Minding the Machines. Jeremy AdamsonЧитать онлайн книгу.

Foreword

      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.

Many of the world's leading organizations can attribute their success to the fact that the practice of data science is increasingly becoming a strategic function. Analytics and data science enable consumer experiences that have become indispensable in our daily lives and deliver highly personalized recommendations and content, and this is now the expectation for almost everything else in our lives. The expectation of the customer has become immediate, personalized services that predict what it is they may want before they may even know it themselves. Data is what powers these great product experiences. Data science is no longer simply a technology function buried within IT or reserved purely for the tech giants in Silicon Valley. Data science and analytics will become increasingly indispensable in health care as it will improve diagnostic accuracy and efficiency. In finance, it will aid in the detection of anomalies and fraud. In manufacturing, it will aid in fault prediction and preventative maintenance. Whether you work in corporate strategy, research & insights, product development, human resources, marketing, technology, or finance, you will no longer be able to effectively compete without leveraging the talent and capabilities of the data science teams.

      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?

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