Building Analytics Teams: Harnessing analytics and artificial intelligence for business improvement

Description

Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key Features Learn to create an operationally effective advanced analytics team in a corporate environment Select and undertake projects that have a high probability of success and deliver the improved top and bottom-line results Understand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your team Book Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learn Avoid organizational and technological pitfalls of moving from a defined project to a production environment Enable team members to focus on higher-value work and tasks Build Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organization Outsource certain projects to competent and capable third parties Support the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analytics Analyze the operational area, the processes, the data, and the organizational resistance Who this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful. Cover Copyright Packt Page In Praise of Foreword Contributors Prologue Table of contents Preface Introduction Becoming data and analytically driven An analytical mindset Building an analytics team and an environment for collaboration Collaborators in the analytics journey Selecting successful projects Organizational dynamics Competitive advantage or simply staying competitive The core collaboration/innovation cycle Focusing on self-renewing processes, not projects – an example Summary Chapter 1: An Overview of Successful and High-Performing Analytics Teams Introduction AI in the education system We are different The original sin The right home Ethics Summary Chapter 1 footnotes Chapter 2: Building an Analytics Team Organizational context and consideration Internships and co-op programs Diversity and inclusion Neurodiversity Disciplinary action Labor market dynamics A fit to be found Evolved leadership is a requirement for success Continual learning and data literacy at the organizational level Defining a high-performing analytical team The general data science process Team architecture/structure options The implications of proprietary versus open source tools Summary Chapter 2 footnotes Chapter 3: Managing and Growing an Analytics Team Managerial focus and balance Sponsor and stakeholder management An open or fixed mindset? Productivity premium The rhythm of work Personal project portfolio Managing team dynamics The front end of the talent pipeline It takes a team Simply the best Organizational maxims Summary Chapter 3 footnotes Chapter 4: Leadership for Analytics Teams Artificial intelligence and leadership Traits of successful analytics leaders Building a supportive and engaged team Managing team cohesion Being the smartest person in the room Good (and bad) ideas can come from anywhere Emerging leadership roles – Chief Data Officer and Chief Analytics Officer Hiring the Chief Data Officer or Chief Analytics Officer – where to start? Summary Chapter 4 footnotes Chapter 5: Managing Executive Expectations You are not the only game in town Know what to say Know how to say it Shaping and directing the narrative Know before you go How many of us are out there? There is a proven path to success What are you hoping to accomplish? Outsourcing Elephants and squirrels Daily operations Summary Chapter 5 footnotes Chapter 6: Ensuring Engagement with Business Professionals Overcoming roadblocks to analytics adoption Organizational culture Data or algorithms – the knee of the curve or the inflection point A managerial mindset The skills gap Linear and non-linear thinking Do you really need a budget? Not big data but lots of small data Introductory projects Value realization Summary Chapter 6 footnotes Chapter 7: Selecting Winning Projects Analytics self determination Communicating the value of analytics Relative value of analytics The value of analytics, made easy Enabling understanding Enterprise-class project selection process Understanding and communicating the value of projects Delegation of decision making Technical or organizational factors Guidance to end users Where is the value in a project? Operational considerations Selling a project – vision, value, or both? Don't make all the decisions Do the subject matter experts know what "good" looks like? The project mix – small and large Opportunity and responsibility Summary Chapter 8: Operationalizing Analytics – How to Move from Projects to Production The change management process Getting to know the business Change management Analytics and discovery Analytical and production cycles and systems – initial projects Summary Chapter 9: Managing the New Analytical Ecosystem Stakeholder engagement – your primary purpose Bias – accounting for it and minimizing it Ethics Summary Chapter 10: The Future of Analytics – What Will We See Next? Data AI today Quantum computing and AI Artificial General Intelligence Today, we are failing Teaching children to love numbers, patterns, and math Blending rote memorization with critical thinking as a teaching paradigm Summary Chapter 10 footnotes Other Books You May Enjoy Index