Minding the Machines. Jeremy Adamson. Читать онлайн. Newlib. NEWLIB.NET

Автор: Jeremy Adamson
Издательство: John Wiley & Sons Limited
Серия:
Жанр произведения: Базы данных
Год издания: 0
isbn: 9781119785330
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a significant need for leaders who can bridge the gap between the business and the data science and analytics functions. Minding the Machines fills this need, helping data science professionals to successfully leverage this powerful practice to unlock value in their organizations.

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      I would love to connect and hear what you thought of this book or to discuss opportunities to collaborate. You can reach me via:

      Website: www.rjeremyadamson.com

      Email: [email protected]

      LinkedIn: https://linkedin.com/in/rjeremyadamson/

      Twitter: @r2b7e

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      How is analytics unique in a corporate context? What have other organizations done right? What have they done wrong? What are the expectations on a new analytics leader?

      Building, integrating, and leading effective analytics teams is a business imperative. The organizations that are most successful overall are those that effectively leverage their analytics capabilities to build a sustainable competitive advantage. However, many organizations are simply not getting the return that they expected on their investments in analytics.

      Does hiring an engineer cause the surrounding buildings to be more robust? Could hiring five engineers make those buildings even more robust? Would hiring a pharmacist make you healthier? Would hiring an actuary increase your longevity?

      This new corporate function has been integrated in several support and core functions and has quickly become indispensable. Analytics is expected to add $16 trillion US to the global economy by 2030 and companies are eager to realize some of that value (PwC, 2017). As a result there has been a surge in demand for practitioners. There are approximately 3.3 million people employed in analytics in North America, and this is projected to grow by 15 percent a year in the United States over the next decade according to the US Bureau of Labor Statistics (2020). Educational institutions are eager to meet this demand.

      Essentially every major college and university in the world offers some sort of analytics program or specialization, within multiple faculties such as mathematics, engineering, or business. There are several hundred books published in this space, and it enjoys a highly active online community. These resources are strong, edifying, and comprehensive and cover every new technology, framework, algorithm, and approach. With such an active community, new algorithms and methodologies are packaged and made publicly accessible for tools such as R, Python, and Julia, almost immediately after being developed. The best and brightest are choosing to enter the field, often called the “Sexiest Job of the 21st Century” (Davenport & Patil, 2012).

      So, with overwhelming demand and a staggeringly capable pool of talent, why are there so many failures? Why are most organizations struggling to unlock the value in data science and advanced analytics? With so much executive support, so much talent, so much academic focus, why are so few organizations successfully deploying and leveraging analytics? In the 1980s, economist Robert Solow remarked that “you can see the computer age everywhere except in the productivity statistics.” Why now can we see data science transforming organizations without a commensurate improvement in productivity?

      Effectively all organizations realize the benefits of analytics. In a survey by Deloitte in 2020, 43 percent believed their organization would be transformed by analytics within the next 1 to 3 years, and 23 percent within the next year (Ammanath, Jarvis, & Hupfer, 2020). Though most organizations are on board with analytics being a key strategic advantage, they are unaware of how exactly to extract value from the new function.

      Short-tenured data scientists, employed in a frothy and competitive market, share stories of unfocused and baffled companies where they have been engaged in operational reporting, confirming executive assumptions, and adding visualizations to legacy reports. Uncertain what to do with the team, and in a final act of surrender, the companies no longer expect the function to “do data science” and transform the team into a disbanded group of de facto technical resources automating onerous spreadsheets in a quasi-IT role.

      Contrasted with those organizations who have truly got it right, the differences are stunning. For several companies, well-supported analytical Centers of Excellence are a key team, perpetually hiring and growing, and are solicited for their input and perspectives on all major projects. In others, internal Communities of Practice encourage cross-pollination of ideas and development opportunities for junior data scientists. New products are formed and informed after a thorough analysis by data scientists, who are also supporting human resources with success indicators and spending Fridays pursuing their transformative passion projects. Theories and hypotheses are quickly tested in a cross-functional analytical sandbox. Individuals are sharing their work at conferences and symposia, building eminence, and gaining acclaim for the organization.

      The title of this book, Minding the Machines, is meant to be an affectionate recursion. Those talented and creative practitioners, craftspeople, data scientists, and machine learning engineers, who create the algorithms that are transforming the way business is done, mind and care for those machines like a shepherd. Those machines need to be trained, informed, given established processes, encouraged to be broadly interoperable,