Data science, along with the Artificial Intelligence (AI) and Machine Learning (ML) fields, is often seen as the new direction in which we ought to move our analysis of all the bits and bytes that we collect and store in our databases. There is so much hype now about those technologies, and managers are buying in.
I’m not sure I agree. I do think that AI and ML will increasingly be used, but they’re just a part of what you use to analyze data. Buck Woody has a good post about the way in which we might examine our technology stacks used for BI work.
We have a lot of reporting technologies to enable us to make better decisions, and there is a space for all of them. Many people like Excel, some use tools like Power BI and Tableau, still others prefer to get insight boiled down to a single number that influences them to move one way or the other.
There is a lot being written about AI and ML technologies and certainly many organizations experimenting with them. Data Ccience covers these areas and more, asking our data not just what it says, but potentially what this might mean in the future.
However, this doesn’t replace traditional BI and reporting. As Buck notes, these are tools and you should use the ones that work for your organization. Learn about them, experiment, understand the impact they have on your audience, and choose the best tools for the job.
I’m sure this area will continue to evolve, and we’ll get new tools and techniques to help organizations make better decisions. Whether this will actually improve forecasting is likely up to the skills of both the technical and business people.