You’ve heard the saying “if you do what you love, you’ll never work a day in your life,” right? Well, I hate to say it, but that’s me. I never dreamed that I would wind up in a field that combined all of my interests, but somehow that happened. Through my research at the MIT Media Lab I get to apply my legal and social sciences background to human-robot interaction. Which yes, does mean that I mostly get to play with robots all day.

To this point, AI has been applied to augment analytics in a somewhat bifurcated fashion. On one hand, we have seen natural language support the business consumer that requires simple answers to known questions, helping them quickly take action. And, on the other, AI helps content authors and BI developers auto-suggest charts and automate data preparation, improving efficiency and reducing manual workloads. But, there’s a gap, and the value is huge.

In a previous article, we talked about the lost art of questioning and its importance when working with data and information to find actionable insights. In this article, we will expand on this topic and explain how questioning differs depending on what stage in the process you are from transforming data and information into insights.

In this series of demystifying the tech trends, my colleagues and I will be looking at busting the buzzwords to help you keep on track. Concerned about puzzling parlance, analytics argot, techie terminology – or plain old jargon? This series breaks down words and concepts to give you the deepest insight and understanding into how to talk the talk in the world of tech, so you can engage in conversations with the confidence of being data literate.

Greek philosopher Heraclitus wasn’t talking about the challenge of today’s enterprise IT landscape but the quote certainly fits. From the advent of the first digital computer in the 1940s to the emergence of first public cloud in 2004, the rate of change has only accelerated. In fact, over 60% of corporate data resides in the cloud in 2022, up from 50% last year.

The digital revolution has truly transformed modern organizations, embedding data and analytics in every business process and customer interaction. Advances in technology enable smart supply chains with predictive analytics, automated logistics for same-day delivery, and AI advisors that reduce medical errors. As this continues, workers in all roles will need new a new skill—data literacy—to collaborate with these systems and each other.

In 2019, global venture capital investment in Fintech totaled at least $33.9 Billion. For years, the incumbent players had been warning the sector that when the next recession hit, or when fintech faced a “real” crisis, that the sector would, at worst, collapse, and, at best, see a whole bevy of fintech players disappear

I’ve been blogging for about a year about the power of misinformation and our obligations as data professionals to combat it. In a March 2021 blog post, titled “The Power of Misinformation,” I outlined some of our biological instincts that make us susceptible to misinformation and how tricksters exploit them.

I don’t think anyone could argue that the last two years have changed the workplace forever. Entire nations have been confined to their homes during months of lockdowns, and the way we conduct business and interact with each other has passed a point of no return.

Asking the right questions of your data and knowing what you are looking to find is a critical component for gaining insights from your data that drive specific actions. Data is not black and white; there is so much you can do with it. Accordingly, two people with the same data can come up with very different insights. This is because so much depends on the specific problem to be solved and the approach you take to solve it.