Big Data, People, and Performance: Myths and Must Dos. Nicole Dessain, MA, CPT, PMP Founder & CEO talent.imperative

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1 Big Data, People, and Performance: Myths and Must Dos Nicole Dessain, MA, CPT, PMP Founder & CEO talent.imperative

3 and here to stay! Board members say that attracting and retaining top talent is considered one of the most important levers for achieving strategic objectives. (Harvard) Employee turnover rates are forecasted to rise with 160 million workers preparing to leave their jobs in (Hay Group) Head of HR Analytics is one of the top 10 executive jobs in (Fortune) 2014 talent.imperative inc, All Rights Reserved 3

5 Wanted: definition, training, support, and jobs Big data is high-volume, -velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. (Gartner) 2014 talent.imperative inc, All Rights Reserved 5

6 The evolution of people analytics talent.datafication is the ability to quantify talent-driven organizational value creation and fundamentally change the way companies view talent and predict business outcomes. HR/Workforce Analytics Talent Analytics talent.datafication Employee data for HR the what Talent data for the business the why Talent value quantification for all stakeholders Examples: Headcount Attrition Examples: Predictors of top performance Drivers of high performer attrition Examples: Personalized performance tracking real time TX (talent.experience) = CX (customer experience) 2014 talent.imperative inc, All Rights Reserved 6

9 Myth #3: Big data will replace other decisionmaking factors. Image credit: Junge Karriere Photographer: Samantha Jones Dig up all the information you can, then go with your instincts. We all have a certain intuition, and the older we get, the more we trust it. I use my intellect to inform my instinct. Then I use my instinct to test all this data. (Collin Powell, former U.S. Secretary of State) 2014 talent.imperative inc, All Rights Reserved 9

11 Myth #5: Everybody welcomes talent analytics with open arms. Image credit: Yahoo! Movies An anthropologist might conclude that we are only capable of quantitative talent analysis while drinking beer on our couches. Ultimately, most leaders seem uncomfortable converting subjective judgments into quantitative evaluations. (Tom Monahan, Chairman and CEO at CEB) 2014 talent.imperative inc, All Rights Reserved 11

14 Must Do #3: Instill a data-guided, self-reflective mindset. Image credit: Workforce Magazine Image credit: FastCompany (Photographer: Andrew Whyte) A mountain of scholarly literature has shown that the intuitive way we now judge professional potential is rife with snap judgments and hidden biases, rooted in our upbringing or in deep neurological connections that doubtless served us well on the savanna but would seem to have less bearing on the world of work. (Don Peck, The Atlantic: They Are Watching You at Work ) 2014 talent.imperative inc, All Rights Reserved 14

15 Must Do #4: Empower leaders and employees with analytics tools and education. Leaders Employees 1 Craft crunchy questions Prioritize Develop awareness of unconscious bias Co-design and educate on guiding principals and ethical data governance Accelerate reporting efforts with real-time data insights via intuitive dashboards Keep data-guided talent topics top of mind Think of your employees as talent.preneurs Empower talent with data to drive better job fit and performance Leverage data to assist employees in identifying skill gaps and to access resources Make it easy, safe, and fun to share data (social; gamification) 1 Term coined by Deloitte 2014 talent.imperative inc, All Rights Reserved 15

16 Must Do #5: Create data-informed talent success and experience profiles. Crunchy Questions: What are our key talent segments, who are the high performers in each segment, and what makes them successful? What are our key talent segments, what is the demographic make up of each segment, and what do they value in an employer across the talent.experience lifecycle? 2014 talent.imperative inc, All Rights Reserved 16

17 Case in point: Google 1. Treat your employees data with respect 2. Use data to determine successful attributes in individuals and teams 3. Determine which methods are most predictive in assessing success 4. Empower managers with data to enable behavior change 5. Don t loose the human insight 2014 talent.imperative inc, All Rights Reserved One of the applications of Big Data is giving people the facts, and getting them to understand that their own decision-making is not perfect. And that in itself causes them to change their behavior. (Laszlo Bock, The New York Times: In Head-Hunting, Big Data May Not Be Such A Big Deal ) 17

18 But not every company is like Google Predict job success Enterprise Solutions Company launched new online evaluation with algorithm analyzing answers along with factual information. Result: attrition reduced by 20%. Finding: previous experience not critical success but commuting distance driver of retention. Retention profiling High Tech Company developed statistical profiles for retention risks and conducted custom interventions (mentors, compensation adjustment, etc.). Result: Reduction in attrition rates by 50%. Coaching insights Professional Services Company created a real-time dashboard for leaders with key retention and engagement drivers; color coded for red flags so leaders can take coaching or other actions talent.imperative inc, All Rights Reserved 18

19 So, how do I get started? Define key stakeholders and prioritize crunchy questions. Determine your talent analytics maturity level. Create a roadmap and change management plan. Define needs for capability, coalition, technology, and governance. Start with a quick win or pilot. Don t get discouraged and don t be afraid to ask for help talent.imperative inc, All Rights Reserved 19

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