Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
A key trend in 2014: talent.datafication
and the rise of the underdog
@Nicole_Dessain
June 19, 2014
Big data in HR is all over the news…
… and here to stay!
Board members say that “attracting and
retaining top talent” is one of the most
important levers for a...
What does big data in HR really mean?
Wanted: definition, training, and support
A definition of big data
Every minute we send over 200 million emails, generate
almost 2 million Facebook likes, send over...
The evolution of evidence-based HR
talent.datafication is the ability to quantify talent-driven organizational
value creat...
Why are we so scared of big data?
Myth #1: “I don’t work in talent analytics so why
should I care?”
Applications for analytics span the entire
talent.experience lifecycle
• Scenario-based workforce
planning
• Job success
p...
Myth #2: “I don’t have the skills or tools to
manage analytics initiatives.”
 Is data getting
entered consistently?
 Doe...
Myth #3: “Big data means analysis paralysis
and more metrics we have to track.”
Myth #4: “Big data will replace other
decision-making factors.”
“Dig up all the information you can, then go with your ins...
Myth #5: “Everybody welcomes talent analytics
with open arms.”
“An anthropologist might conclude that we are only capable ...
What Would Data Do (aka WWDD)?
Must Do #1: Design a roadmap based on your
level of talent analytics maturity.
Must Do #2: Build analytics principles, coalitions,
governance, and capability.
Talent
Analytics
Framework
Capability
Gove...
Must Do #3: Instill a data-guided, self-reflective
mindset.
The Corporate Executive Board surveyed 500 managers
and 74% sa...
Must Do #4: Empower leaders and employees
with analytics tools and education.
Leaders
 Craft “crunchy” questions
 Priori...
Must Do #5: Balance needs for data privacy
and transparency.
What does this all mean for me?
6C Talent Analytics Success Model™
Case in point: Intuit
Source: http://www.talentmgt.com/articles/7024-intuit-digs-data
“We were spending lots of time with ...
Case in point: Google
o Treat your employees’ data
with respect.
o Use data to determine
successful attributes – in
indivi...
But not every company is like Google…
Job success
prediction
Enterprise Solutions Company – launched new
online evaluation...
So, how do I get started?
 Determine your organization’s talent analytics
maturity level.
 Define key stakeholders and a...
Don’t get sucked in by the myths!
Connect with us!
Nicole Dessain
Founder
talent.imperative inc
nicole@talentimperative.com
(312) 659-6499
talent.imperative...
About talent.imperative inc
Upcoming SlideShare
Loading in …5
×

Big Data = Big Headache? Using People Analytics to Fuel ROI

828 views

Published on

• Interpret trend information to understand the business case for Big Data in HR.
• Examine your fears and assumptions about Big Data.
• Learn from best practice case studies how to demonstrate HR’s contributions to ROI.
• Understand how to engage key stakeholders as part of your organization’s people analytics journey.

