How to lead with data

How to lead with data

With the data science market growing at a tremendous pace, data specialists are in high demand. According to IBM, by 2020, the demand for data scientists will increase by 28% - and HR will be just one of many corporate divisions looking for their share.

It’s something Jesper Helt, CHRO at Commvault, is acutely aware of. Although he’s always on the lookout for suitable talent, Helt concedes that he’s in a fortunate position: his company provides data protection and information management solutions for some of the world’s largest enterprises. “We are a data management company, so we think data,” he told HRTechNews.

Helt has witnessed first-hand the upsurge in demand for HR professionals with data analytics capabilities.

“Ultimately for us, and for any other HR operation, we need to lead with data. The way to build relevance, to position ourselves as strategic partners and to ensure that when the going gets tough then HR is not the first casualty to be cut, is by leading with data.”

It’s been suggested that to bridge this skills gap, HR professionals get on the front foot by training their mathematicians and analysts in data science courses. However, Commvault’s specialty means Helt is exposed to more data specialists than the average HR professional – and it’s evident to him that this talent is not going to come from people with HR backgrounds.

“What’s helping to drive innovation for us in this space is the talent – and that talent may not be found in HR,” Helt said. “It’s really getting in with developers, engineers, data scientists and others, with a view to bringing the best innovations to market; if I’m even a little bit influential I can do some stump work for the HR side as well.”

Helt believes it’s time to recast traditional notions of what constitutes a ‘perfect’ background to move into HR.

“It’s a matter of bringing in almost an alternative to classic HR talent,” he said. “Perhaps it’s people with a finance, marketing or data science background. I think it’s only a matter of time before I bring in someone with a development background into my team because you need that different skillset – the left brainers as opposed to use right brainers – to really help think about how you can visualize, connect and analyze data.”

Helt and his team are becoming adept at ‘cross-pollinating’ data sets. “One thing we need to understand in HR is that the power lies in disparate sources of data. So you start to bring together HR data and sales data, commission data, customer satisfaction data. It’s not easy; many companies can’t do that.”

He cites a classic HR example: high employee attrition.

“We needed to understand why people were leaving after just short periods of time with us,” Helt said. “We could have had a brainstorm about why people are leaving but the real insights only flow when you start to marry the data together. When we looked at the folks that are leaving, where they are going to, their past quarterly performance, as well as the outlook in the sales pipeline, only then did we start to see trends.”

Not surprisingly, Helt said employees who were not earning enough money by benchmarked standards tended to leave more quickly and more frequently, but when their pipeline was factored in – for example, if they didn’t have the ability to make money in the next quarter or two – then the disparate pieces came together and the ‘push factor’ became clear.

Helt said that from where Commvault sits, it’s a short jump to start looking at predictive analytics and proactively taking steps to ensure people don’t leave the company.

“Pull these data sets together and then look at LinkedIn data, for example. You can see who is active on LinkedIn, who is updating their profile. That could either be an indication that we’ve just given them a promotion and they’re happy to share that with the world, or it could be an indication they’re ready to dust off their resume and make themselves attractive to the outside world.

“If you start to marry all these data points together then ultimately I hope we can get to the point where we have an indicator: here is someone who may be thinking about leaving, let’s make sure we chat to him or her and understand what’s going on.”


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