What are the 3 Stages where your Data Science Teams might Fail?
What is the right mix of skills, tools, and processes for every stage in your analytics maturity
Today, most organizations claim to be AI-driven. What this really means is open to interpretation. Some organizations might be churning out excel reports, while others might be building cognitive models. But, the truth is that everyone aspires a play in analytics.
But the big question is, where should one get started?
Where should one source the talent from? How should teams be organized? How do they scale up to avoid the inevitable, slow death that stares most teams in the face? What works in the early stages of data science teams doesn’t hold true a few years later, after they deliver on their initial promise.
With the data science market still evolving, there are no standard guidelines to follow. This article aims to address this gap.
Firstly, what’s the need for Data Science teams?
Going by any analyst estimate, hundreds of billions are being thrown in by companies to solve problems with data. The key ask is to draw actionable insights that can drive business decisions. The very mention of the word ‘analytics’ brings up images of predictive models and fancy algorithms.
However, data science delivers value only by the right application of pertinent techniques in a relevant business context. Even the simplest of exploratory analysis when done right delivers substantial returns. AI has it’s rightful place, but it’s not a silver bullet for every data problem.
What are the 3 stages of the Evolution of Data Science teams?
Let’s see how one can go about incubating a data science practice or for that matter a startup with analytics offerings. Drawn from the lessons in building Gramener, I’ll trace the 3 distinctive phases of growth, focus areas and skills needed in each, and share intelligence on how to acquire the right talent.
Surprisingly, this scaling of data science teams has several similarities with how our forefathers built shelters over the ages. So, to throw in some fun, lets contrast each stage with the analogy of home-building and trace the rising sophistication across times: the stone age, middle age and the modern age.
Stage 1: “Makeshift Camp”
The best way to bootstrap a data science practice is by diving right in, with prioritized challenges, and a set of readily available tools. One must pick a few urgent and critical business problems that can be solved by data, rather than boiling the ocean. Analytics is a long journey and a job begun is half done.
Similarly, too much preparation for an ideal mix of skills could lead to analysis-paralysis. Onboard generalists, people who can cover many of the needed skills in analytics (say statistics, programming and information design), even if only to a limited depth. The need is for survivors who flourish in scarcity, wear many hats and instill dynamism to solve any given challenge.
Hiring such startup-stage superstars needs to be done unconventionally. Shun all regular channels. Tap into your network, speak and connect in conferences and send out feelers through tech evangelists. These early folks don’t come in for the job or perks, but for unbound challenges that feed their raw passion.
Start where you are. Use what you have. Do what you can. — Arthur Ashe
In our house-building analogy, we’re now in the stone age. To meet a basic physiological need, cavemen built basic, functional homes. Generalist skills that helped pick feasible sites, gather raw materials, design the crudest architectures, and a common sense to “get job done” was all that was needed.
Stage 2: “Thatched House”
Having won small victories with the initial team and established a purpose, the data science team can start fanning out into adjacent use cases. Slowly expand the scope of problems addressed and deepen partnerships with users. Initial pilots can now mature into longer initiatives spanning a quarter or year.
Showcase enhanced ROI to justify the next level of investment needed. While things may start to work in one’s favor, avoid over-committing in this interim stage. Start specializing by investing in a few deeper areas (say Sales analytics, NLP), while continuing to be shallow and get the job done in others (say design).
As the generalists continue to anchor the show, start filling more specialized roles. Expand hiring channels with hackathons, specialized finishing schools, while also up-skilling existing folks into data science. Start organizing teams by the few areas of specialization chosen, while keeping the vision broad, and response nimble.
Advance, and never Halt, for advancing is Perfection. — Khalil Gibran
As humans marched on, the sophistication of house building shot up. Efficiency and effectiveness came about with slightly better raw materials and few core roles like masons, while generalists covered the rest. In spite of dreams for a little more than a roof-over-the-head, function & purpose reigned supreme.
Stage 3: “Palatial Home”
As an evolved entity, the data science team is essentially a mature business unit now. With specialized domain expertise and grasp over all key data science skill areas, the team is now ready to handle sufficiently complex problems, across a wide breadth of areas.
No longer faced with existential challenges, the team’s mandate can be deeply woven into the long-term business objectives of the stakeholders. Teams could be structured with vertical alignment, or as technical centers of excellence alias horizontals, or maybe along with a hybrid, matrix structure which goes in between.
Fuel the fast-growing practice with mature hiring processes and in-house recruiters. For big and diverse numbers, cast the hiring net wide and far by adding conventional channels. It's critical to play a balancing act to retain early generalists, meet the aspirations of specialists, while also standardizing processes to scale the organization.
Perfection is not attainable, but if we chase perfection, we can catch excellence. — Vince Lombardi
Fast-forward to the modern age and we have construction firms that operate at scale. Specialized organizations own aspects of home-building like design, architecture, engineering. With form and function taken for granted, the promise is for superior quality of life and nuanced aspects like elegance and usability.
Summary
We’ve looked at the 3 key stages of a data science team’s evolution. The emphasis in the first is on taking baby steps to get started quick-and-dirty, to accomplish small goals. The second involves taking measured strides, by bolstering capability in chosen areas, while staying a generalist in all others.
Finally, the third is more like pacing a marathon, where one must get the focus and process right, and be prepared to efficiently perform in the long haul. Thus, the focus in each stage, the skills, and channels of hiring, and the guidelines to structure teams are drastically different across the 3 phases.
A failure to recognize this distinction can prove fatal for the practice, while adept handling of these nuances can act as a nitro booster to unleash growth. And the key tenet that cuts across all three is the delivery of actionable insights that drives business decisions and brings in ROI for the data science dollars.
If you found this interesting, you will enjoy these related articles I wrote:
- 4 Superpowers That Will Make You Indispensable In a Data Science Career
- 4 Ways to fail a Data Scientist Job Interview
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