The valuable hire in data science is elusive, and it does not come with a surprise, as a “full-stack” data scientist has mastery of statistics, analytics and machine learning. When organisations can’t get their hands on a three-in-one combination, they set their sights to lure the most impressive prize among the single-origin specialists. So, among these the skills get the highest pedestal, read on further to find out.
The current trend in data science favours flashy sophistication algorithms with a dash of sci-fi, make AI and machine learning the toast of the job market. Alternative challengers for the coveted profile come from statistics, thanks to a century-long reputation for rigor and mathematical superiority. Where do the analysts fit in here?
Has Analytics Become a Second-Hand Citizen?
If a specialist says that his/her primary skill is analytics (or business intelligence or data-mining), chances are that their self-confidence may have taken a beating as statistics and machine learning have become prized possessions within companies, the job market, and the media.
The three professions under the data science umbrella are completely different from one another. They may use common equations and methods, but that is where the similarity ends. Instead of seeking an analyst’s assistance to develop on statistics or machine learning skills, consider encouraging them to reskill on their own discipline first. In data science, excellence in one area beats mediocrity in the two disciplines.
The Vigour in Statistics Excellence
Statisticians are specialists who are your best guard against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than a mental block. Statisticians deeply care about whether the methods applied are apt for the problem and they agonize over which inferences are valid from the information at hand.
Delivering Performance with Machine Learning
In the evolving era, the machine learning specialists know that they would not find the perfect solution written in textbooks, instead, they will have to put their expertise in a marathon of trial-and-error judgments. It may take them time to try each new conclusion with an acceptable accuracy more valuable than the intimate knowledge of how the algorithms work. Performance means scalable, easy-to-maintain and reliable models that perform well in the production stage.
Wide Versus Deep Working Styles
Statisticians and machine learning engineers have different working styles. They are narrow-and-deep workers who need to be pointed at problems that deserve the effort. If they end up carefully solving the wrong problems, an organisation’s investment in data science will suffer a beating. To ensure that does not happen, business needs to make good use of narrow-and-deep experts and has to be sure it has the right problem or needs a wide-and-shallow approach to finding one.
Excellence in Analytics Comes from Speed
The best analysts are lightning-fast coders who can scan vast datasets and investigate on the potential insights faster than those other specialists who rely on whiteboard. Speed is the highest virtue of an efficient analyst. A mastery of visual presentation of information adds a great value too as interactive dashboards allow the mind to extract information faster, which pays off in time-to-potential-insights. The result is that the business gets an upper edge to the previously-unknown unknowns. This generates an inspiration that helps decision-makers select valuable business problems to send statisticians and ML engineers on the quest.
The Art of Stellar Storytelling
Analysts summarize interesting facts and use data for inspiration which makes them master storytellers. In some organizations these facts become an input for human decision-makers, whereas, in more sophisticated data operations, data-driven inspiration gets flagged for proper statistical follow-up.
Good analysts have an unwavering respect for one golden rule in their profession, of not coming to conclusions beyond the data at their disposal. While statistical skills are required to test hypotheses, analysts are an organisation’s best bet to come up with these hypotheses in the first place.
Of the three breeds, analysts are the most likely heirs to the coveted decision throne. Analysts are jack of all trades with subject matter expertise which goes a long way towards helping organisations spot interesting patterns in their data faster.
Driving Analytics for Decision-Making
Analytics has evolved as the kingmaker driving decision-making more accurate by integrating the powers of statistics, machine learning and visualisation techniques.
An excellent analyst does not imitate or is a shoddy version of a machine learning engineer; whose coding style is optimized for speed on purpose. Analysts are not that good statisticians themselves since they do not deal at all with uncertainty, they deal with facts and figures. If an organisation overemphasizes on hiring and rewarding machine learning and statistics it will lose its analysts. When in doubt an organisation should hire analysts before recruiting for any other roles.
Analysts must be appreciated, rewarded and encouraged to grow to the heights of their chosen career. Of the cast of characters in this write-up, are the ones that are imperative to every organisation. This leaves space for specialists whose services will be needed when an organisation knows exactly what is expected from them. In the meantime, business enterprises must start with analytics and be proud of the newfound ability to open its eyes to the rich and beautiful information in front of them, as data-driven inspiration continues to grow as a powerful disruption.