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As I was on my way to our encore of an unconventional convention in Europe, I thought about the famous phrase, “Mind the Gap”—used by the London Underground to warn passengers of the gap between the platform and the train door—as the perfect metaphor for the talent gap we are facing in managing data science and analytics platforms. “Data science” and “analytics” are no longer myths or buzzwords; they represent essential business talents, skills and tools. Every day we hear stats, predictions and statements in the news and in research about the shortage of data scientists, and the increasing innovation around data, artificial intelligence (AI) and machine learning (ML).
A recent LinkedIn Workforce Report indicates that desired data skills are in short supply, reporting an estimated shortage of 151.7K data science professionals in the U.S. alone. And if left unresolved, this shortage will only continue to grow with the development of AI and ML, as a World Economic Forum study predicts that data-related jobs will be the most in demand within the next four to five years, along with AI and ML specialists. On day two of the conference, I hosted a roundtable discussion with seven customers and partners—Copa Air, Kubrick Group, Sainsbury’s and UBS—to discuss the urgency of this talent gap and how we can solve for this together.
Altering the Status Quo
Companies of all sizes are recognizing the tremendous potential for data, but many struggle to turn that data into actionable insights that improve business results. Our roundtable panelists are experts in leveraging their data and spoke to how they’ve approached building a culture of analytics within their respective organizations and earning buy-in from internal stakeholders across the organization. “You need to lay a breadcrumb trail,” revealed Nick Allen, partner at the Kubrick Group. “The process can be a slow one, but once they are on the right path, they begin to realize the power of data.” This is one of the principal challenges in today’s age of innovative speed, it is not how things can get done, but how fast they are done.
“I didn’t know SAS or any other complex technology when I took on this role but was able to learn Alteryx quickly. It is important to have a tool that people can use—it is not about overcomplicating, it is about simplifying,” shared Samantha Hughes, analytics systems developer at Sainsbury’s. Hughes and her team are helping to alter the status quo at Sainsbury’s by enabling their colleagues to be self-sufficient in advanced analytics, breaking down the model of reliance and dependency on the data science team. “When we relocated the business and lost the data science support we were used to, we had to start over. Now I know that if I leave [Sainsbury’s] tomorrow, the next person will know exactly what I do in my job because Alteryx is self-documenting.”
There is no perfect answer or approach to transform your businesses’ mindset to a data-driven one, and it won’t happen overnight, but Jennifer Belissent, principal analyst at Forrester, shared her perspective on where to start. “We must demonstrate data analytics in a way that other people will understand. If we go head first into technical jargon, it will ward people off, rather than drawing them in,” warns Belissent. We need to explain the benefits of developing data science and analytic talent in a way that is relatable to respective teams across the business; for instance, helping IT teams alleviate hours of work by making other teams self-sufficient in reporting.
Stay Curious My Friend
A critical component of creating a culture of analytics within an organization is having the talent to support it. The burning question: How do we bridge an ever-expanding gap, which so many businesses seem to be falling into?
Many businesses recruit for skills, but neglect to recognize the human element behind the skills, overlooking existing potential talent. Hughes made a nod to this concept when expressing that, “Curiosity is one of the key traits [Sainsbury’s] looks for when hiring. We look for undergraduates who are doing relevant degrees and go through their universities to make them aware of the opportunities with regards to data-specific roles. I think part of the problem is the idea that data roles aren’t fun, but there’s no better thrill than uncovering something new in the numbers and making a ton of progress.”
Allen emphasized the importance of pairing communication skills with technical talent to tell the business story, corporate curiosity and the ability to break down a problem. “We think it is better to spend an hour questioning the problem and task process than jumping in head-first and getting the wrong end of the stick.” Kubrick Group and Sainsbury’s shared similar strategies in seeking professionals with experience in business strategy, mathematics, engineering and other applicable fields to develop data science talent. Allen also shared that while training new talent is important, organizations need to make an upfront investment in experienced workers to create the leadership needed to grow these budding data scientists.
Reaching Lightspeed: Millennials Take the Wheel
During the discussion, Nuria Saavedra Miro, intelligence analyst at Copa Air, touched on the importance of recruiting graduates and cautioned organizations not to underestimate entry-level talent. She and her colleague, Isacar Racine Rodriguez, senior intelligence analyst, shared that their intelligence team is primarily comprised of Millennials under 30. Rodriguez shares, “Young people tend to be more willing to learn new things—that’s a really key skill because the rate of new tech coming out is so high—no single person can learn it all…companies should be helping their younger team members to get to grips with things like AI and its potential.” This speaks to what is known in baseball as the “farm system,” providing an opportunity to “grow your own” and uniquely up-skill staff to meet the needs of the business.
We also discussed the importance of preparing both the current and incoming workforce to tackle new challenges with the advancement of technologies like ML and AI, and what these developments mean for the future job landscape. Nick Bignell, director of analytics at UBS, shared his perspective on the topic: “We are a large company and we invest a lot of money in this kind of tech [AI and ML] to help us complete routine tasks. The technology is not replacing employees—you still need a human steward there to deal with any problems that arise—really, they [humans] are just being freed to do more interesting jobs.”
Next Steps: A Leap for Mankind
We’ve discussed how organizations can take tangible steps to resolve the skills shortage within their own businesses, but the data science and analytic talent gap cannot be the responsibility of individuals within an organization alone. Industry needs to take the lead in rallying our global communities around a common goal and work in lockstep with universities to create the workforce of the future.
We need to extend that reach to non-profits and those driving social good, so their initiatives can keep pace with technological innovation. Yug Muley, an Alteryx for Good evangelist in India, joined our roundtable and shared the importance of helping charities and non-profits to become self-sufficient, and the role data science and analytics can play in that. “We put a lot of focus on training the people in the charities we work with. They don’t have the same resources, but they face similar challenges and have the talent potential. I recently helped a woman that was 70+ years old to learn Alteryx, which was pretty inspiring.”
I want to thank Forrester and the members of the press that helped us amplify the discussion and our customers and partners that took time out of their day to discuss this challenge and share your ideas for the future. Your passion for bringing the thrill to solving drives us every day and we are confident we can make the leap across the gap together.