One of the most expensive cars in the world is the Ferrari LaFerrari with a price tag of $1.4M. Ferrari cars are hand-built and described by the manufacturer as a “unique piece of art”. Customers specify their options which include 16 exterior colors, 12 leather types, 8 carpet colors, and 3 trim options, making 4 million ways to build a Ferrari. Ferrari produces only 5,400 cars per year, ensuring owners have a unique vehicle.
By comparison, Toyota produced over 10 million vehicles a year. At this volume you can be certain that these are not hand-made and that a single technician does not produce an entire engine. And by the way, these vehicles cost a lot less.
Division of labor is a concept introduced to auto manufacturing by Henry Ford in the early 20th century. Ford broke work up into smaller pieces and standardized it. This simplified his training of employees and ensured they became extremely proficient at their particular tasks.
I had a fascinating discussion with a colleague from the Northeast chapter of the Project Management Institute a few months back at the PMI Region 4 leadership conference. This colleague works for one of the large insurance companies (I will refer to it as Acme) and was telling me about some of the work they do in their call centers. Acme employs some individuals with the title of “Data Scientist”. This term can mean a lot of things, so we dove into some of the details on what this role means for Acme.
Here is a summary that aligns with our conversation that I lifted from mastersindatascience.org:
Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges.
Acme is using their data scientists to analyze massive volumes of data from the call centers for the purposes of predicting a customer’s need. Transcribed recordings of calls are run through statistical models to identify certain patterns. Those patterns are plugged into an application built by the data scientists. The result is that when communicating with Acme via the chat function on their website, you may actually be having an exchange with a bot.
The data scientist, as an emerging profession, covers a wide range of topics. As this profession evolves, I would expect that specialties develop.
This feels to me much like the field of computer programming years ago. At the time there were computer programmers doing extraordinary things working directly with businesses. The programmers listened to the business needs and determined how to code the applications to meet those needs. Eventually the field of Business Analysis evolved. These were the people who had a good understanding of how the business functions and were able to more quickly identify the business needs and formulate something that was useful for the programmers. The business analysts brought to the table the ability to speak both the language of the business and the language of the programmer. Having that translated allowed the programmers to focus on developing outstanding applications.
Programming as a profession has been around for may years and the training, techniques, and job expectations have been pretty well formed. With the Business Analyst having been around for some years, but being relatively new, the profession is not nearly as defined. People come into this role with much different background employing much different techniques. This makes me think the data scientist role will split at some time in the future, as the programmer / business analyst developed.
So what are the implications of separating these professions? Efficiency and cost. Having someone who is really good at defining business needs, really good at analyzing data, and really good at programming is not very common. It is as though these high-caliber individuals are hand-building the $1.4M Ferrari. If you are able to perfect the profession of the Business Analyst, you can start to implement a system at the cost of the Toyota.