If you’re one of the majority of Americans who have shopped online, you’re already familiar with retailers’ tendencies to make buying suggestions for you based on your past purchases and on-site behavior. Oh, you’re buying a tent? You might also like: this air mattress, this camping grill and this sleeping bag. And if the suggestions are spot on, you’re probably thankful that the company saved you a couple extra clicks.
This is just one example of artificial intelligence and machine learning using data to identify patterns and relationships, then offer intelligent suggestions based on these findings.
Unsurprisingly, forward-thinking businesses across every industry are harnessing AI and ML in an attempt to derive maximum value from their stored data. Here’s more on that marriage between machine learning and business intelligence today.
What Can Machine Learning Do for Today’s Enterprises?
Artificial intelligence (AI) used to be rules-based, meaning someone had to code AI to respond certain ways in certain situations. As you can imagine, that’d be a hefty — if not impossible — task to undertake in the vast realm of business intelligence (BI).
But machine learning (ML) operates using examples, rather than needing hard-and-fast rules. It “learns” as it goes based on the relevancy of the data insights it uncovers. The more companies use ML, the better the system gets at understanding what constitutes a relevant insight for a user or a team.
So, no longer do data scientists have to manually dig through data until they stumble upon a trend, outlier or causal relationship between data sets that might be useful. But they also no longer have to code every rule their BI tools will follow in seeking out insights. The machine can use human feedback to keep refining what it pulls from data.
Here’s just one example: A BI system equipped with AI and machine learning analytics, like ThoughtSpot’s SpotIQ engine, is able to detect anomalies across billions of rows of data. Over time, it becomes even more knowledgeable in what constitutes a deviation from baseline performance.
In other words, the ML component of data analytics gets better and better at identifying and delivering outliers because it continues to refine its understanding of what’s normal/ideal performance. Human business users help the AI engine learn by giving its insights thumbs up or thumbs down based on relevancy. SpotIQ takes this feedback and incorporates it into its operations, another example to guide it on anomaly detection.
The benefit? If an anomaly occurs — a production dip on the manufacturing line, a spike in hospital patient infection rates, an interruption in retail sales, a suddenly inflated customer churn rate— AI and ML can quickly identify this in the data. Then the system will push this information to employees in clear natural language. This primes human decision makers to act, notifying the appropriate stakeholders or coming up with a plan to address these performance blips.
Anomaly detection is one aspect of machine learning in BI today, but algorithms can also pull longer-term trends, overlooked relationships lurking within data, and more.
Machine Learning Is for Everyone — Not Just Data Scientists
The behind-the-scene workings of ML and AI are complex, to be sure. But you don’t need to be a data scientist to use this technology today, nor to understand the insights generated. Modern BI prioritizes a straightforward user experience and insight comprehension for non-technical users.
The challenges enterprises face as they embrace machine learning and advanced business intelligence are transparency — in order to trust insights, they must understand which data source they derived from and what the data is telling them. Companies will only reap the benefits of deploying BI with ML if employees actually use the insights for good. As ZDNet writes, salespeople could still “stubbornly pursue leads deemed as less than promising by predictive scores” if there’s a lack of trust and transparency surrounding this technology.
The marriage between business intelligence and machine learning can be very fruitful for enterprises looking to turn previously hidden insights into savvy business decisions — without the need for data scientists to manually train AI how to operate in every case.