We have gobs of data, nearly limitless cloud compute, and ever-improving machine learning algorithms, so what on earth is holding companies back from succeeding with big data? "Talent, talent, talent," says Dr. Kirk Borne. "The limiting factor is talent." To be sure, Borne has done more than most when it comes to fostering data science talent. Fourteen years ago, before his recent stint at Booz Allen Hamilton or his new gig at DataPrime, Borne helped create the nation's first data science degree program at George Mason University.

In recent years, as the promise of artificial intelligence (AI) crystallized across industries, organizations revamped their talent strategies to gain the skills necessary to deploy and scale AI systems. They hired legions of data scientists and other data experts to build AI applications, trained analytics translators to connect the business and technical realms, and upskilled frontline staff to use AI applications effectively. One role in particular, the data scientist, has been especially difficult for leaders to fill as competition for its illusive knowledge increased. McKinsey Global Institute research has also highlighted the talent shortage and the potential for hundreds of thousands of positions to go unfilled. Incumbent companies found it especially hard to compete with start-ups and tech giants such as Google to attract or retain the best practicing data scientists and the newest crop of graduates. One multinational retail conglomerate, for example, put in place a highly attractive package last year, with education perks and salaries up to 20 percent higher than market rates, to attract the 30-plus data scientists it needed to support its strategic road map of priority AI use cases.

In recent years, as the promise of artificial intelligence (AI) crystallized across industries, organizations revamped their talent strategies to gain the skills necessary to deploy and scale AI systems. They hired legions of data scientists and other data experts to build AI applications, trained analytics translators to connect the business and technical realms, and upskilled frontline staff to use AI applications effectively. One role in particular, the data scientist, has been especially difficult for leaders to fill as competition for its illusive knowledge increased. McKinsey Global Institute research has also highlighted the talent shortage and the potential for hundreds of thousands of positions to go unfilled. Incumbent companies found it especially hard to compete with start-ups and tech giants such as Google to attract or retain the best practicing data scientists and the newest crop of graduates.

AutoML, with its ability to perform data pre-processing, ETL tasks, and transformation, will likely become the most popular trend for the year 2020. With the advent of big data, advanced analytics, and predictive models, data scientists today are expected to possess more talent and updated skills when it comes to handling artificial intelligence and machine learning. But these highly skilled data scientists are rare to find. However, bridging the skills gap, the other side of the herd has not only been able to survive but are also capable of building models using the best diagnostic and predictive analytics tools, and part of the reason is AutoML. AutoML packages like auto-sklearn can automatically do the model selection, scoring, and hyperparameter optimisation.

BENGALURU: Tech upskilling in areas like artificial intelligence (AI)/machine learning (ML), digital marketing and design thinking is crucial for boosting organisational performance, a survey by skilling platform Great Learning has found. The survey was conducted among 307 companies and 25% of all respondents believe equipping AI and machine learning skills is the goto skill for upgradation. In light of the growing digitisation of all aspects of business, digital marketing was seen as the second-most crucial tech skill required for companies to thrive in today's increasingly digital world. Around 19% respondents believe digital marketing skills are imperative for companies to grow. "The technology skill gap among employees is one of the biggest challenges that organisations in India are beset with. While it is encouraging to see that a majority of companies are aware of the need for upskilling, the time to act is now," Hari Krishnan Nair, co-founder of Great Learning, said.

The potential for AI and automation are huge; McKinsey forecasts that the potential economic impact of automating knowledge work could be between $5.2 trillion and $6.7 trillion by 2025. And as we move into the new year, AI will continue to evolve beyond chatbots to something more akin to a digital colleague. While Alexa, Siri and their ilk might seem like modern-day inventions, the concept of the chatbot has actually been around since the 1960s with the creation of ELIZA by MIT professor Joseph Weizenbaum. Of course, ELIZA was fairly simple by today's standards. And in light of what is possible today, Star Trek's talking computer revealed the stunningly prescience of the series' creator.

A survey amongst 307 corporates, ranging from SMEs to large corporates, highlights that tech upskilling in skills like artificial intelligence, machine learning, digital marketing and design thinking is crucial for boosting their organizational performance. Conducted by Great Learning, the survey aimed to find out the top skills that organizations will need to drive future growth and how they plan to bridge the impending skill deficit amongst their ranks. Inspite of the increased awareness around upskilling, the survey found that 47% of the companies have still not assigned budgets for upskilling their workforce. Hari Krishnan Nair, co-founder, Great Learning said: "The technology skill gap among employees is one of the biggest challenges that organizations in India are beset with. While it is encouraging to see that a majority of companies are aware of the need for upskilling, the time to act is now. Skilled employees will continue to be the biggest asset for any organization going ahead and while options like lateral hiring and outsourcing may help in the short term, from a cost and effectiveness point of view, upskilling is the best way to stay competitive in the long run."

It's no secret that our cybersecurity industry today suffers from a yawning talent gap -- a statistical juggernauton track to reach 3.5 million unfilled positions by 2021. As the wakeup call spreads, we're seeing more cross-disciplinary trainingsand nurture efforts deep into the educational pipeline -- from pre-K, elementaryand middle schoolinitiatives, to programs for high schooland higher education. We just got fresh momentum from the recent White House "Executive Order on America's Cybersecurity Workforce," which proclaims our collective cybersecurity talent pool "a strategic asset that protects the American people" and in need of "work-based learning, apprenticeships, and blended learning approaches…for both new workforce entrants and those who are advanced in their careers." As a cybersecurity education evangelist, this proclamation is at once music to my ears and the mother of all to-do lists. That's because the Executive Order focuses primarily on the Why and What -- meaning it's largely up to the industry to keep figuring out the How.

Enterprises are no longer struggling to decide if, but when and how, to apply artificial intelligence in their operations. Businesses are constantly finding new ways to leverage AI, raising adoption rates and comfort levels with the technology. This is what the second edition of our global AI research confirms, according to views from senior executives, employees and consumers. A quarter of the senior executives in the study say that their companies plan to fundamentally reimagine their businesses with AI in the next three years--these organizations are the "visionaries" that are taking bolder steps. What's also significant is that more workers see career opportunities from AI than feel it threatens their current job.

The essence of every company's revenue growth plan is based on how well they attract, nurture, hire, grow and challenge the best employees they can find. Often relying on manual techniques and systems decades old, companies are struggling to find the right employees to help them grow. Anyone who has hired and managed people can appreciate the upside potential of talent management today. Strip away the hype swirling around AI in talent management and what's left is the urgent, unmet needs companies have for greater contextual intelligence and knowledge about every phase of talent management. Many CEOs are also making greater diversity and inclusion their highest priority.