The rise and rise of the ‘Soft Quant’
By: James Bailey
Tags: Articles, Data Science, Quantitative Finance, Solutions, Leadership

A critical hybrid between the technical and the business-minded

Business can be intimidating for a quant, and vice versa!

“Purple people”; “Analytics translators”; “Light quants”; “Unicorns”.

Just when you thought every label possible had already been coined to describe the famed nomads who straddle the worlds of business and technology, I bring you a new one — the “Soft Quant”. You may hear the word “quant” being thrown around your organisation as someone who identifies as a Quantitative Analyst, is educated in a quantitative discipline, or is currently working within quantitative finance. People often describe quants as having intimidating intelligence, but suffer poor communication skills, so often their ideas are undervalued. What I have discovered is that there is a spectrum of quants who live among us; and the scale ranges from “hard quant” to “soft quant”. Let me explain.

What differentiates a “soft quant” from a “hard quant” has nothing to do with their texture or elasticity. Wayne Eckerson — author and analytics thought leader — describes “purple people” as those who are, neither pure technologist (red), nor pure business (blue); they are a blend of both. Meanwhile management consultancy McKinsey and Co. describes “analytics translators” as people who, “draw on their domain knowledge to help business leaders identify and prioritize their business problems… then convey these business goals to the data professionals… and finally ensure that the solution produces insights that the business can interpret and execute on”. My definition of “soft quant” is a blend of both — those who can produce sophisticated models and sympathise with data experts, but also communicate clear strategy and risk with their business counterparts. On the other hand, a “hard quant” is your typical technologist, educated in deep quantitative disciplines and uninterested in dealing with people or solving typical business problems. However, they possess unfathomable skill in data analytics and modelling, critical to accurate reporting for each business.

In my travels I have seen the benefits of being both, but also the surprising and dramatic disconnect between the technical world and the business world. This is mainly due to a lack of trust and respect between the two worlds: neither side understands the pressures, deadlines, and challenges that the other faces in order to achieve the common goal. Each side underestimates the required efforts of the other, while becoming frustrated that things aren’t progressing due to some perceiving trivial obstacle.

Soft quants are becoming increasingly sought after as additions to data science teams for their leadership, communication, industry expertise, and project management skills. Their ability to ensure that organisations derive real impact from various analytics projects deems them indispensable and highly versatile in the projects they are placed in. So now that we are convinced soft quants are necessary for success of business intelligence solutions, where do we find them?

It may seem like an organisation may ‘just hire’ some soft quants externally and throw them into the business like a holy hand grenade for rapid problem solving and project execution. Need I remind you of the period not long ago, when data science had only just become ‘sexy’ and every executive who could afford a team of data scientists flaunted them like a new watch, only to restructure when the aforementioned data scientists had no clear direction or problems to solve. It is for this reason, that hiring a fresh soft quant with many transferrable skills will lack one main attribute which brings them immediate value and impact: deep company knowledge. It is important, therefore, to identify leadership and organisational skills within technical teams to promote the use of deep company knowledge to straddle across the business world. Similarly, strong attention to detail and rational thinkers within business teams may also yield potentially high-performing soft quant roles. Soft quants often come from diverse backgrounds, and rarely possess a linear career path. They perform best within a team of diverse backgrounds for maximum innovation potential, and maximum capability to liaison/negotiate with multiple areas of a business for consensus in project execution.

With McKinsey & Co. forecasting a growth in demand for “analytics translators” of between two and four million by 2026 in the US, it is easy to see that upskilling identified leaders in each world to a soft quant status will become an important success factor for any business. Implementing successful projects as a result will provide fulfilment for the technical teams and increase retention, which will ease pressure on executive leaders to replace expensive talent.

References:

“Physics Envy” and the Second — Class Status of Translators, 2019, International Institute For Analytics (Tom Davenport), <https://www.iianalytics.com/blog/2019/10/30/physics-envy-and-the-second-class-status-of-translators>

Analytics translator: The new must-have role, 2018, McKinsey & Company (Nicolaus Henke, Jordan Levine, and Paul McInerney), <https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-translator>

Purple people: The heart of cognitive systems engineering, 2015, Deloitte (Tom Davenport), <https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/artificial-intelligence-purple-people.html>

“Purple People”: The Key to BI Success, 2010, Wayne Eckerson, <https://tdwi.org/Blogs/TDWI-Blog/2010/04/Purple-People.aspx>