| |OCTOBER 20249CIOReviewto understand more and wrap their heads around the blasts of insights that challenged not the status quo but gave them a whole new advantage against competitors in taking the business not just to the next level but years ahead.But no one could have foreseen what was next. It wasn't only the top senior leadership seeking more; it was also the middle managers, the analysts, and the people in the field having constant contact with the customer. Everyone wanted to be part of it, understand the data, and understand how it could improve, change, and evolve faster than ever before. This revolutionary new era wasn't just growing; it was exploding faster than the talent was available, and tools to access the data were hardly keeping up with it.That's how more levels and layers of needs provoked a split for the good of the data world into at least three different types of approaches. Business intelligence is the core of IT, with software engineers, programmers, and all sorts of data-educated developers taking care of the wellness and harmony of the systems. Business analytics is the front and center of business needs, composed mainly of analysts of all sorts who know how to pull, modify, and transform the data with simple methods, and data science, the new buzz-worthy, the shiny golden boy of any organization, pushing aside the other two, by taking a special seat at the table, where the geniuses with a talent for it answer the impossible questions and respond accurately to the challenges, with educated statisticians and programmers that could outperform the other two groups but possess unique and complex knowledge and capabilities hard to find when working with the data.Since then, data scientists have done a great job of making themselves valuable and setting a new standard for what business analytics means. Capable of resolving inaccurately established challenges of senior management into tangible, approachable, and easily digested insights that can be implemented and directly impact the revenue, traffic, and bottom line of any company. Understanding what generates engagement with endless data points and what creates loyalty has always been the craving, always changing question that drives the need for more data, and data scientists live for it.The story doesn't end there. The pandemic challenged businesses in a new way, not just to thrive but to survive. Pushing all limits and known ways even further away and taking a leap forward that has shaken things up for good. While Artificial Intelligence (AI) and Machine Learning (ML) have always existed and been done, they have become buzzwords. Many applications have been created that caused shock and were widely covered by all sorts of media, especially ChatGPT, which created a sort of renewed fear in older generations because of the sudden and almost instant adaption by GenZ and almost null understanding by everyone else.For everyone like me who has come up and developed themselves in the analytical world, AI and ML are only coined terms of methods and algorithms that have always existed, theorized, and been implemented. Just terms to express, as an umbrella, a lot of studied and experienced knowledge that technology has just made simple to adapt, and it seems that the buzz has everyone making investments that might not really impact their business, but only because of the fear of missing out again. Maybe we failed at coining a term early in the game or missed creating a disruptive, flashy application such as ChatGPT to promote our work. Either way, we are here for it; we breathe data and models and resolve challenging questions while keeping ourselves as valuable as we can in the organizational world.
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