Workforces across the world have changed rapidly over the past couple of years thanks, in large part, to two major trends. The first is the rapid digitization and transformation of service offerings during the pandemic. As our working lives shifted from office-based to online-based, the services that enable employees to do their jobs had to follow the same pattern. And with working practices predicted to remain a hybrid mix of office and remote-based, these changes are here to stay.

Qlik enhances analytics exploration in Snowflake by launching Direct Query, a new capability that allows Qlik Sense applications and dashboards to query Snowflake directly using SQL pushdown.

I recently sat down with CFODive to discuss the importance of modern financial analytics in transforming the way financial leaders and their organizations operate – a topic that is only becoming increasingly prominent. Business strategies have had to rapidly adjust to address market volatility, consumer trends, and unpredictable world events. These dynamics have forced finance teams to rethink how they are using data and analytics and take a more modern approach.

Finance has been at the forefront of enterprise analytics for decades. Over the years, these analytics have evolved from reactive, descriptive analytics related to financial performance, treasury holdings, and inventory management to predictive and prescriptive analytics for risk, credit, and financial business modeling.

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.