By: Greta Roberts, Conference Chair, Predictive Analytics World for Workforce 2016
In anticipation of his upcoming Predictive Analytics World for Workforce conference presentation, How Eleks is Building a Career Advisor Tool Based on Predictive Analytics, we interviewed Daniil Shash, Head of Data Science at Eleks. View the Q-and-A below to see how Daniil Shash has incorporated predictive analytics into the workforce of Eleks. Also, glimpse what’s in store for the new PAW Workforce conference.
Q: How is a specific line of business / business unit using your predictive decisions? How is your product deployed into operations?
A: We’re into software development business, so our operations are project based teams, that consist of different employees – developers of different seniority levels and technology stacks, project managers, UI/UX experts, tester, product managers and others. Project durations, team sizes and compositions are mostly different. Projects may last from 6 months to 20+ years, as long as our company exists! At the same time team size may vary from 3-4 employees to 100+ employees in one team.
What we were interested in was to understand what projects are successful and how does combination of skills, experiences, trainings and other team member individual data influence project overall performance. We’ve been looking for correlations and causations between different project attributes and team and individual attributes.
This is what we wanted to understand predict – project performance. Actually, we’re interested in moving from predictive to prescriptive analytics, so what we want is not only to predict project performance but to be able to influence it – understand what we need to do to improve performance in terms of specialists involved.
The users of our solution is talent staffing office, as we call it – department responsible for assembling teams for different projects. So our goal is to help them build teams that are capable of delivering highest quality for projects with specific attributes. By using the tool they get support in deciding which employee would be a good candidate for a team and which would not. We’re still working on the project and we realize that where we are now is just the first step. Complete solution not only would recommend available specialists for specific projects but would help our company understand which skills bring more value and so develop them internally or bring from outside of the company.
Q: When do you think businesses will be ready for "black box" workforce predictive methods, such as Random Forests or Neural Networks?
A: Businesses are using those methods for other purposes, including customer segmentation, image recognition and recommender engines, so generally the business is ready now. The question is when will this trend reach workforce management? Now, why are businesses ready to trust “black box” methods in advertisement and marketing but are not in talent and workforce management? What is needed for business to trust such methods in workforce management? I believe the answers are on the surface – proved and predictable performance improvement. Once we will be able to predict and prove performance improvement of using “black box” or any other predictive method – then we will have business trusting and investing in workforce predictive solutions. Stakes are much higher with talent decisions then they are with another targeted audience advertisement, so business leaders want to be sure they are making the right decisions, especially with “black box” methods.
Ability to understand and predict measurable business impact of the workforce related decisions actually opens a whole new set of opportunities for HR leaders. You can only be a real partner to business when you can influence business results and predictive analytics is something that makes it possible. Talent (workforce, employees, specialists) is what makes a difference for business nowadays, all the innovative and game-changing decisions are made by humans, disruptive products are made by humans, human capital is the core business asset (well, maybe not for oil and gas industry, which is not in their best days now). So imagine that you can help business understand how to significantly improve and strengthen their core business asset? This, in my opinion, is the key opportunity for HR leaders in predictive workforce analytics: Opportunity to drive the business forward rather than support its needs.
Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?
A: The main goal for predictive workforce analytics in my opinion is to help in taking better workforce decisions with predictable business impact. In practice, it means that HR leaders will need to work closely with other company executives on strategy and business goals and transform them to workforce and talent related decisions; from training and development to new jobs profiles creation. What does it mean in terms of business culture changes? It means that HR leaders need to be even closer to business and business processes, understand and work on company development strategy, understand financials and be a part of a board for some organizations. At the same time, business needs to stop treating HR as a supporting function but start realizing that HR, as we call it now is something that is as a key
Don't miss Daniil’s conference presentation, How Eleks is Building a Career Advisor Tool Based on Predictive Analytics, at PAW Workforce, on Monday, April 4, 2016, from 3:55 to 4:40 pm. Click here to register for attendance.