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'The Challenge Simply Lies In Demonstrating Exactly How Data Can Make Each Function Within The Department More Effective And More Efficient'

Interview with Michael Gethers, Data Scientist at Salesforce

30May

There has been a growing awareness of the benefits of people analytics over the past few years, with HR departments in organizations of all sizes realizing the necessity of showing credible data to evidence productivity, engagement, and performance in order to become more proactive and make better-informed decisions. The decision to award the 2016 Nobel Prize In Economics to Bengt Holmström of the Massachusetts Institute of Technology (MIT) and Oliver Hart of Harvard University for their work in Workforce Analytics further suggests that the practice is finally gaining the recognition it deserves. However, while the rate at which data analytics is being adopted by HR departments has certainly increased over the past year or so, in comparison to other departments it is still some way behind in terms of maturity.

One sector leading the way, as usual, is Silicon Valley. Michael Gethers is a data scientist on Salesforce's People Analytics team, where much of his work focuses on creating a world-class experience for Salesforce employees by increasing the efficiency and intelligence of their internal operations. We sat down with him to discuss why HR had been late to the party and what companies could do to change the situation, as well as what the future holds for HR analytics. He will also be presenting at the HR & Workforce Analytics Innovation Summit, which takes place this June 19-20 in San Francisco.

How did you get started in your career and what first sparked your interest in analytics?

My interest in analytics really started as a simple interest in math and statistics. After taking my first machine learning course in college I was really overcome by the power and potential of prediction and statistical learning. I loved the logic and theory behind these subjects, and the applications seemed endless. It felt like there were so many directions that I could take these same concepts, and it was really only fortuitously that I ended up in workforce analytics in particular. After having worked as an Actuarial Analyst in Mercer’s Retirement line of business, the opportunity arose for me to jump into the People Analytics space with a team that was seeking to apply predictive techniques to a field that was really flowering before our eyes. I think there is an ever-expanding appetite for data science within HR, which allows for many opportunities for experimentation and innovation. This was and is incredibly exciting to me, and it is what keeps me driven and motivated to solve problems that are still relatively unexplored.

Do you feel HR is behind other departments when it comes to implementing data initiatives? If so, why do you feel this is the case and what can HR leaders do to rectify the situation?

Though I want to be careful not to make blanket statements here, I think it’s no secret that HR is not the most traditionally data-driven department within most companies, and as a consequence it can be quite difficult to get data initiatives to take proper flight. From ineffective data warehousing to an HR workforce that is ill-equipped to properly interpret results of data-driven projects, there are significant challenges in place that can make it difficult or even impossible for data innovators to operate as nimbly and effectively as they otherwise could. Unless these challenges are addressed, HR departments will lack the foundation that is required to construct robust analytical processes on even an ad hoc basis, much less to weave these processes into the fabric of the organization, which is very often the proclaimed objective.

These hurdles are not insurmountable, but they require significant investment and messaging from HR leaders to begin to shift the culture in a more data-driven or data-literate direction. It is not enough to simply bring on a skilled data scientist or analyst and expect that they are going to be able to immediately begin producing valuable insights on a regular cadence without first investing in proper data infrastructure. And when it comes to infrastructure, it is important for business leaders to understand that they often don’t know what they don’t know. If there is really a genuine commitment to data initiatives within the department, there should be open dialogue with the data scientists/engineers/analysts who have the knowledge surrounding how those initiatives could conceivably come into being. What data would we need to have for this initiative? And how much of it? In what format would it need to be? Where will it be stored? How should it be accessed? How will it be cleaned and manipulated? What kind of testing will we have to do? How much manual effort will this require, and how much of the process can be automated in the long run? These are questions that business leaders themselves are not always well-equipped to answer without consultation from those who would actually be producing the models or analyses, and data initiatives can easily stall if they are not fully thought out.

In addition to infrastructure, messaging is key. The entire department should be bought into a data-driven future. Data work is much more consultative than it’s often made out to be, and domain knowledge is absolutely essential to successful data projects. While those actually producing the data product should have some understanding of the domain that their work pertains to, the bulk of that domain expertise, at least in the beginning, is usually going to come from those who have been closest to that work in the past. This cooperation is very important, and can be facilitated by HR leaders through communication of the value of these new initiatives.

How important is it to introduce a data-driven culture across the organization? How is it best achieved?

Generally speaking, very important. As mentioned in my response to question 2, department-wide cooperation on data initiatives is usually required, and this often requires something of a culture shift if the department has not traditionally been data-driven. Unsurprisingly, this can be a real challenge, but in theory it shouldn’t be. Data is tremendously powerful, and when people start to recognize that and understand how it can help them personally, they hop on board pretty quickly. So the challenge simply lies in demonstrating exactly how data can make each function within the department more effective and more efficient. It almost always can, and if you are able to communicate that to key stakeholders, you can generate some real excitement for the potential of workforce analytics.

What do you see as the most important metrics to look at to gauge employee satisfaction?

I’m not sure there is a good answer to this question. ‘Employee satisfaction’ is not a well-defined term, and what it means for one organization can be very different from what it means for another. If you can reduce employee satisfaction down to other, more tangible concepts that are relevant to your own organization, you may be able to derive clearer metrics from those. For example, concepts like engagement, career development, and tenure are much easier to measure than something as abstract as employee satisfaction.

What technologies do you see as having an impact in the analytics space in the near future? Wearable devices are an obvious way to collect data on employees, do you think we will see them being used more in the future? Should they be?

The future of workforce analytics is wide open and could go in any number of different directions. What I expect, though, is that we’ll see a shift away from active data collection (i.e. surveys, scorecards, anything that requires manual data input) toward passive, non-intrusive data collection (collection that happens in the background and does not influence the day-to-day life of the employee). This is where the big data boom began, as passive data collection was built into everyday products and services, and I think we’ll see HR departments follow that lead. This data can be collected more quickly, more accurately, and less abrasively than its active counterpart, but requires more of an initial investment in infrastructure to get it up and running. I think that as more companies commit to data science in the workforce analytics space, that will be seen as less of a prohibitive barrier to entry and more of a small upfront cost associated with massive downstream potential.

Wearable devices seem to be particularly intrusive, and I have to believe that a company initiative that involved the collection of data from them (even voluntarily) would be met with significant resistance. But it is important to remember that people’s willingness to offer up their personal data is almost entirely predicated on trust: trust that that data will not be used with nefarious intent, and trust that they will actually derive some personal benefit from surrendering it. If a company is able to build that trust with its employees regarding wearable devices, then it’s certainly within the realm of possibility that this could be another opportunity for passive data collection.

You can hear more from Michael, along with other leading experts in the field from the likes of Facebook, Airbnb, and Chevron, at the HR & Workforce Analytics Innovation Summit. View the full agenda here.

BONUS CONTENT: Tiffany Morris, VP, Talent Management & HR Business Partner, Sears Holdings discusses data-driven talent management at the HR & Workforce Analytics Innovation Summit in Chicago in November 2016.


 

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