Working as an Analytics Team
Image credit:
Randy Fath
The sheer volume of data available today presents real challenges for businesses and opportunities for those who can analyze and explain it. The department leading the charge must enable and empower the rest of the organization with solutions to make better, data-driven decisions. Kim Larsen writes, “Fire Your BI Team” in a recent appeal to shift the mandate and engagement model.
Whether this group call themselves Analytics or Operations Research or Data Science, who are these people, and what are their roles in the organization?
Cassie Kozyrkov, talking about the talent marketplace in this space, in “What Great Data Analysts Do — and Why Every Organization Needs Them”:
“The top trophy hire, the data science unicorn, is elusive, and it’s no surprise: a “full-stack” data scientist has mastery of machine learning, statistics, and analytics. When teams can’t get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Which of those skills gets the pedestal?”
Brandon Rohrer explores 6 archetypes of Data Scientist Practitioner: the Generalist, the Diva, the Detective, the Oracle, the Maker, and the Unicorn.
What these archetypes have in common is that they provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting time and money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise.
Kozyrkov:
“But what the uninitiated rarely grasp is that the professions under the data science umbrella are completely different from one another. They may use some of the same methods and equations, but that’s where the similarity ends. Far from being a lesser version of the other data science breeds, good analysts are a prerequisite for effectiveness in your data endeavors.”
Kevin Hartman of Google, puts it this way
“The way I teach analytics is informed by my business day job,” Hartman said. “That is also why I don’t want to just impart practical knowledge to them, because that practical knowledge has a very short half-life. Things are moving so quickly. If I teach you how to do something today, that might not be relevant in a week or a year.”
Collaborative teams harness the best of diverse skill sets. They learn from one another, and are much more likely to understand the big picture goals and break down barriers. Analysts, working close to the business, curate practical domain level connections. Ultimately, the best teams are balanced to iterate effectively through the process going from problem to delivery, spending minimal time on modeling and maximum time at the ends of the spectrum:
- Beginning of Workflow: Critical Business Understanding, Domain Expert Engagement, Data Understanding, Data Quality, and Feature Engineering
- End of Workflow: Communication with Project Stakeholders, Ensuring Product Delivery
Of the cast of characters mentioned here, the ones that every business needs are decision-makers and analysts. When in doubt, fill the team with analysts before other roles. Both data science and strategic analytics are tremendously important, have clear purposes, require different competencies, and offer rewarding career paths. Appreciate them and reward them. Encourage them to grow to the heights of their chosen career (and not someone else’s).
