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      Building and Leading Data Science and Analytics Teams

       Jeremy Adamson

      Data. There was a time when this word made reference to a Star Trek character or something professionals in the IT department who worked on databases would manage. Today data, data science, data engineering, data analysts, or any term including the use of data is pervasive across business, industries, and society. The use of the term data has practically become everyday vernacular in business; it seems to be the holy grail solution to everything. However, most organizations are still in the very early stages of their journey.

      The need for knowledge in Data Science & Analytics, Algorithms & Artificial Intelligence is becoming evident in the sheer volume of online courses, degrees, and certifications available on EDx, Coursera, Udacity, and other online education providers. Top-ranked universities across Canada have introduced graduate degrees in data science and analytics. Two of the most prestigious universities in the world, the University of California, Berkeley, and Massachusetts 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?

      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

      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

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