Data Science is not Enough for the Success of HR Analytics!

The Geeks arrives in HR,” an article by Josh Bersin, published in Forbes doesn’t require any introduction. Many such articles have been published in mainstream magazines, and by industry thought leaders, since then. Tom Davenport and DJ Patil published an article in HBR, titled “Data Scientist: The Sexiest Job of the 21st Century” which defines Geeks’ most basic, universal skill is the ability to write code. However, this has evolved to next level where sophisticated coding skills like machine learning, etc. are not sufficient enough to meet the need of the hour.

Which piece of the puzzle is missing then?

Most of the Data Geeks follow a defined standard framework when they solve any business problem, and that includes (usually):




The issue with the above framework, it doesn’t take customer end point of view as the core of the analysis. While on the other side, one of the common frustration that we keep hearing from Data Geeks (no offenses to anyone, I am also an HR Analytics professional) is that HR don’t understand our language. The issue is not solely HR function competency but missing design thinking from the HR Analytics solutions. The good news is that we can’t escape anymore from this. “Design Thinking” has arrived at HR.

Global Human Capital Trends 2016 report by Deloitte lists “Design Thinking” as one of the ten HR trends this year. Josh Bersin, Marc Solow and Nicky Wakefield articulates that Design thinking takes aim at the heart of unnecessary workplace complexity by putting the employee (customer) experience first—helping to improve productivity by designing solutions that are at once compelling, enjoyable, and simple.

The above-described mindset is not limited to HR systems and processes only but equally important and critical for HR Data Geeks too to adopt, while designing analytical solutions. You need to bring creativity and science together to deliver expected business impact. Warren Berger is the author of GLIMMER: How design can transform, business, your life, and maybe even the world, defines the question, care, connect, and commit as the four phases of design thinking.

Intuit takes this approach to the next level by bringing design thinking and data sciences together to craft their in-house design principal of “Design for Delight.” D4D method puts “Delight of the customer” at the center, with deep customer empathy, go broad to go narrow, and rapid experiments with the customers at the periphery.


Here is an another example where two different organizations, IDEO (design thinking) and Datascope (data drive solution provider), came together to solve business problems by leveraging creativity and science.

By now, we have established that impactful measurement begins with people. However, the question is that how do you go about creating deep customer empathy which helps you build what they want. Especially, when they don’t know what they want. Luma Institute has created a framework to help choose the best tool for each step of the innovation process, based on the people you’re designing for and the complexity of the systems in which you operate. Luma distilled the portfolio down to 36 of the most effective tools for innovation—the majority of them in common use—organized into three categories: looking, understanding, and making.

Google Ventures has created a 5 days sprint model based on design thinking that can be applied to any product or solution design. It makes you fail fast, learn and pivot to improved prototype without wasting tangible/intangible capitals.

Can these models be applied to HR Analytics solutions as-it-is? I firmly believe so.  Here are the possible areas:

  • Building a proposal for funding for HR Analytics competency
  • Development of People MI Packs or Dashboards – Get maximum ROI from your BI tools investment
  • Data-driven conceptualization of new HR processes such as Performance Management, Employee Engagement, L&D, etc.
  • Decision making of new tool implementation
  • Implementation of HR strategy based on predictive model outcomes such as retention strategy based on attrition model

Do you have examples of an application of design thinking in HR Analytics? Share it.

10,809 total views, 3 views today



Web Master