How we got here
The Global Financial Crisis of 2008 created both problems and opportunities. Many financial firms needed to rethink how to treat their customers in the post-crisis world, one in which these customers suddenly needed more money but were less able to pay it back. This meant answering, at a customer-by-customer level, whether to lend them more money, how to collect on the money owed, how to sell them investments and other services as times got better, and even how to reach them on the phone.
As the new head of customer data and analytics at one such firm, I Inherited a team of six analysts but did not find in any of my office drawers a guidebook on how to launch a business analytics function in the aftermath of a financial crisis. From a decade of consulting, I knew something about how business analysts should ask and answer questions using data, how to share the results, and even a bit about how to do the technical side of the job, as I had been a coder many years earlier. But I was missing practical advice on how to create, grow, and lead an operation that would use data and analytics to help a large enterprise succeed in a novel, real-world business setting.
This is the textbook I wish I had found in a drawer when I started that job.
There are other books that can fill in the blanks on specific technical questions: how to set up a data warehouse, build a regression, calculate customer lifetime value, and use colors when visualizing data. This book is a complement to those resources, taking the perspective of a senior leader who must run such an operation day to day or oversee it at a strategic level. It also complements the way that I teach this subject, which is to include training on problem-solving, business intelligence tools (such as Tableau), and using novel sources of data, whether free or commercially available as “alternative data.”
Counting the financial-services stint and many other jobs and projects, I have spent nearly a quarter century helping companies and investors think about how to use data and analytics to improve performance. Because most of my work has been in the for-profit sector, “improving performance” has generally meant helping an enterprise make more money while reducing the risk of not making any in the future.
In this textbook, when I say “business analytics” or “data science,” I mean the discipline of using large and sometimes unstructured data sets to answer descriptive questions, make predictions, and recommend actions to help businesses succeed. To me, “business analytics” has connotations of thinking in terms of customer lifetime value instead of focusing on just one metric at a time, de-averaging customers instead of looking at them as an undifferentiated mass, doing careful observational studies or well-controlled experiments, visualizing the results of analyses, and using artificial intelligence and machine learning where applicable (as we will see, they are not always applicable).
Although it is focused on real-world impact, including financial impact, the term “business analytics” also implies working on something other than purely financial calculations (such as calculating a business unit’s variance to budget). The business analytics team is usually tackling a problem that is more granular than or upstream to the eventual financial results as reported.
How to use this book
This book comprises four chapters. Each of the subchapters, or sections, has a date stamp and illustrations and generally looks more like a blog post than a textbook section. That is because this book has indeed been curated from a blog—one that you can subscribe to, if you like.
For the first two years of that blog’s existence, my aim was deliberately to create the material for Chapters 1 and 2 of this book, setting out the overarching rules for leading analytics and a masterclass on how to forecast more accurately. I then posted on a wider range of analytics topics, many of which touched upon questions of talent (which I have curated and placed in Chapter 3) and competitive strategy as it relates to data and analytics (Chapter 4). Because the book is composed of blog posts, any section can be read on its own.
This book is for people starting out in a business analytics job, people directly leading or distantly overseeing the analytics function, and people studying the subject in school. If you are in the first category (data scientist or business analyst), you might read the whole thing in sequence. If you are in the second category (leader), you might just read the table of contents and then go to a specific section only when a relevant issue pops up at work. If you are in the third category (student), that’s easy: just read the assigned chapters and answer the assigned questions.
For each section, there are both comprehension-testing questions, which have objectively correct answers, and open-ended questions to get the reader to think critically about the ideas presented and explore how the ideas apply to their world. The exploratory questions are geared toward readers who have work experience.
I hope you enjoy the book and learn something useful from it. If you have any questions or comments, especially if you disagree with my point of view and want to debate it, please get in touch. For my current students, this means using your school’s learning management system. Anyone else should feel free to reach out to me through this website’s contact page. I hope to hear from you.
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