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Power BI Machine Learning: Here is What You Need to Know About BI in the Age of AI
Most companies are sitting on a veritable goldmine these days, whether they know it or not. Stored data may not shine and sparkle like a treasure trove, but it’s immensely valuable in today’s competitive business landscape.
Organizations are aiming to monetize their data by converting from stored rows of information to actionable insights.
After all, it’s not doing anyone any good sitting buried in a server somewhere. There’s still plenty of opportunities to use data monetization to your competitive advantage, too, as only one in 12 companies is currently doing so to its fullest extent.
Business intelligence (BI) is whatever technology organizations use to turn structured data into insights that tangibly drive revenue and reduce wastefulness.
BI helps turn data related to past performance into something employees can use to guide present and future decision-making.
But BI itself is changing, especially given that companies are now able to leverage artificial intelligence (AI) to get even more from their data stores.
You may be wondering: What does BI look like in the age of AI? Let’s take a closer look at Power BI Machine Learning
Using BI Software to Ask Questions
One major component of BI has always been allowing employees to ask questions and get the answers they need to make an informed decision.
Here’s one small use case: A merchandiser for an e-commerce company would want to ask questions about SKU performance and inventory levels before placing a massive order.
Only once they understand how certain products performed by region, channel or time period can this employee make an informed purchasing decision.
Ordering too few products means customers will be disgruntled; too many and the retailer will eat the cost of stocking extra merchandise.
Organizations recently began to deploy self-service analytics because it enabled users to get insights faster—without having work through a centralized data team to get reports.
Asking questions of data became as simple as entering a search query in any online engine, and the wait time dwindled down to seconds or minutes.
Make no mistake, search-driven analytics tools still immensely useful and sought after for companies on the road to monetizing their data. But they’re no longer the entire picture when it comes to BI.
Power BI Machine Learning – Uncovering Answers Before You Ask the Question
Search analytics helps users uncover answers to their most crucial questions. But what about the rest of the insights lurking under the surface of stored data—the answers to questions people haven’t yet had a chance to ask?
Platforms like ThoughtSpot have taken business intelligence one step further, using built-in artificial intelligence to pull actionable insights out of billions of data points.
AI in BI alleviates the need for human analysts to manually sift through vast swaths of data on the search for potentially interesting insights.
Algorithms are capable of querying data on a huge scale, uncovering trends and anomalies in seconds.
Here’s what one BI research director has to say about the potential for AI in BI: “AI can broaden the reach of BI, making it easy for non-technical users to get more answers on larger and more varied data sources, including to questions they may not have thought to ask.”
AI in BI forges a partnership between machine logic and human decision-making—especially because of human feedback guides machine-learning algorithms toward making insights more relevant over time.
AI analytics tool can even push insights directly into workflows so employees can pounce on the opportunity to act as early as possible.
BI in the age of AI gives employees the insights they need to make smart decisions—in response to questions they’ve asked and those still hidden within data.
