We’ve discussed several aspects of technology in this HR technology series. Today I wanted to share a concept that we should be a bit more focused on: prescriptive analytics. Technology now allows us to do more than just process information, it helps us make decisions.
A few weeks ago, I spent a couple of days with the folks from O.C. Tanner at their Influence Greatness conference. (BTW – Wonderful event. I learned a lot and will be sharing more in the weeks and months to come.) I’ve known O.C. Tanner for years and worked with them when I was the president of HR Florida, the state affiliate for the Society for Human Resource Management.
During the conference, they spent a lot of time talking about the value of prescriptive analytics in the workplace. So, I asked to speak with their principal data scientist Dr. Padmashri Suresh, and luckily, she said yes!
Let’s start with a definition. What are prescriptive analytics? And how does it differ from predictive analytics?
[Suresh] Predictive analytics is predicting the most likely outcome of an action. Prescriptive analytics is more pre-emptive in its approach and recommends which of the possible actions or decisions would most likely lead to the desired outcome.
To put this in the context of an HR problem, consider the problem of employee retention. Predictive analytics can help us predict which of our employees are most likely to quit. Prescriptive analytics would prescribe the course of action that is most likely to succeed in retaining these employees.
To help us wrap our heads around this, can you give us another HR-related example of prescriptive analytics.
[Suresh] Apart from employee retention, prescriptive analytics can be used to generate recommendations for training strategies that improve employee productivity, strategies that improve employee engagement, etc. The bottom-line is, if you have reliable and robust data, you can use prescriptive analytics to empower the HR manager in any of the areas that they deal with on a day-to-day basis.
As an HR pro, what is it about prescriptive analytics I need to know? Meaning, do I need to know how the algorithms work?
[Suresh] When it comes to solutions generated using prescriptive analytics, the most important thing that an HR professional needs to understand is the scope of the solution (i.e. what are the caveats associated with the solution and in what context and scenarios is this solution applicable.)
For instance, let us consider the case of Netflix ‘Recommended for You’, a solution powered by prescriptive analytics. Its scope is limited to prescribing customized selection of movies, television shows, documentaries, and other videos for its viewers. But it is not capable of providing music recommendations. Also, the recommendations provided for one viewer will not hold good for others. This level of awareness of the scope and context is sufficient to effectively use solutions that are powered by prescriptive analytics in the HR realm.
Regarding the algorithmic understanding, I don’t think it is essential for an HR pro to understand the math that powers prescriptive analytics. However, high-level understanding of the different types of analytics (i.e. understanding what are descriptive, predictive, and prescriptive analytics) will be useful. A high-level understanding of these concepts will help the HR pro to figure out how best to leverage collaborations with analytic teams within their organizations.
If prescriptive analytics is about suggesting options, do I need to be concerned about the quality of the options?
[Suresh] The concept of ‘garbage in, garbage out’ applies here. The quality of the recommendations made by a prescriptive algorithm will only be as good as the data that is fed into the algorithm which generates these recommendations. So, if you have unreliable or incomplete data, the quality of such recommendations will not be optimal. However, we need to remember that when there is incomplete or unreliable data, whether we get a subject matter expert to look at the data and decide on the course of action, or use prescriptive analytics to arrive at a decision, the decisions will not necessarily be optimal.
On some level, this sounds too good to be true. What’s the downside to prescriptive analytics?
[Suresh] Although I would not call it a downside, prescriptive analytics is an iterative process and requires time to collect data and fine-tune the prescriptions given by the algorithms.
If I want to learn more about the potential of prescriptive analytics, are there resources you can share (i.e. blog post, books, etc.)?
[Suresh] Due to the pervasive use of analytics in all fields and industries today, there are several success stories of businesses that are powered by prescriptive analytics. I think the official blogs of any of the technology big-wigs that use prescriptive analytics, like Netflix, Google, Spotify, etc., are a great place to learn about the impact and influence prescriptive analytics can exert on the bottom line of an organization.
Although not exclusively about prescriptive analytics, another great read would be Nate Silver’s “The Signal and the Noise”. It is a good read for someone who wants to understand the potential of analytics but not necessarily delve into the math of how it is done.
A HUGE thanks to Padmashri for sharing her expertise with us. She didn’t mention it but one of the things that O.C. Tanner does really well is research. If you haven’t seen their 2018 Global Culture Report, you might want to check it out. It shares what over 15,000 employees and leaders across six continents say about the current and future state of workplace culture.
Analytics are the future of HR. As Padmashri said, we don’t need to know the math, but we do need to have an understanding of how prescriptive analytics works. Not only for our role as human resources professionals, but for our role as business partners.
Image captured by Sharlyn Lauby after speaking at the SHRM Annual Conference in Las Vegas, NV16