This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEOs to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results. In this blog series, I have
Self-driving cars, lifelike robots, and autonomous delivery drones are the sexy, headline-grabbing face of the digital transformation that we see all around us today. None of these would be possible, though, without data – the oil of the fourth industrial revolution – and the analytic technology we’ve built to allow
Big data is of big importance. It has been for years. But in 2021, we’re going to see marketing teams especially focused on taking control of customer data and analytics efforts for the welfare of the enterprise. Case In Point: Research shows it’s four times more expensive to get a
According to Gartner’s latest CIO survey, 94% of Energy, Oil & Gas Utilities’ IT leaders prioritize location and cybersecurity, 38% are investing in Artificial Intelligence and machine learning and 17% in the Internet of Things including sensor-based video technologies. Energy, Oil & Gas Utilities are facing increasing pressure to reduce
Marketing teams have been struggling for years to adjust to their changing roles amid digital transformation. This year, they’ve added a global pandemic, layoffs, and budget cuts to the ever-changing marketplace. Whereas just a few months ago, many were arguing whether CMOs are even relevant post-digital transformation, marketing teams are
As we start a new decade, the top trends for business analytics platforms are cloud, artificial intelligence, automation, on-device (edge) analytics, and augmentation. Cloud ecosystems empowered with AI have matured greatly in recent years. Smart, augmented prediction and decision-making tools are at a stage where they are ready to be deployed across organizations, from the boardroom to the shop floor. The challenge is making sure your business is ready to use them.
Today’s HR teams potentially have access to huge amounts of data, and this can bring great rewards for those who use that data intelligently. But, data also brings its own unique challenges. Therefore, before implementing any data-driven HR approach, it’s important to consider the potential pitfalls that surround employee-related data, particularly when it comes to their personal data.
In the last few years, we’ve seen incredible advances in data, analytics, and artificial intelligence (AI). While there is understandable concern about what this means for human jobs, I still believe this is an exciting time for businesses – and the people who work in them.
As AI and machine learning continue to develop, the way we use analytics also continues to grow and change. While in the past, businesses focused on harvesting descriptive data about their customers and products, more and more, they’re about pulling both predictive and prescriptive learnings from the information they gather. So – what is the difference between descriptive, predictive analytics and prescriptive analytics? And do you need the latter in your company?