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Project 5: Workforce Analytics – Search for Insights PowerPoint Presentation
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Project 5: Workforce Analytics – Search for Insights

Project 5: Workforce Analytics – Search for Insights

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Project 5: Workforce Analytics – Search for Insights

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  1. Project 5: Workforce Analytics – Search for Insights Jeff Buchmiller Business Research & Intelligence Group Alliance Data January 2013 214-494-3431

  2. Customers get more • products they want • Customers save money • Company performs better

  3. Workforce Analytics Function – CLC Model Data Analysis – 2012 • Identify and focus on high-priority metrics and analysis • Manage data collection and metrics data inputs • Ensure data integrity • Perform advanced statistical analysis • Conduct predictive modeling and behavioral modeling • Calculate ROI in selected areas • Establish data warehouse specifications Service Delivery & Consultation – 2011 Support business unit HR executives and other line leaders and executives with HR data analysis and project management support Market HR metrics services internally Provide informal metrics subject-matter expertise and education Train clients to understand HR metrics Help clients integrate analytics into current decision-making processes Maintain service quality Solicit feedback from key stakeholders and clients Content Creation – 2013 Develop new metrics products Prepare ad hoc reports and analysis Design and maintain the HR dashboard Integrate user feedback into continuous process improvement Modify/tailor content based on business unit needs and user feedback Clearly communicate information and present findings in a relevant, compelling, and actionable manner

  4. Workforce Analytics Function – CLC Model • Typical Stakeholders • CHRO & HR leads 100% • Business executives 67% • Board of Directors 50% • CFO 33% • Typical Competencies Used • Identifying proper data for analysis 100% • Ability to embrace multiple perspectives 95% • Data access/influence skills: obtaining data from others 90% • Writing/communication (make statistical results understandable) 79% • Basic univariate statistics (mean, percentiles, etc.) 79% • Root cause analysis (identifying causal paths) 74% • Advanced univariate statistics (correlation, differences, etc.) 58%

  5. Workforce Analytics Data Model – Excerpt 1

  6. Workforce Analytics Data Model – Excerpt 2

  7. Workforce Analytics Data Model – Excerpt 3

  8. Workforce Analytics Function – CLC Model • Purpose • Identify a key insight into how the organization works or experiences issues or how its people behave to create value • Which can be used to support a business management decision that enhances the effectiveness of the organization and its people • Which generally means to experience fewer issues, to deliver better products & services to customers, and to create more value

  9. Workforce Analytics Function – CLC Model • Function • One of discovery, based on statistical principles and/or calculations • It is very time-consuming – and sometimes very difficult – to identify the most important factors in organizational effectiveness, because there are huge numbers of ways to slice the available data when looking for a distinction or a pattern • This project seeks to implement an automated insight generator • It is fine to generate “false positives” that end up not being interesting, along with some truly interesting and previously unknown insights are generated in the output so that they can be identified quickly • The reality of such a vague concept as “interesting & insightful” is that there is no objective right answer, nor even necessarily agreed-upon great answers, because so much of the target for this work is subjective and specific to each individual user • This project is to create a small sample of “artificial intelligence,” for those who like to categorize software programs, which can aid the Workforce Analytics function at Alliance Data

  10. Workforce Analytics Function – CLC Model • Deliverables • A software program that is able to: • Load a range of data sets as input • Search for the most promising insight candidates • Follow guidance provided by the user about what is interesting & insightful • Produce a list of the most promising insight candidates discovered • The software will be used to search for insights in various workforce data sets, such as: • Terminations • Individual performance • Organizational performance • Employee engagement • Rewards