In anticipation of her upcoming Predictive Analytics World for Workforce conference presentation, Using Analytics in Recruitment to Identify Improvement Opportunities, we interviewed Sue Lam, HR Diagnostics Manager at Shell. View the Q-and-A below to see how Sue Lam has incorporated predictive analytics into the workforce of Shell. Also, glimpse what’s in store for the new PAW Workforce conference, May 14-18, 2017.
Q: How is a specific line of business / business unit using your predictive decisions? How is your product deployed into operations?
A: Shell recruitment receives over 100,000 applications for their global graduate programme each year for fewer than 1,000 technical and commercial positions. The foundation of the recruitment process is the assessments because they provide data and insight on which to make fair and unbiased selection decisions. In order to stay competitive in the market with job seekers, Shell recruitment wanted to create a streamlined assessment approach. HR analytics and assessment specialists collaborated to review and analyze the current graduate assessments to understand which areas should be kept and which areas could be streamlined. The goal of the project was to enhance candidate assessment data, boost candidate experience, leverage technology and make structural changes that improve cost effectiveness, scalability and efficiency for variable hire demand levels. Using data analysis of assessment and performance data, we identified areas ripe for change and the new methodology will launch in early 2017.
Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?
A: I work in the employee engagement and leadership assessment space and it would be helpful to understand what personality and environmental characteristics are most related to sustained business performance in our organization in light of the volatile business environment that we are currently operating in.
Q: When do you think businesses will be ready for “black box” workforce predictive methods, such as Random Forests or Neural Networks?
A: Companies need to have clean and accurate data, be fully comfortable with data analysis, and trust that the outcomes are sound before moving forward with them. Some industries will likely be ready sooner than more traditional or conservative industries. However, I think human intervention will always be needed regardless of what methods are employed. For example, a predictive hiring model may be very sound analytically, but it may increase adverse impact in hiring (e.g., hires only men, people of a certain age group). Humans need to be able to intervene to ensure all workforce predictive methods are fair.
Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?
A: Statistics can appear to be complicated, full of jargon, and scary to HR professionals who aren’t accustomed to working with data all the time. To solve workforce challenges, the biggest hurdle for data scientists to conquer is to get senior stakeholder buy-in. Without executive buy-in, any recommendations from analytics projects will fall flat. We need senior leaders to champion our causes to ensure that a project goes from analytics to action. The first step to getting buy-in is to craft a compelling story about the results. To put it bluntly, most businesses will not care about the details of the analysis so explain the story without jargon. It helps me to think about explaining the results to a friend of mine who doesn’t know anything about statistics. I try to use real-world analogies and cut out any jargon related to the analysis and I stick to using percentages when I talk about the outcomes. People are typically comfortable with percentages and they’re easily understood, so they can help to drive home a compelling point. Using charts, graphs, and other visuals can help with the complexity as well.
The most important part about getting your results across is to tie them back to business outcomes. Many times, I see really interesting analytics projects but the recommendations aren’t clearly linked back to the bottom line. Finally, always answer the question of “what’s in it for me?” for your customer. Your customer has not come to you with an analytics project just because they think it is interesting or they want to try out a new methodology. They likely want to solve a practical business problem that they have. Before starting an analytics project, understand what questions your customer is trying to answer and what they plan on doing with the results. Find out what will happen if the results that you find are contrary to your customer’s views. For example, will a program or process be dismantled? Will they lose budget or scope? Will it create a lot of work or the mobilization of many employees? Understanding your customer’s motivations will help you as an analytics professional to better craft a story and provide recommendations. If the results are contrary to your customer’s thinking, provide explanations for this and follow-up actions. I see statistics as a tool to help me have a more meaningful conversation with the business and not something to be feared. The analysis is just a starting point.
Q: What is one specific way in which predictive analytics actively is driving decisions?
A: Predictive analytics is helping businesses understand the best way forward. If the business has a number of different choices that it can take, it would be most fruitful to understand which has the highest likelihood of success. Subject matter experts can provide input on areas of interest and data scientists can test hypotheses based on this expertise.
Q: How does business culture, including HR, need to evolve to accept the full promise of predictive workforce?
A: I think the business and HR can benefit from being more open-minded about what data tells us (and what it doesn’t). Some organizations may be better at looking at data and making logical decisions from the data, but generally speaking, there is a culture of mistrust when data is involved, particularly if the results don’t match our views. When the analysis reveals outcomes that are contrary to our beliefs (e.g., we should change this process that has been around for a long time because there is a better way of doing things), people tend to get defensive. People may get defensive because they’re worried about how it will appear to other people if their work is being challenged or changed. In situations like this, rather than being defensive, business and HR professionals would benefit from being curious about what produced that outcome and what could be improved easily. To support these behaviors, a company needs to have an innovation mindset, where there is no culture of blame.
Similarly, for analytics professionals, it is important to understand why the business and HR may be hesitant to move forward with your recommendations. Conducting analytics is one thing, but to mobilize people and resources to change a process requires patience and understanding the limitations placed on the business when making changes.
Don’t miss Sue’s conference presentation, Using Analytics in Recruitment to Identify Improvement Opportunities, at PAW Workforce, on Wednesday, May 17, 2017, from 10:40 to 11:00 am. Click here to register for attendance. Use Code PATIMES for 15% off current prices (excludes workshops).