Tobias Zwingmann - AI-Powered Business Intelligence: Improving Forecasts and Decision Making with Machine Learning

by Tobias Zwingmann

Copyright 2022 Tobias Zwingmann. All rights reserved.

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Pop culture has had a huge impact on the way we view AI. If theres anything that sci-fi movies such as The Matrix and The Terminator have taught us, its that AI is out to get us and that AI has a human touch. Arnold Schwarzeneggers Terminator showed us what an AI-turned-human could look like. From The Matrix, we learned that we might not even recognize that our lives are all controlled by a superior artificial intelligence.

While I think that both of these takeaways are problematic, I do believe they convey some truth in the context of business intelligence (BI). AI is coming to get us and it might have a human touch. But before we get into the details, lets first clarify some key terms. What is BI, exactly? BI is difficult to define because it depends on who you ask, when, and in what context.

The definition of BI for the context of this book, however, is fairly straightforward. I define BI from a systemic point of view: business intelligence is a system or software that enables business users and analysts to look at data from multiple sources within an organization with the goal of making better-informed decisions. So when I refer to BI, this term is closely related to BI systems such as Microsoft Power BI, Tableau, and Looker, and the underlying infrastructures that run these systems.

Lets take a quick look at AI, a term that we will explore in much broader detail in the course of this book. First, AI fuels all kinds of business processes today. More than 50% of all companies worldwide are on an AI journey, with at least some small use cases or early proof-of-concept (PoC) projects. The most popular AI use cases include the following:

• Search engines

• Churn prediction

• Demand forecasting

• Document processing

• Predictive maintenance

• Customer segmentation and personalization

• Quality control and process monitoring in manufacturing

If you look at the breadth of AI use cases, it would be naive to think that the impact of AI initiatives would skip the domain of BI. Its inevitable and only a matter of time that a proportion of traditional BI tasks such as planning and forecasting will be outperformed by AI. If you try to beat AI at finding patterns in data, good luck! Youll probably lose.

Second, when we think about AI, we often think about technology replacing our jobs. However, how realistic is this concern, really? Ask yourself, when do you expect an automated end-to-end AI system or robot to be good enough to take over 100% of your daily job? Five more years? Ten more years? Never? On the other hand, when do you think a colleague sitting next to you could use AI to outperform you on important tasks? Next year? Next month? Is that person already in the company? Its important to realize that AI itself is not replacing our jobs. Rather, people who know how to harness AI are entering organizations and changing the status quo. This is where data scientists come into the picture.

Shortly after the term

All of a sudden, traditional data professionals such as business analysts and BI professionals found themselves standing on the sidelines, while new data scientists were brought in to work on exciting AI use cases. This led to a disconnect between the two sides.