2.  Deep Analytical Talent  Data Savvy Professionals  Technology and Data Enablers

3.  People with advanced training in quantitative disciplines, such as mathematics, statistics and machine learning

4.  People with basic knowledge of statistics and/or machine learning,who can define key questions that can be answered using advanced analytics

5.  People providing technical expertise to support analytical projects. Skills sets including computer programming and database administration

6.  Technically savvy, with strong analytical skills  Combination of skills to handle raw data, unstructured data and complex analytical techniques at massive scales  Needs access to magnetic, analytic sandbox  Examples: ▪ Data Scientists, Statisticians, Economists, Mathematicians

7.  Examples:  Financial Analysts  Market Research Analysts  Life Scientists  Operations Managers  Business and Functional Managers

8.  Examples:  Computer Programmers  Database Administrators  Computer System Analysts

9.  Reframe business challenges as analytics challenges  Design, implement and deploy statistical models and data mining techniques on big data  Create insights that lead to actionable recommendations

10. Data Scientist Quantitative skills Technical Aptitude Skeptical Curious and Creative Communicative and Collaborative

11.  Quantitative skills, such as mathematics or statistics  Technical aptitude, such as software engineering, machine learning, and programming skills  Skeptical  Data Scientists can examine their work critically rather than in a one-sided way

12.  Curious and Creative  Data Scientists must be passionate about data and finding creative ways to solve problems and portray information  Communicative and Collaborative  Articulate the business value in a clear way, and work collaboratively with project sponsors and key stakeholders