"Big Data" – Views from China HR Leaders in Shanghai

DSM and the Chapman Consulting Group co-hosted the first of our China HR networking series on 12 May in Shanghai. A lively group of HR Leaders discussed three case studies with topics ranging from cloud-based platforms for HR, using data to shape your interactions with the business, and whether or not the ‘bell curve’ is still the best way to assess talent. Over 30 HR Heads attended from a variety of organisations including ADP, Bayer, Delphi, Dow, Electronic Arts, Ernst & Young, GE, GSK, IBM, Siemens, Tesco, Zimmer and many others.

Quality Data In = Quality Data Out

The first discussion centred on Delphi and the analytics transformation the company is going through globally. Rakesh Patnaik, who is in a Global Talent role based in Shanghai, shared a short presentation highlighting the changes Delphi are embracing and the rationale behind those changes. Rajesh shared that, “HR capability will depend on how we manage and process data – we have to make sure that quality data in, will mean quality data out.”

He also added, “The question is whether we can take the data and move our organisation from a reactive to a proactive state. That is the future we are aiming for. Predictive HR is not about what exists — it’s about making proactive decisions possible. At Delphi, we have focused on analytics, access and acumen to cement the changes.”

Aligning Data with the Business

The second topic discussed amongst the group was introduced by Annie Chen, L&D Head of E&Y China, who presented on how to align the L&D function to demonstrate benefit to the business, as opposed to simply appearing as a cost centre. Annie stated that, “Traditionally the data in the L&D function has been focused on the number of hours of training completed, how many participants actually showed up, the budget, money spent per person, and evaluation survey feedback. If we only do this, L&D becomes an operational admin centre — in other words, a cost centre.” She continued, “We now use additional elements to evaluate the benefits of learning: the reaction, behaviour and results all have to be considered. This adds more than analytics alone – we need to have the predictive capability to change the usual thinking of L&D as a cost. Instead it’s a business enabler.”

Collin Tang, who was previously the HR Head for Nippon Paint, shared that he believes taking the data to the business can increase the value-add that HR can offer. Collin shared his experience and successes with data when acting as a partner to the business. He asked leaders in his organisation to examine where the sales revenue was coming from — was 80% of it coming from 80% of the sales team? When it became clear that this was not the case, he then used data to support proactive changes, which resulted in a higher-level discussion with the business.

Bell Curve — Still Effective?

The final presentation was from Jan Arne, our host and the corporate HR Director for China with DSM, who challenged the use of bell curves in leadership assessment. Jan had experience using the data enabled through the company’s performance management system, but in 2013 Microsoft moved away from this approach, and the group debated the best way forward. Overall it seemed that the rationale to drive talent assessment and performance management must depend on the business culture.

Conclusion

In summary the group wrestled with the ‘big data’ topic vs. practical quick wins, which enable a higher-level conversation with the business. A more holistic solution combining data with qualitative input was considered best. It was also agreed that approaching the business with ‘data in hand,’ to speak their language is the first stage of becoming a true HR Business Partner. However, it was noted that moving to this next level requires HR Leaders to have some capabilities and solutions, which not all possess at this early stage of development.

Many thanks go to DSM for hosting this insightful discussion, and we look forward to our next HR networking series, which will be held in November.