Jim concludes his Analytics Mindset Series elucidating on one of the hottest career disciplines – Data Scientists – Hero or Hype? Catch up on his previous blogs of the series here:
Of all the enabling technologies built on the internet, few have been as historically important as the browser and the search technology layered on top. Since we first laid hands on Netscape in the early 1990s, humankind has been captivated by the unlimited universe that the internet has revealed. “Googling” has indeed become a verb spoken by the common person in daily life, simplifying what is a complex capability that requires unfathomable networks of computers and code into a single word. Browsing and searching are native to our relationship with technology, just as running water is to the feeling of home – in their absence, civilization itself exists in a diminished state.
This first era of the internet has been referred to as the Internet of Information (IoI), having enabled all variety of services that have a primary mission around the sharing of ideas and associated commerce. Within this paradigm, the computer programmer is the oracle behind the curtain making the magic happen. Now, this information age is not done yet – many believe it is near full maturity – but a new dawn is upon us. This new era has creeped up over the past decade and is now evident in all of the smart devices, robotics, and connected networks that power everything from Wall to Main Street. One might call this the Internet of Enablement (IoE) as it is transforming capabilities and human capacity in all aspects of daily life.
In this IoE, the data scientist is clearly a workhorse – and what code is to the programmer, data is to the data scientist. Most assuredly, given the Artificial Intelligence (AI) infused technology that exists in our homes, offices, markets, streets – everywhere – we have unprecedented amounts of data to contend with today. Data scientists are not flustered by this as they know the real truth – without gobs of data over time, AI and its offspring Machine Learning (ML) are not practical.
Demand for data scientists has never been greater and the educational system has responded accordingly. Dozens of universities now offer Bachelors and Masters programs that churn out these algorithmically minded citizens, most of which are scooped up by hiring enterprises as soon as their cap and gown are removed. With six-figure starting salaries and mid-careers approaching $300k, there seems to be unbounded love for these darlings of the IoE.
Just hire one (or three), point them to the data warehouse, and let ‘er rip – yes?
Caveat Emptor. As we learned in the 1849 California Gold Rush, a pan and pick-axe does not a successful 49er make. Given the demand for data scientists, there seems to be an unnatural spike in that job title in the marketplace. True, data scientists have a strong foundation in mathematics – but a B.S./M.S. in Statistics is table stakes on this journey. The successful data scientist will have an algorithmic mindset evidenced by their ability to deploy mathematical frameworks to business-driven models. They must go “beyond the math” and be adept at using modern programming languages (R, Python, SAS) and visualization tools to deliver business consumable outcomes. They are data-whisperers, able to see patterns in the data to “find the story” and make order out of seeming chaos. Finally, the best data scientists can tell that story, getting beyond the jargon and connecting directly with business sponsors to profound impact.
Domo Arigato, Mr. Roboto? On the analytics maturity scale, the ability to prescribe actions based on model outputs is a goal that you would want to achieve in your analytics program. Data scientists have a role in achieving this goal as they have the skills to interpret model outputs within the context of a business objective. In consultation with business experts, they can deliver action-oriented results that drive transformation. Today’s ML systems are quite sophisticated, some demonstrating that model output assessment and prescription of next steps are possible without human interaction. Certainly, we see this today in factories where sensors capture real-time data and take action to avoid unwanted outcomes. Or, in algorithm-driven trading systems that take input from relevant market ecosystems that decide what to buy or sell, when, and in what quantity. However, the ability to interpret across broad use cases and within nuanced circumstances (think healthcare) is still beyond most automated capabilities, thus human skill is still front and center in most implementations.
Beyond IT. The habitat for data scientists should not be restricted to technology teams, per se. We should not mistake them for developers or architects, which are generally considered native to Information Technology departments. Rather, a broader perspective of the citizen data scientist should be adopted, whose habitat is within operational and corporate groups, charged with leveraging data and algorithmic approaches to solve business objectives. These citizen data scientists require most of the skills that make their IT counterparts effective but have the advantage of working within the business units themselves. Despite (potentially) lacking some educational aspects of the discipline, citizen data scientists are nonetheless an important way to accelerate your analytics maturity as an organization.
So, to answer the initial question – hero most certainly, with proper expectations and support. If you find yourself trying to sell the notion of a data scientist with something akin to “greatest thing since …”, reel yourself back in or risk crossing over to hype territory.
This blog was originally published on LinkedIn