Perspectives in Talent Management

Transcription

1 Perspectives in Talent Management Developing Analytics & Decision Science Teams at Ford Motor Credit Company Alfredo Cateriano VP and Chief Analytics Officer Ford Motor Credit Company October 2015

2 Stating the obvious Data explosion and the automotive industry. Analytics has the ability to influence decisions across the OEM productmanufacturing-marketing continuum. Given the magnitude of operations, the multiplier effect of analytics at OEMs is enormous. Harnessing data from connected vehicles, mobility experiments and autonomous vehicles presents entirely new paradigms. The advent of big data, combined with the growth in computing power, is making analytics a competitive must-have. At financial institutions such as Ford Credit, data has long been recognized as an asset, and analytics is a competitive advantage. 2

3 The implications Industrial companies are investing heavily to compete on analytics. ZDNet-Sep 16, 2015 CBS Local Wall Street Journal-Dec 15,

4 Managing Analytics Talent Building analytics teams to deliver business value. D A Identifying Talent Who is a Data Scientist? Data Science programs B C Organizing for Success Align team composition with analytics maturity Build interdisciplinary teams Onboarding and Retaining Talent Steep Learning Curve Business Relevance Success Factors Executive Sponsorship Analytics Culture Parallel Investments 4

5 A Identifying talent Who do we look for? Cannot just be thinkers, need doers able to get hands dirty with the data Ability to keep current with changing technology platforms and software Continuous learning philosophy Where we spend the most time Problem formulation Ability to translate complex concepts for business audiences Business mentoring Business Center visits Data and Software Dexterity Innate curiosity Business Acumen Math and Statistical Skills Hardest quality to find/identify Many candidates look good on paper Critical thinking skills are key Curiosity about the business, ability to connect the dots Ability to transform into internal consultants and evangelists Deep quantitative skills (math, statistics, economics, physics ) University alliances, internships, recruitment Core quantitative and modeling skills/abstract thinking Considerations: Serious competition for analytic talent. Immigration policy and approach candidates with strong quantitative skills are disproportionately non-us born. Balancing the growth and popularity of data science programs with the need to create multidisciplinary teams composed of individuals with advanced degrees in strong quantitative fields. 5

6 B Organizing for Success Team composition morphs as the data landscape matures and as business adoption grows. Descriptive What happened? Predictive What will happen? Analytics maturity Prescriptive How can we influence outcomes? hindsight insight foresight Data and Technical Dexterity Math and Statistical Skills Business Acumen Low data maturity/low business adoption. Need entrepreneurial data science teams. Business integrators are key to success. Greater data maturity/some business adoption. Need stronger technical teams. Creativity and visualization are critical. Growing importance of data munging - ability to bring together different pieces of information in insightful ways. Complex, Big data/high degree of business adoption. Operationalized analytics/realtime delivery of insights Need analysts adept at using big data technologies/machine learning, etc. Business partnership complements technical abilities of data scientists. Technical integrators are key to success. Considerations: End-to-end ownership of the data. Close alignment with IT, Business & Data Governance functions. Important role of analytics integrators. Centralized vs. embedded organizational models. 6

7 C Onboarding and Retaining Talent Getting from talent to productivity. Hmm I ve never had to deal with such massive amounts of data before. And what is this rule they keep talking about?? Think I m finally getting the hang of this - data, tools, and how I fit into the business It s all coming together now. I really see the impact I am having I love it here! I have nothing to worry about I ve got a great quantitative degree. Sexiest job of the 21 st century - here I come!!. Software Training. Data Immersion. Hands-on projects. Buddy system. Peer-to-Peer sharing. Technical seminars. Business understanding. Problem formulation training. I ve got some cool insights; but why is the business team not jumping for joy? Frequent business interactions. Change management. Reiterate the importance of business relevance. Considerations: Individual autonomy and business relevance are key to retaining the right analytic talent. Technical track offers an alternate path for advancement. Personnel continuity plans to manage attrition. Analytics pay scales may seem relatively high, particularly in the broader automotive/manufacturing context. Protecting analytic intellectual property in a highly competitive market for analytic talent. 7

8 D Success Factors Harnessing the power of Analytics Analytics is about the effective use of data to drive business decisions. Harnessing the power of data requires creative Data Science teams. The most successful data science teams will be internal consultants who are trusted advisors to the business community. Analytic integration should start with clear business use cases and build on successes. Important to recognize the key role of executive champions in the integration of analytics organizations. Building a corporate test-and-learn culture ensures long term analytics success. Business value focus ensures that scarce analytic talent is directed to solve the most critical business problems. Parallel investments in infrastructure and data governance are needed; particularly as organizations tackle real-time analytics. 8