We’ve noted on OutsideInsight.com a continued trend in which companies in previously ‘nontech’ industries are beginning to position themselves as tech companies – from big banks, to the automotive industry to the fashion industry.
In a world where tech and data have become the hottest commodities, yet simultaneously more accessible than ever, is it time to adjust our thinking around what constitutes a tech role or a tech company?
The role of a marketer has moved from nearly pure creative toward ROI-driven quant. The term ‘analyst’ in the context of a major bank might now refer to either a data scientist, or a traditional financial analyst. The roles of Chief Data Officer and Chief Data Scientist have come to the forefront, and with them entirely new departments with impact across an organization. These trends point to a confluence of what were previously viewed as very siloed roles and responsibilities.
If you look at job descriptions today, we’re beginning to see overlap in the experience, capabilities and responsibilities required for different roles. Goldman Sachs is looking for a subscription-based sales lead. Fast food chains are hiring data scientists. Politicians are taking their talents from Washington to Silicon Valley. The list goes on.
Similarly, companies that are considered purely ‘tech’ are not hiring only for tech roles. According to a report by Glassdoor, “Out of all the open positions at tech companies on Glassdoor today, just over half are tech roles (57 percent or almost 71,000 open jobs). The remaining 43 percent are non-tech roles, or almost 53,000 open jobs.”
The future of work
One potential contributor to this trend, specifically when it comes to data science roles, is the fact that demand for pure data scientists is still on the rise, and supply remains relatively limited. As a result, organizations doubling down on their data and predictive analytics offerings are getting creative. After all, a large part of data analysis is based on mastery of language, psychology, economics and science. Who’s to say experts in these fields can’t apply what they’ve learned to applications in the machine learning and data analysis space?
The Wall Street Journal reports some big banks, including TD Bank Group, Principal Financial Group and Zurich North America are betting on it. These firms “have assembled staffs that create machine-learning and predictive-analytics applications, including systems that make predictions around retirement assets and insurance claims. These companies have been hiring not only computer scientists, but also people with backgrounds in physics and English.”
Essentially, the entire team doesn’t need to be filled with advanced AI engineers in order to build a successful analytics product. TD Bank’s AI team, who is looking to predict individuals who might soon purchase a home, includes people with degrees in theoretical physics and biochemistry. Principal’s 24-person research and data science team includes people with English degrees who can look at textual analytics, noting that analytics is essentially a language problem.
The result is a collective group with diverse backgrounds. In order to combat discrepancies in expertise and experience, Principal is requiring these new hires to first undertake an extensive course of certifications upon receiving the job in order to ensure “everyone has the same baseline competencies and opportunity to contribute.” According to their team, it’s worth the wait.
What is a tech company anyway?
On the other end of the spectrum, DC-born salad chain Sweetgreen – a brand so profitable and prolific it was on track to IPO – announced a major pivot late last year to instead focus on technology in an effort to “fix the entire restaurant industry and improve the health of the world.”
According to a profile in Inc. Magazine, the founders have effectively begun “thinking like a tech company”, developing their own mobile app, adding digital ordering options like Uber Eats, and embracing cashless checkouts. As a result, they were able to raise $200M and soar into the ethos of unicorn valuation.
The founders’ vision is effectively to “blow up the whole idea of a restaurant.”
“How do you think about the menu in your hand in a digital way? How do you think about the experience in the kitchens in a digital way? How do you completely break this notion of what a ‘restaurant’ is and what a ‘menu’ is?” Co-founder Jonathan Neman said. “This menu of 12 things, why does it even make sense?”
Launched by three graduates of Georgetown University fresh out of undergrad, the business began as a campus eatery and quickly emerged to the profitable chain it is today before the most recent pivot. Now, the company is bringing on “data scientists from Amazon, product czars from Uber, [and] digital mavens from big food chains like Starbucks and Domino’s” to develop the food platform of the future.
“We want to be the Nike or the Apple or the Spotify of food."
Combined with the founders’ knowledge of the food and retail industries, expertise from their growing board and this new influx of data science and tech talent, the team is looking to do more than hop on-board the “we’re a tech company” trend. Being a tech company, says co-founder Nicolas Jammet, “is no longer confined to selling software or hardware. Technology is the enabler, but it is not the product.”
The world of work is at an interesting crossroads today. These new developments in job descriptions, cross-breeding of tech and nontech talent, and move toward tech-forward business models across the board hint at what some experts suggested might be a potential impact of advancing AI technology. Rather than strictly eliminating jobs, it’s creating new opportunities for skilled professionals across different sectors.
