helps in the early identification of high potential talent.
What are the challenges to adopting HR analytics?
Technological enablement alone, although necessary to provide fast and robust solutions, is not enough.
There are a number of other challenges:
- The attitude towards HR when perceived as a support function rather than one able to drive business forward
- Finding a quantifiable business case to start the journey towards data-driven HR
- A lack of analytic skills in the HR team
One should consider the right infrastructure enablers too:
- A robust infrastructure permitting the size of the data and the complexity of analytics to grow while protecting the security and privacy of the data
- A scientific culture pervading the organisation so that data can be read, interpreted and acted upon at all levels
- A continuous measurement of any initiative’s ROI is necessary to guarantee its sustainability
Not least, we need to consider the cultural implications of a pervasive data driven HR. HR is about people; nobody likes to be treated as a number.
Particular attention must be given to obtain the necessary buy-in from all users involved in HR analytics programs. Being open and showing that they benefit the employees as much as their companies, for example giving the appropriate access to the initiative results to all the employees, will be a key part of the success of all HR analytics.
Four steps to successful adoption of HR analytics
Using an agile approach:
1. Start with the business: solve a problem benefitting the whole business not only HR
2. Think big – start small: although it is important to have a roadmap in mind for the evolution of the HR analytics landscape; contain the scope of the initial projects so issues can be easily addressed and quick wins achieved to build momentum
3. Start now: don’t wait until all your data and systems are perfect – starting is the best way to highlight the critical issues so they can be fixed. More importantly, all your competitors are doing it now
4. Grow incrementally: an incremental approach always aligned to the measurable value provided to the business
If you would like to know more, contact: Sébastien Guibert, Big Data Solutions & Programs Director for Capgemini France or Andrea Capodicasa, Senior Solution Architect, Capgemini UK Insights and Data.
This article was written by Andrea Capodicasa from CapGemini: Insights & Data Blog and was legally licensed through the NewsCred publisher network.