As Chair of the Strategic Analytics Program at Brandeis’ Graduate Professional School, I spend a lot of time thinking about our curriculum. Is it relevant? Is it serving the needs of our students in the highly competitive and rapidly evolving fields of business data analytics and data science? What’s the right mix of case studies, programming, project management, and mathematical skills to help our students succeed? Which sets of software tools and platforms should we adopt? What are the overarching learning outcomes we strive to achieve?
All of these topics also come up regularly in conversations with many different stakeholders: faculty, school administration, curriculum designers, and of course, our students and prospective students. In many of these conversations - especially the ones with students - I’m invariably asked some form of the question “what skills are most important for a successful data analytics career”? Not surprisingly, in my professional life - where I lead analytics teams and am a practicing data scientist - I’m frequently asked the same question, especially by job applicants and professionals just starting their data careers.
Usually the conversations steer towards ranking the technical skills that data pros are known for - writing dazzling computer code in any or all popular languages, producing deep statistical analysis, creating compelling visualizations and dashboards, adroitly wrangling even the messiest data, and building cutting-edge machine learning models.
To be sure, all of these competencies are important. Most successful data professionals are highly skilled in at least one of these areas. And if you’ve a savant in one of these specialties, it’s rocket fuel for your career.
So which one matters the most? What’s the secret data sauce? The short answer is: none of the above.
Before I explain in greater detail, let’s take a detour.
What’s the difference between a good cook and a great chef? Both have a passion for cooking, both understand enough of the science and chemistry behind cooking to avoid kitchen disasters, and both have solid technical kitchen skills. A good cook opens a refrigerator, sees ingredients, follows a recipe, and can competently assemble those ingredients into a pleasing dish. A great chef will open that same refrigerator, see those same ingredients, and understand the sublime culinary possibilities in even the simplest set of ingredients. A great chef understands flavors and how ingredients connect with one another to bring their vision of an incredible dish to life.
So what does this have to do with data analytics? A good data analyst is competent with key technical tools, can query, transform, and explore data, identify an appropriate statistical or machine learning model, and –with a bit of care - assemble all of these “raw ingredients” into an analytical solution that will probably meet their stakeholders’ expectations.
But a great data analyst/data scientist - like a great chef - sees a business problem and can harness their experience to develop a deep intuition around how to recognize, formulate, and execute on analytical solutions. They routinely connect the dots between the fundamental characteristics, nuances, behaviors, and economics of their domain. They understand how to create effective analytical strategies for solving these problems using the models and methods of modern data analytics.
The technical tools and software skills are a means to an end, not the end itself. The best analytics professionals are the ones that see this bigger picture and can repeatedly demonstrate a deep understanding of how to identify and cultivate business value using the ever-improving portfolio of data analytics tools.
As your career progresses, this “softer” skill will become increasingly important. You will probably find yourself transitioning from the purely technical mindset that most of us - including me - start our careers with to a more creative or strategic mindset. This is true, even in a field like analytics, which is deeply tethered to mathematics and computer science.
The hardest and most rewarding business challenges for data professionals rely on your ability to intuitively recognize valuable business problems that can be addressed by analytical and data-driven solutions. The “what” is almost always more important than the “how”.
Written by: Mark Coleman, MA, Program Chair of Strategic Analytics