g-f(2)4 Companies need to identify the type of talent they need in order to become data-driven

Extra-condensed knowledge

Whether people are called data scientists, quantitative analysts, or something else, these titles are still relatively new to companies. It’s not surprising that there is little consensus on their meaning. Over time, new titles will emerge that better describe the particular activities involved in a job; data engineer, for example, has already arisen to describe people who spend most of their time wrangling data. Until more consensus is reached across our society, however, it’s important for companies to emulate TD Bank and clearly identify the types of talent required to become data-driven and then acquire it, nurture it, and unleash it.

Genioux knowledge fact condensed as an image.

Condensed knowledge 

• While many companies are hiring data scientists and other types of analytical and artificial intelligence talent, there is little consensus within and across companies about the qualifications for such roles. 
• The term data scientist might mean a job with a heavy emphasis on statistics, open-source coding, or working with executives to solve business problems with data and analysis. The idea of data scientist “unicorns” who possess all these skills at high levels was never very realistic.
• Colleges and universities have responded to the demand as well by offering hundreds of new programs on data science and analytics. But the skills taught in such programs vary widely, and some universities offer multiple programs with different emphases. For both newly hired and experienced employees, titles such as data scientist and quantitative analyst are not likely to be good guides to their actual capabilities.
• Developing new standards typically takes many years.
• Understand the Data Roles Needed. Analytics and AI talent is a scarce and valuable resource for every company, but particularly for those with a desire to be data-driven. That’s the situation at TD Bank Group, a large North American bank (the largest by assets in Canada). 
• TD executives made a series of internal investments directed at the critical importance of assessing, hiring, and motivating its data and analytics talent. Peter Husar, vice president of enterprise analytics strategy and planning, told me that he and his colleagues saw an opportunity to expand the role of analytics by first bringing consistency to the job definitions and positions related to data and analytics that existed across the bank.
• Husar and his team focused on understanding who was in their data and analytics community in order to provide a clear picture of their talent landscape.
• Acquire and Retain New Talent. In addition to more effectively managing existing data and analytics talent, organizations need to make a concerted effort to bring on and retain highly skilled employees.
• Build a Data and Analytics Community. TD data and analytics leaders are conscious of the need to build community and knowledge-sharing approaches among the data and analytics professionals across the bank. That includes providing opportunities for data- and analytics-oriented employees to network and hear about trends in the industry. Five years ago, the bank had its first annual Big Data & Analytics Summit, with 40 employees attending. In 2019, the bank had its fifth annual summit, with over 2,000 employees in attendance — and the numbers continue to grow.
• TD’s initial job-mapping exercise led to a clearer vision for how individuals can move around, upskill, and grow their career paths. The team spearheading the plan created educational materials to help data and analytics employees develop their skills and made those materials available on an enterprisewide online platform for self-learning and development.

Category 2: The Big Picture of the Digital Age

[genioux fact extracted from MIT SMR]

Authors of the genioux fact

Fernando Machuca

References

How Large Companies Can Grow Their Data and Analytics Talent, Thomas H. Davenport, November 18, 2020, MIT SMR, MIT Sloan Management Review. 

ABOUT THE AUTHORS

Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, as well as a fellow at the MIT Initiative on the Digital Economy and senior adviser to Deloitte’s Analytics and AI practice.

Extracted from Wikipedia

Thomas Hayes "Tom" Davenport, Jr. (born October 17, 1954) is an American academic and author specializing in analytics, business process innovation, knowledge management, and artificial intelligence. He is currently the President’s Distinguished Professor in Information Technology and Management at Babson College, a Fellow of the MIT Initiative on the Digital Economy, Co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.

Davenport has written, coauthored, or edited twenty books, including the first books on analytical competition, business process reengineering and achieving value from enterprise systems, and the best seller, Working Knowledge (with Larry Prusak) (Davenport & Prusak 2000), on knowledge management. He has written more than one hundred articles for such publications as Harvard Business Review, MIT Sloan Management Review, California Management Review, the Financial Times, and many other publications. Davenport has also been a columnist for The Wall Street Journal, CIO, InformationWeek, and Forbes magazines.