In 2021, we launched the Future Says Series to bring you commentary, interviews, and insight from some of the world’s brightest best minds so they could discuss the past, present, and future of artificial intelligence (AI). From the conversations we’ve had, the experts we’ve spoken to have made one thing clear – AI has the power to transform our lives. But that’s just the tip of the iceberg.
Each season has brought its own unique flavor to the table and given viewers comprehensive, wide-ranging commentary on the world’s latest technology trends. In Series 1, we learned how Telefonica is using data for social good, why DNB Bank is building an ESG Data Task Force, how Scania is laying their data mesh foundations, the ways Husqvarna is developing data-driven business models, how H&M is keeping people in the loop, and more.
Moving forward, in Series 2, leaders from Google, Capgemini, PWC, Fasanara Capital, Zenseact, King, and the U.K.’s National Health Service (NHS) spoke about fostering diversity and inclusion, building responsible AI frameworks, augmenting cryptocurrency decision-making, utilizing gaming as an AI proving ground, and dealing with the healthcare industry’s data privacy challenges. And to wrap up Series 2, Altair founder and CEO Jim Scapa talked about how Altair is building its workforce, tools, and business model around the idea of technological convergence. The season’s diverse, international lineup of experts from a range of industries ensured there was something useful for any viewer.
The Data Science Field: Obstacles and Potential
But while our guests spoke about the immense benefits and further potential for AI, they often referred to a common challenge – finding experienced, specialized data scientists that can handle their rapidly growing data science needs. Recent data backs this up; according to Glassdoor, data scientists are the most sought-after employees in the workforce today. Moreover, according to the World Data Science Initiative, the number of jobs requiring data science skills is expected to grow by 27.9% by 2026, and data engineering job postings have grown by an astonishing 88.3% within the past few years. But supply is falling far short of demand. In the U.S. alone, Deloitte estimates there’s a shortage of 250,000 data scientists, and the British government released similar findings in their latest National AI Strategy Report.
Additionally, prior guests have talked about the need not just for data scientists, but for people equipped with domain knowledge. In Series 1, Piab’s Girish Agarwal spoke about how there are 22,000 mechanical engineers at Husqvarna that can configure a piston but not an AI model. He said that if the company could make all these engineers more data literate, they could achieve outstanding innovation alongside a team of data scientists working collaboratively. And in Series 2, Zenseact’s Vanessa Eriksson mentioned how, since cars can now generate a wealth of up to 50 terabytes of data per day, the automotive industry needs a similar wealth of people equipped with the data and domain knowledge needed to put this data to use.
As the statistics show, there’s currently a shortage of qualified data science talent. However, as data science technology and tools evolve, there’s reason to believe that more citizen data scientists – people with basic data literacy skills and advanced domain knowledge – will emerge and fill these much-needed roles. That’s largely because of the rapid growth of low- and no-code coding software tools. Combining these tools with their inherent technical and statistical background, engineers represent a unique opportunity to champion data science within organizations. In fact, Gartner has found that by 2024, 65% of enterprise software development will be considered low-code. Gartner also predicts that the market for citizen data scientist roles will grow five times faster than that of the traditional data scientist market.
When we think about how technology-related entry barriers fall, the rapid rise of low- and no-code software tools shouldn’t come as a surprise. We’ve seen a similar story in the computer market; for example, in 1990, only 15% of American households owned a computer because they were unintuitive and required users to have special knowledge that the average user didn’t have at the time. Today, not only does almost every American have a basic understanding of computers, hundreds of millions work on one each day. Thus, it makes sense that as our technology and software develop, entry barriers become lower and lower – even for non-specialized users.
Building a Smarter, Data-Driven Future
The key to the future of data science is building teams of people that have collaborative, overlapping skillsets. In Series 1, Errol Koolmeister, former head of AI engineering at H&M, discussed how the organization aimed to build teams of people with “T-shaped skills profiles.” In other words, this means that people should have a deep understanding of one field, but also have a basic understanding of many others as well. Furthermore, Telefonica’s Dr. Richard Benjamin emphasized the need for teams that satisfy “’four C’s’: creative thinking, critical thinking, collaboration, and communication.”
And in Series 2, many guests talked about ways they’re training their employees to ensure they’re using data science tools in optimal ways. For example, Capgemini’s Niraj Parihar discussed how the organization has started a creative training program to optimize employees’ AI skills and understanding by using “ranks” that indicate progress, such as “cadet, genie, guru, and captain.” The NHS has also invested in its employees’ skills, as Ming Tang described how she’s given her team time for “self-directed learning” so they can acquire data and AI skills that can enhance their roles.
Of course, finding people with the right skills and building well-rounded teams is all easier said than done. That’s why Series 3 is laser-focused on this and all things data science and engineering. This season’s guests all have engineering backgrounds, but they’ve also made unique career transitions into the AI sphere that allow them to speak to how engineers and data scientists can collaborate, enhance each other’s workflows, and better work as a team. Series 3’s lineup will include:
- Vijayakumar Kempuraj, digital twin lead, Ford Motor Company
- Ravi Parmeswar, vice president of business intelligence, Johnson & Johnson
- Jan Chirkowski, vice president of analytics and fleet operations, Kongsberg Maritime
- François Deheeger, senior fellow of AI and data science, Michelin
- Geertrui Mieke De Ketelaere, adjunct professor of sustainable, ethical, and trustworthy AI, Vlerick Business School
- Jada Smith, global engineering director of software platforms, Aptiv
Additionally, Series 3 will have a special emphasis on engineering and manufacturing because these are the industries that are lagging in digital maturity and are finding it hardest to ditch outdated legacy processes. As such, Series 3 will emphasize how to bring processes and products into the digital age, how to build good data infrastructure, how to modernize operations and organizations, and more.
That said, data literacy is still one of our main objectives not just within the Future Says Series, but within Altair. We believe anyone – regardless of industry or discipline – should have the tools and knowledge to analyze, understand, and operationalize data so they can better their team and organization. Stay tuned to this season of Future Says to hear from the world’s leading organizations and experts, and to bolster your knowledge of data science, AI, and machine learning – we can’t wait to present the newest episodes!
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