In the last installment, I embarked on a project--perhaps only a task--to assemble a membership list for an organization. It sounded simple: how hard could it be to merge two lists of people? Of course, I couldn’t just stitch one list on top of the other as there are members who subscribed to the newsletter as well as joined the Facebook group. These duplicate rows must be merged so that…
An organization wanted to understand its base of members so the first order of business was constructing a database of all people who can be considered members. We decided to define membership broadly. Members included those who join the Facebook group, and those who subscribed to the newsletter. The organization kept two separate lists which I would merge to create a master list. For simplicity, I’ll call them the FB…
Here was how I spent last weekend: At college reunions in beautiful Princeton on a glorious sunny day. I also spoke about data science at a Faculty-Alumni panel titled "Science Under Attack!". Here is what I said: In the past five to 10 years, there has been an explosion of interest in using data in business decision-making. What happens when business executives learn that the data do not support their…
I mentioned the Harvard Business Review article on business use of customer data in the "Big Data" era. In the previous post, I looked at the nature of the evidence used by the authors. In this post, ignoring my discomfort with some of the evidence, I examine the conclusions of the article. *** The report has a three-part structure: the first section describes the issues; the second section communicates results…
Harvard Business Review devotes a long article to customer data privacy in the May issue (link). The article raises important issues, such as the low degree of knowledge about what data are being collected and traded, the value people place on their data privacy, and so on. In a separate post, I will discuss why I don't think the recommendations issued by the authors will resolve the issues they raised.…
In my latest piece for Harvard Business Review (link), I tackle this common problem in the interactions between data scientists and business managers: A typical big data analysis goes like this: First, a data scientist finds some obscure data accumulating in a server. Next, he or she spends days or weeks slicing and dicing the numbers, eventually stumbling upon some unusual insights. Then, a meeting is organized to present the…
