My role: Business Intelligence vs Business Analytics. Um, may be, Machine Learning specialist?!
The classic clash of titles in Data Science. Is it worth it?
Disclaimer: This is not a post about explaining what each of the fancy titles in Data Science job industry mean. I think Google search engine does a pretty good job at it. This is a personal experience that I encounter in my everyday life while working at the intersection of analytics and engineering.
I earned Engineering degree in computer science and then pursued MBA in decision science. Throughout my career, I’ve worked in technology companies and most of my roles have been in business units like Sales, Corporate Strategy, Product and Market research. While my primary responsibilities were always directly related to the business function that I was a part of, there were times when I had opportunities to develop and lead data-driven processes and mechanisms to be used by internal stakeholders and/or external customers. With my academic background, passion and a lot of interest in mathematics, statistics, and coding, I often ended up working on some of the cool projects that can be mostly categorized under the wide umbrella of “Data Science”. I never cared too much in terms of what “data functions” I was performing. Rather, I focused more on solving the problems at hand and my engineering and data skills came handy to address those.
Until a decade ago, there weren’t many official, fancy terms (like Data engineer, BI engineer, Machine Learning specialist, etc.) associated with the jobs that had to do anything with data. But more recently, I often fall into the trap of titles when my colleague ask me what exactly I do with “data”, what exactly is my title and how am I different than my geeky counterparts in Data Science teams.
Colleague X: “Are you a BI Engineer? Or Data Analyst? How do you define your role?”
Me (ignorant of official industry terminology and plethora of confusing job titles): “Umm, possibly, Business Analytics, because I use analytical skills to address real-world business problems like market share, trends, market segmentation, supply-demand modeling, sales forecast, scenario-planning, etc.”
Colleague X: “Oh, so you are kind of Machine Learning specialist.”
Well, to be honest, I don’t know. I care more about solving my team’s problems, making my hiring manager look good and, in the process, make some money. “Just get it done”, is the motto.
From my experience in the tech industry,
Business Intelligence teams help identify big trends, and patterns using historical data from data-warehouse without digging too much into the why’s or predicting the future.
Business Analytics function use BI reports as inputs to extract information in a more sophisticated way in order to solve why’s, address operational applications, and increase productivity of business teams to address customer needs.
Further, a Machine Learning specialist performs data transformations on structured and un-structured data, uses statistics and coding to implement algorithms that use existing data to “learn” in order to “predict” future outcomes.
Overall, I think if you work in tech industry and due to the burgeoning demands of data-driven processes in most business units, if you have interest in coding, understand data readiness, transformations and keen to learn & apply algorithms, you’ll have a high chance working at the intersection of the above mentioned three common areas of data science.
Forbes published an article Why There Will Be No Data Science Job Titles By 2029, expanding that the reason companies hire people into data science job titles is because they recognize there are emerging trends (cloud computing, big data, AI, machine learning), and there are business problems to solve. Data Science as a degree is here to stay, but the job title won’t. In fact, the key skills that are practically important and will keep up with the winds of change will be:
- Communication skills
- Applied domain expertise
- Creating revenue and business value
Don’t tie your self-worth to the title. Rather, think about solving problems at hand, and more importantly, the impact it can make for businesses at scale. Learning new skills will also be a good outcome to help you prepare for the career ahead. It is all about staying relevant in current times.
I am on my journey where I often end up in career roles that fits the criteria that help answer: Data Science + _______ = A Passionate Career (source). The applications of data science are endless. I’m always on the lookout for the opportunities and problems that excite me. That way I’ll be more willing to make a project successful.
Here is my favorite piece of suggestion: Spend time learning new technical (hard skills) and non-technical skills (soft skills), try connecting dots, research and learn about the industry domain you are most interested in, talk to domain experts (and find a mentor if you can!), learn about what your business/team needs the most, and make an impact. Money and title will follow.
Be curious, self-driven, and passionate about finding answers.
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