Minding the Machines. Jeremy Adamson

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СКАЧАТЬ Institute of Technology, are creating entirely new institutions within their campuses to come to terms with the ubiquity of data and the rise of artificial intelligence.

      However, it isn't simply technical, mathematical, or scientific horsepower that is required by organizations in the data science world. In most organizations the premise is still that data science teams are overindexed in the technical practice versus being embedded in the business to drive business performance. The most successful data science teams are those that have a focus on contributing to the strategy of hiring and retaining people who are focused on value creation and finding ways to democratize access to data and decision making. Because it is one of the newest functions in most organizations, there is little body of work to refer to on how to design and build the right data science organization. We are all learning in real time, across all industries and geographies. How do you hire? How do you structure the teams? What problems do you solve? How do you set up the culture of experimentation? How do you think about democratizing access? How do you evolve beyond reporting and move into prediction models and algorithms?

I have had the pleasure of being a senior leader to technology teams at organizations with widely varying analytical maturity, in industries ranging from Silicon Valley giants to aviation and from sports to media and digital. The key differentiator for those teams finding success using data science is not whether they have a data lake or are deploying neural nets and reinforcement learning. The winning teams are those that integrate into the organization, understand the business, build strong relationships, collaborate, align their objectives to the business, and see data science as a toolkit for solving business problems rather than an esoteric and technical field of study. They are integrated into the business and serve a strategic function with support at the highest levels of the organization, all the way to the president or CEO. The three pillars described in this book, people, process, and strategy, are every bit as important as the data and technology. The challenge, despite all the focus on the technical skills, is still a very human one. There is no doubt machines are helping drive more automation and the increasing power of data and algorithms to help make decisions. I would argue that the importance of the human element, the people responsible for building the models, doing the analysis and creating the algorithms, will becoming increasingly important. The need for leadership, empathy, and an understanding of organizational behavior is becoming increasingly important. It is essential for these teams to have the ability to deal across the enterprise with privacy and data governance, ethics, and bias, and to ensure that the capital and operational investments are solving the problems that really matter. As the field of data science advances, the human element and the team you build becomes even more important. The more important the machines become, and the data that powers them, the more the people element will be critical. Strategy, process, culture, and the human side of data science will be the next evolution of the practice to deliver on the promise of big data and business results.

      Most data science leaders, focusing mainly on the technical aspects of their craft, have struggled to find successes in organizations and to unlock real business value. Minding the Machines helps to fill that gap and redirect these professionals to the things that matter. Blending the science of data and the leadership of people, process, and strategy is what Jeremy manages to do brilliantly in this book.

      —Alfredo C. Tan

Introduction

      Minding the Machines provides insights into how to structure and lead a successful analytics practice. Establishing this practice requires a significant up-front investment in understanding and contextualizing the initiative in contrast to better-understood functions such as IT or HR. Many organizations have attempted to use operating models and templates from these other functions, showing a fundamental misunderstanding of where analytics fits within an organization and leading to visible failures. These failures have set back the analytical maturity of many organizations. Business leaders need to hire or develop data-centric talent who can step back from analysis and project management to view their work through a lens of value creation.

      Readers will understand how organizations and practitioners need to structure, build, and lead a successful analytics team—to bridge the gap between business leaders and the analytical function. The analytics job market is booming, and the talent pool has swelled with other professionals upskilling and rebranding themselves as data scientists. While this influx of highly technical specialists with limited leadership experience has had negative consequences for the practice, it also provides an opportunity for personal differentiation.

      Minding the Machines is organized in three key pillars: strategy, process, and people.

       Strategy—How to assess organizational readiness, identify gaps, establish an attainable roadmap, and properly articulate a value proposition and case for change.

       Process—How to select and manage projects across their life cycle, including design thinking, risk assessment, governance, and operationalization.

       People—How to structure and engage a team, establish productive and parsimonious conventions, and lead a distinct practice with unique requirements.

      Minding the Machines is intended for analytics practitioners seeking career progression, business leaders who wish to understand how to manage this unique practice, and students who want to differentiate themselves against their technical peers.

      There is 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.

      How to Contact the Publisher

      If you believe you've found a mistake in this book, please bring it to our attention. At John Wiley & Sons, we understand how important it is to provide our customers with accurate content, but even with our best efforts an error may occur.

      In order to submit your possible errata, please email it to our Customer Service Team at [email protected] with the subject line “Possible Book Errata Submission.”

      How to Contact the Author

      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

      Instagram: r2b7e

CHAPTER 1 Prologue

      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 СКАЧАТЬ