Published in: Recruiting & HR

Big Data = Big Headache? Using People Analytics to Fuel ROI

  1. 1. A key trend in 2014: talent.datafication and the rise of the underdog @Nicole_Dessain June 19, 2014
  2. 2. Big data in HR is all over the news…
  3. 3. … and here to stay! Board members say that “attracting and retaining top talent” is one of the most important levers for achieving strategic objectives. (Harvard Business Review) 82% of organizations will begin or increase use of big data in HR over the next three years. (The Economist) Head of HR Analytics was one of the top 10 executive jobs in 2014. (Fortune)
  4. 4. What does big data in HR really mean?
  5. 5. Wanted: definition, training, and support
  6. 6. A definition of big data Every minute we send over 200 million emails, generate almost 2 million Facebook likes, send over 250 thousand Tweets, and upload over 200,000 photos to Facebook.
  7. 7. The evolution of evidence-based HR 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 Reporting (internal data) “Employee data for HR – the what” Examples: • Headcount • Attrition Talent Analytics (internal & external data) “Talent data for the business – the why” Examples: • Predictors of top performance and culture fit • Drivers of high performer attrition talent.datafication (full data integration) “Talent value quantification for all stakeholders” – the how” Examples: • Talent no longer a liability on the balance sheet • Quantify impact of talent on customer experience
  8. 8. Why are we so scared of big data?
  9. 9. Myth #1: “I don’t work in talent analytics so why should I care?”
  10. 10. Applications for analytics span the entire talent.experience lifecycle • Scenario-based workforce planning • Job success prediction based on big data algorithms • Predictive models to enhance mentoring “match making” • Data-driven identification of “regrettable losses”
  11. 11. Myth #2: “I don’t have the skills or tools to manage analytics initiatives.”  Is data getting entered consistently?  Does everybody know how to use current tools & technology?  Have you talked to your current technology vendors about additional training and analytics capability?
  12. 12. Myth #3: “Big data means analysis paralysis and more metrics we have to track.”
  13. 13. Myth #4: “Big data will replace other decision-making factors.” “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)
  14. 14. Myth #5: “Everybody welcomes talent analytics with open arms.” “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)
  15. 15. What Would Data Do (aka WWDD)?
  16. 16. Must Do #1: Design a roadmap based on your level of talent analytics maturity.
  17. 17. Must Do #2: Build analytics principles, coalitions, governance, and capability. Talent Analytics Framework Capability Govern- ance Coalition Guiding Principles • Identify Capability: What types of skill sets and analytics tools do you need? • Establish Governance: Monitor success, and ethical use of data • Create Coalitions: Finance, Marketing, IT, Legal & Compliance • Design Guiding Principles: What are the ground rules for how we use talent analytics in our organization?
  18. 18. Must Do #3: Instill a data-guided, self-reflective mindset. The Corporate Executive Board surveyed 500 managers and 74% said their most recent hire had a personality “similar to mine.”
  19. 19. Must Do #4: Empower leaders and employees with analytics tools and education. Leaders  Craft “crunchy” questions  Prioritize talent challenges  Develop awareness of “unconscious bias”  Co-design and educate on guiding principals  Accelerate reporting efforts with real-time data via intuitive dashboards  Provide guidance on talent-related actions based on data insights Employees  Provide guidance on data privacy, security, confidentiality  Empower with data to drive better job fit and performance  Use data to assist in identifying skill gaps and to access resources  Make it easy and fun to share insights (social; gamification)
  20. 20. Must Do #5: Balance needs for data privacy and transparency.
  21. 21. What does this all mean for me?
  22. 22. 6C Talent Analytics Success Model™
  23. 23. Case in point: Intuit Source: http://www.talentmgt.com/articles/7024-intuit-digs-data “We were spending lots of time with the business trying to understand their needs. And the team worked very diligently toward getting good data into their hands. So as we built credibility as a team, people just started to come to us.” (Michelle Deneau, Director of HR Business Intelligence, Intuit)
  24. 24. Case in point: Google o Treat your employees’ data with respect. o Use data to determine successful attributes – in individuals and teams. o Determine which methods are most predictive in assessing success. o Empower managers with data to enable behavior change. o Don’t loose the human insight.
  25. 25. But not every company is like Google… Job success prediction Enterprise Solutions Company – launched new online evaluation with algorithm analyzing answers along with factual information. Result: New hire attrition reduced by 20%. 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 more targeted coaching actions.
  26. 26. So, how do I get started?  Determine your organization’s talent analytics maturity level.  Define key stakeholders and ask “crunchy” questions to prioritize talent challenges.  Create a roadmap and change management plan.  Define needs for capability, coalition, technology, and governance.  Start with a “quick win” or pilot solving a critical business problem. Create a data-supported storyline.  Don’t get discouraged and don’t be afraid to ask for help.
  27. 27. Don’t get sucked in by the myths!
  28. 28. Connect with us! Nicole Dessain Founder talent.imperative inc nicole@talentimperative.com (312) 659-6499 talent.imperative company page talent trends Group on LinkedIn https://www.linkedin.com/in/ndessain @NicoleDessain https://www.youtube.com/channel/UCzsO_iZBb38uu_Fkzio1Iyg Email us at info@talentimperative.com to receive a free copy of our “Talent Analytics Self-Assessment”.
  29. 29. About talent.imperative inc

×