embedded disparate data science specialists throughout the organization and incurred enormous technical debt.
These issues are not insurmountable, however. Whether an organization is beginning its first foray into the analytics space or it is rebooting a failed team for the third time, the key is the creation of a carefully considered strategy, the establishment of realistic goals, and the full commitment of executive leadership. The advantages associated with analytics are too great to overlook, and the long-term cumulative impact of interrelated and interdependent models provides a powerful incentive for aggressive adoption.
This book was written for anybody who aspires to lead or be part of an effective analytics team, regardless of managerial experience. Every analytics leader, from a first-time team lead to a seasoned VP, has unique challenges to overcome.
For the Leader from the Business
Every new role is a challenge, regardless of ability, disposition, or motivation. This is particularly the case with a unique subculture of academic technocrats with whom it is difficult to establish credibility without enough time being “hands on keyboard.” Without the respect of your team, it is impossible to get the buy-in required to establish best practices and ensure that the output of the team is not simply self-satisfying experimentation but can bring real value to the organization. As a corollary, every practitioner has experienced a manager who is out of their depth and who has compensated for their lack of self-confidence with authoritarianism and distrust, shifting the focus of the team toward an end they are more confident with, such as reporting.
For all but the most analytically committed organizations, there is a point along the chain of command where a practitioner reports to a non-practitioner. This can be a challenging junction for both parties without clear expectations, transparent communication, and mutual respect. Catching up from a technical perspective isn't feasible or advisable, but by leveraging your business understanding and domain knowledge to become an intermediary, translating business needs into projects and analytical outputs into operationalizable processes, you can unlock the power of your new team and give them the opportunity to develop into more business-oriented individuals.
For the business leader, I hope that this book helps you to reframe and refine your current leadership abilities, and to use them in an analytics context in order to engage your team and find success together.
For the Career Transitioner
Those who transition to data and analytics mid-career have a key differentiator from those who have entered the field directly from university—breadth and context. The ability to leverage your multidisciplinary background from engineering, finance, sciences, and so on is valuable both to your career and the organization you join.
Though it would be almost impossible to compete with trained data scientists on a technical basis, it is the disciplinary and sector diversity of the team that drives innovation, and those who have worked in multiple industries and functional areas bring a unique perspective to the teams with whom they work. Rather than starting a new career as a new hire and individual contributor, with personal study and intentional self-reflection mid-career transitioners are often able to seamlessly make a lateral move. Having familiarity with the different AutoML and analytics-as-a-service offerings, combined with transferable managerial skills, can make for a powerful combination.
For the career transitioner, I hope that this book helps you to prepare for lateral movement into an analytics role and to use your transferable skills to add value in your new function.
For the Motivated Practitioner
It is an unfortunate truth (and perhaps an unfair generalization) that the skillset that makes a practitioner a competent data scientist is rarely the skillset that makes them a competent manager. Though there are certainly analytically minded people who have the natural inclinations toward leadership and bigger picture thinking, it is rare that in practice those people would have the technical depth to stand out as a candidate for management. Often, those with the natural capabilities required to enter management can appear to be less effective as individual contributors on a purely technical basis.
To make the leap to management is to leave an objective and predictable role with performance metrics such as p-values and ROC curves and exchange them for stakeholder management, workshop facilitation, and inherent subjectivity. Those able to successfully make this transition while maintaining the ability to downshift to provide analytical support establish themselves as leaders in the practice and are in high demand. Exceptional managers who have legitimate technical credentials are the unicorns of data and analytics.
For the practitioner, I hope this book helps you to understand what is required to move up the value chain and to prepare for leadership opportunities.
For the Student
When a student pursues an applied field such as business or engineering, the curriculum is generally developed in a way that seeks to balance between foundational academic elements and applied profession-specific education. The curriculum is updated and maintained such that it remains aligned with the changing needs of the field. For several professions such as accounting, law, and engineering, this takes place within a partnership between the administrative body of the professional practice and the educational institute, and through accreditation it's ensured that graduates of these programs are broadly educated and prepared to work in the field they have studied. This is unfortunately not the case with data and analytics.
Most North American universities have data science or analytics offerings, but having no natural home they are generally provided through multiple faculties such as business, mathematics, engineering, finance, or computer science. These programs provide instruction in highly simulated and well-defined problem solving, focusing on the improvement of a statistical metric. The data is often perfectly presented and accompanied by a well-articulated data dictionary, in great contrast to real life experience. Additionally, most curricula emphasize such topics as computer vision, natural language processing, and reinforcement learning, fairly esoteric topics that have little applied usage in industry. Finally, and most importantly, effectively none have mandatory coursework on the strategic and operational elements of an advanced analytics and AI team. Without this understanding, typical graduates have a thorough mathematic understanding, much in the way of raw horsepower, but require a significant investment in training before they understand how to leverage their education and apply it to a real-life scenario.
With so many new data and analytics graduates competing with mid-career transitioners and a global talent pool, they often seek ways to stand out as a potential hire. With the exception of highly specialized roles in technology companies, the key development opportunity for these new hires is the formation of leadership abilities in an analytical context. Reframing and focusing analytical concepts into a business context is an immediate and powerful way to differentiate yourself in a new role or in an interview, especially as the profession moves away from long-horizon highly technical solutions toward a focus on immediate value.
For the student, I hope that this book gives you the knowledge to stand out against your peers, to be seen as a strategic thinker, and to be able to add value to whatever organization you choose to work with.
For the Analytics Leader
Compared to other organizational functions, this exciting field has come about abruptly and without a blueprint for how to build or lead these new teams. Often playbooks from other functions have been used, with little success. The lessons that experienced analytics professionals have learned have been hard won. What further complicates the successful deployment
