Analytics for Talent Management (ATM) Certification
Why Should You Attend?
How do you usually make your decisions about talent? Rely on your experience and intuition? That’s a start, but it’s not enough. To be as effective as possible in HR you have to be able to make decisions based on data and evidence—the same way people in the line of business do.
In fact, if you want to prove and communicate the value of HR and talent programs to the business, you need to possess some foundational analytical skills. We’re not talking about the ability to do statistical calculations—most organizations have experts who do that with data.
It’s something that is more valuable—increasing your ability to gain insight from data and use it to communicate and make decisions.
HR can’t be all about feelings—it is the combination of understanding people, knowing the business, and using data for decisions that will make you more of a strategic partner and more valuable to your clients.
HCI’s Analytics for Talent Management will arm you with the terminology, skills and knowledge necessary to make more evidence-based decisions. Topics covered in this course include:
Speak the Language of Analytics
Get comfortable with basic statistical analysis and terms that will enable you to engage meaningfully with data specialists.
Bring Rigor to Your Approach
Develop and test a strong hypothesis, use natural experiments and analytical models, and avoid confirmation bias.
Design Effective Analytics Initiatives
Apply data to talent management initiatives in talent acquisition, engagement, retention, and workforce planning.
Win Support for Your Findings
Communicate your findings in a compelling way that gets attention, addresses objections, and leads to action.
Take It to the Next Level
Identify the skills and next steps to improve your team’s analytics practice, using resources you already have.
Who Should Attend?
This course provides foundational knowledge and skills in using analytics for HR generalists and practitioners. If you want to learn how to do statistical calculations or have an MBA, this class isn’t for you. On the other hand, If you want to increase your credibility with clients, make better evidence-based decisions, and improve your ability to draw insight from data, this is the course for you.
Earn Credits Towards Leading Industry Certifications
This certification has been approved for 11.25 Business recertification credit hours toward aPHR™, PHR®, PHRca®, SPHR®, GPHR®, PHRi™, and SPHRi™ recertification through the HR Certification Institute. SHRM has pre-approved this certification for 11.25 Professional Development Credits (PDCs) toward SHRM-CP℠ or SHRM-SCP℠ Certifications.
The use of this seal is not an endorsement by HR Certification Institute of the quality of the program. It means that this program has met the criteria to be pre-approved for recertification credit. Human Capital Institute is recognized by SHRM to offer Professional Development Credits (PDCs) for the SHRM-CP℠ or SHRM-SCP℠.
The Transformation of HR
- Why analytics matters
- Types of analytics: descriptive/predictive/prescriptive
- The analytics continuum
- The Talent Management Value Chain: What outcomes can HR directly impact?
- Case Study: Talent Acquisition at NCR
Asking Good Questions
- What’s a good question?
Developing & Testing Your Hypothesis
- Hypothesis: a possible answer
- Knowing what to measure
- Use path diagrams to identify drivers and surface hypotheses
- Correlation does not equal causation
- Test your hypothesis with rigorous scientific methods
- Sample size and control groups
- Regression modeling
- Assessing p-value, R, and R-squared
Common Errors to Avoid
- Review results in context
- Confirmation bias
- Difficulties with data
Types of Data
- Structured and unstructured data
- “Big data”
- Common sources of data and how to integrate them
- Privacy and confidentiality concerns
Handling Data: Hands-on Practice
- Spotting mistakes
- Missing data
- Standardizing data
- Case Study: Manager Performance and Employee Engagement at Google
Working with Data: Hands-on Practice
- Clean and prepare data
- Integrate data from multiple sources (HRIS/LMS)
- Generate hypotheses based on the data
- Standardize the data
- Test hypothesis using pivot tables
- Extend exploration of data using multiple regression analysis
- Case Study: Talent Retention at Credit Suisse
Moving Up the Continuum
- Keys to success in analytics
Telling a Story with Data
- The importance of narrative
- Graphics and visualization
- Objection handling: How to handle pushback
Building a Team
- Gathering the skills you need
- Getting stakeholders aligned
Tips and Inspiration
- Some questions
- Start small and low-tech
- Case Study: Workforce Planning at GE Aviation
Make a Plan
- Discuss challenges and potential solutions