Think Ahead: Boost Your BI with AI
By: Therese Moriarty
Tags: Machine Learning, AI, Eyeful Trends, Artificial Intelligence, Business Intelligence

An analyst’s primary responsibility is to observe patterns and locate opportunities. Yet even experts can overlook trends or misinterpret results, particularly if they have to sift through a lot of information. Consequently, more businesses are enhancing their data analysis with ML. In addition to detecting anomalies, ML models can detect meaningful event correlations — some of which businesses can exploit for big wins. For instance, a brand’s emails could have the strongest open rates when they go out on Wednesday mornings and their subject lines contain emojis. This detail is useful to marketers because it can help them strategically plan their campaigns. Nonetheless, it could’ve been missed with ordinary data analysis. ML can return sharper insights in less time, and it’s not as likely to exhibit bias as a human analyst.

4: NATURAL LANGUAGE PROCESSING

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Google Analytics has a built-in AI function that can correlate events and auto-generate insights. Enterprise BI tools like Yellowfin and ThoughtSpot use similar technology to uncover any hidden wisdom in their customers’ data. Interestingly, all of these products harness Natural Language Processing (NLP) to communicate their findings and make data conversational. They can display insights in plain English, as in “25.33% of your site traffic is from the Referral channel.” Additionally, some of these NLP programs let users submit questions and receive answers.

A demo of how ThoughtSpot implements NLP. A user types in a specific question and gets an answer.

NLP can transform a dashboard into a virtual analyst; it lowers the barrier of entry to BI so anyone can stay in the know about their business. Users don’t have to deal with spreadsheets, charts, or SQL queries to get the information they need. Instead, they can interact with software that analyzes the data for them.

5: PREDICTIVE ANALYTICS

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AI can illuminate the past and present, but it can also offer a vision of things to come. Predictive analytics leverages ML to find regular patterns in data and carry them into the future. These projections are common in financial and weather forecasting, and they’re increasingly relevant to BI. Case in point, any retailer who can anticipate product demand will promote higher rates of customer satisfaction. In other words, they’ll have enough of an item in stock when consumers want it the most. Predictive capability comes with a competitive advantage, and more businesses can acquire it via platforms like IBM Watson or DataRobot. The popular BI tool Tableau includes a forecasting feature too.

IT’S NEVER TOO EARLY TO START

AI products will get even better at delivering warnings, correlations, and predictions. While the best is yet to come, you should embrace this technology now. Like many businesses, your competition may depend on humans to conduct their entire analytics operation. You immediately stand out from them once you begin automating your BI. The benefit of being an early adopter is that you’re already proficient by the time most people are just starting out. Prepare for the next wave of innovation, otherwise it’ll sweep you away rather than lift you up.

Always remember that AI isn’t perfect, it’s only as reliable as the data it’s based on. Back in 2018, Waze directed a user to drive his car into a lake and he complied. He assumed the app was correct, despite common sense and his obvious surroundings. In this case, the GPS navigation didn’t give the right instructions because it didn’t have the right information. Moral of the story: never follow AI blindly. The results from your ML models should enhance your judgment, not replace it altogether.

Similarly, AI isn’t a substitute for data analysts or data scientists. We still need specialists to oversee and validate research, except now they can do this work faster and more effectively with AI.