Yesterday I stopped on my way home for gas. As per usual, I was on the phone, and struggled to get out of the car, pop the fuel cover, run my credit card, and pump the gas, all while trying to run a teleconference meeting – god I love multitasking! But I now understand the appel of the Full-Service gas station my mother used to go to when I was a little kid. The guys pumping the gas were “service station attendants”, but we had fun as kids calling them “Petroleum Transfer Engineers”.
We also called the person working the dressing room at the clothing store a “Customer Experience Enhancement Consultant” every time my mom asked “do these shoes go with this dress?” and the clerk would dutifully give her opinion. Then I started thinking, is a “Data Scientist” nothing more than a euphemism for “Analyst”?
While fashion is usually not a high priority for data professionals, it is true that the terms used to describe the work that they seem to have changed in the last few years. Data Science has caught the most attention these days. Many say they are seeing something groundbreaking, but the more jaded observers see a clever twist on an old standard. So what exactly is Data Science and can an analyst, who simply does analytics, accessorize their current outfit to turn heads? Is it any different than data mining?
The problem is that the term has a mystique where its features are hard to distinguish from the crowded taxonomy of different data terms. This revisionism is commonplace in in many fields, but particularly in the fast growing data science field. I know many colleagues who rebranded themselves as data scientists in order to be more attractive in the market. They would argue that an analyst make reports while a data scientist makes visualizations, even if both have the exact same content.
There are a slew of other terms that get lumped in these categories and cause confusion when talking about statistics, business intelligence or data science, but none more elusive than the most nebulous of terms – Analytics.
The graphic below is an effort to help bring clarity to the analytics domain and helps develop a simple glossary for us to frame the actual work being performed.
The overlaps between business intelligence and data science are significant. In fact, a small majority of tasks and vast majority of labor are spent in tasks that are shared between the two related disciplines.
Here are some additional comments on the Infographic:
- Business analytics is a spectrum of different types of analytics with increasing complexity and business value from descriptive to prescriptive.
- The overlaps in analytics phases reflects finer points that would make the graphic less readable.
- Descriptive and predictive analytics reflect that some machine learning, like K-means clustering, are unsupervised, so there is no measurements of accuracy that can apply to the model’s output.
- Using supervised learning, like neural network, can benefit from using simulation testing or simple optimization such as a profit chart. More advanced use cases fall squarely into data science, like using simulation as inputs for predictive models which can drive optimization.
- The statistics in the Data Mining sphere refers to basic quantitative analysis, like cumulative distribution and correlation.
- Decision Management is the combination of business rules and processes to augment the business value of the analytic output.
While this graphic may not be enough to update your title from “analyst” or “business intelligence developer” to “data scientist” (though you would not be the first), it certainly helps give boundaries to very elusive labels.
Well, with that, I’m off with a full tank of gas, but before I get home, I think I’ll stop at the local watering hole and order a beer from my favorite “Beverage Dissemination Officer” maybe order some appetizers from the “Field Sustenance Facilitator”. And boy, I sure hope the “Gastronomical Hygiene Technician” gets all the gunk off the plates this time.
