Building deep and ongoing data science capabilities isn't an easy process: it takes the right people, processes and technology. Finding the right people for the right roles -- as employers and job seekers alike can attest to -- is an ongoing challenge. In this special feature, ZDNet examines how advances in AI, visualization and cloud technology are shaping modern data analytics, and how businesses are addressing data governance and a potential data science skills gap. "The people part is probably the least well-understood aspect of this entire equation," John Thompson, global head of advanced analytics & AI at CSL Behring, said during a virtual panel discussion on Thursday. As the head of analytics at one of the leading international biotechnology companies, Thompson oversees data science teams that tackle a wide range of initiatives.

Machine learning engineer Ari Font was worried about the future of Twitter's algorithms. It was mid-2020, and the leader of the team researching ethics and accountability for the company's ML had just left Twitter. For Font, the future of the ethics research was unclear. Font was the manager of Twitter's machine learning platforms teams -- part of Twitter Cortex, the company's central ML organization -- at the time, but she believed that ethics research could transform the way Twitter relies on machine learning. She'd always felt that algorithmic accountability and ethics should shape not just how Twitter used algorithms, but all practical AI applications.

Originally published on SKAEL's Blog, republished (Feb 2021) "Work is work, and what matters to the worker is neither the product nor the technical process, but the pay, the hours, the attitude of the boss, the physical environment. The application of [technology] to industry will mean very little. What they will care about is what their fathers and mothers care about today -- improvement in the conditions of labor." The quote above was written in 1950, imagining a working world 50 years in the future in the year 2000. Huxley imagined that a huge increase in technological capabilities would change the nature of work substantially and it would be very different from what the farmers and factory workers did in his time. It's nearly impossible for today's working generation to imagine going to the office and seeing rows upon rows of desks with typewriters and adding machines clicking away, with secretaries delivering hand-typed memos and letters to physical inboxes as the primary form of written communication.

Digital transformation is not the latest industry catchphrase. It's a movement--a significant shift in how companies operate, compete, and grow, according to Archie Deskus, who was appointed Senior Vice President and CIO of Intel, the US chip maker, early this year. Deskus reminds us that a corporation is a living organism; it must continue to shed its skin. Methods, focus and values, all have to change. The sum total of those changes is transformation.

Optimization and The NFL's Toughest Scheduling Problem - June 23 At first glance, the NFL's scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the quadrillions. In this latest Data Science Central webinar, you will learn how the NFL began using Gurobi's mathematical optimization solver to tackle this complex scheduling problem.

BOSTON – The daily deluge of depressing headlines notwithstanding, it should be heartening to know that "if you look at the data, the world is not going to hell in a handbasket," said Dr. Amy Compton-Phillips, executive vice president and chief clinical officer at Seattle-base Providence St. Indeed, said Compton-Phillips in the opening keynote of the HIMSS Machine Learning and AI for Healthcare event here in Boston, if one looks at the numbers for global health and life expectancy, there's been a steady and significant improvement of worldwide well-being over the past half-century. "Of all the times to be alive on earth, the best time is now," she said. "It just doesn't always feel that way." And the primary reason things have improved to that extent? Not just raw data, but the use of that data to tell stories that enable positive changes – and the way those stories can in turn enable a deeper understanding of what those data insights are telling us. "We've been doing well because of the scientific method: seeing how we are, applying learning and then improving," said Compton-Phillips.

But for drivers, the beauty of Southern California's great weather and scenery is ruined by one thing: traffic. According to a report by INRIX, my hometown is the worst city in the world for traffic, with a record of 102 hours of congestion during peak hours in 2017. My classmate, Ericson Hernandez, comes from New York City, which is ranked third globally for its traffic woes. Together, we decided to use machine learning to figure out the roots of bad traffic, including elements like road damage from potholes and cracks, and make rides around our beautiful cities enjoyable again. As Ericson and I started studying electrical engineering at Loyola Marymount University, we began to develop an interest in a relatively new topic to the engineering world: machine learning.

At the moment, legal AI is at the "frothy" part of the hype cycle. It's a bit like the scene What did the Romans ever do for us? in the film the Life of Brian, but in reverse. The scene's premise is that the Romans did little, but of course they did a lot. Legal AI promises a lot but at the moment has delivered little. Somewhat frustratingly, while a decade from now it will be obvious in hindsight that the world beater was going to be "X", right now we don't know what that "X" will be.

Technology is changing at an unprecedented pace. The convergence of business and technology across everything we do is both exciting and challenging to business and technology leaders. But CIOs don't have to get swept up in the maelstrom. Rather than thinking about how technology will change the way we work, CIOs need to think about harnessing that technology and using it to change the way we work to achieve more. Accenture's 2017 Technology Vision looks at IT trends and innovations that people will apply to shape business in the next three years.