It seems like artificial intelligence (AI) is everywhere these days, so it’s no surprise that the newest wave of business intelligence (BI) is harnessing this technology. What can artificial intelligence in data analytics do, exactly? Well, this was the subject of a recent webinar sponsored by ThoughtSpot titled “AI for BI: Tapping Into the Potential of AI and Machine Learning for Business Intelligence.”
Let’s go over some of the key points to gain a better understanding of the capabilities of AI data analysis for modern businesses.
Empowering Non-Technical Users to Get Insights
Business intelligence is trending toward self-service analytics. In the past, non-technical users had to rely on centralized data teams to query company data. The primary challenge with this model was the delay between querying and receiving insights. When time is money, it’s beneficial to empower business users to get the data insights they need quickly so they can make timely, informed decisions.
As webinar speaker David Stodder—Senior Director of Research for BI at TDWI,—says, self-service is a priority because it “break [users] off from being completely dependent on IT and IT developers.” He adds that data democratization entails making analytics tools more personalized to users’ needs in search, analysis and visualization.
Here’s one example of self-service analytics in action: To use the Spot IQ AI Engine from ThoughtSpot, users don’t need technical knowledge about algorithms or data sets. With a click, anyone—from a marketer to an executive—can engage in AI analytics, receiving transparent insights in the form of understandable natural language narratives.
Augmented Intelligence: Humans and AI Working Together
Another key point in this webinar: Automation reduces manual effort, but does not replace human intelligence by any means. While AI algorithms do a lot of the “heavy lifting” in terms of finding data and suggesting trends, anomalies, etc., humans have the ultimate say in decision-making.
Machine-learning in data analysis also refines tools like SpotIQ based on human input. Something as simple as giving a “thumbs up” to a relevant insight or a “thumbs down” to an irrelevant one helps AI technology learn what to pull from data.
Possible Use Cases for AI in Data Analytics
This webinar breaks down types of problems addressed by AI and machine learning in data analytics:
Customer focused: Marketing challenges like segmentation, predicting churn, personalization, etc.
Person focused: Client and patient-centric challenges like predicting clinical support needs, managing patient risk, predicting retention, etc.
Operations focused: Equipment-based challenges like identifying machine problems ahead of time, optimizing maintenance, etc.
Risk and fraud focused: Identifying fraud and risk across data sources and content
This just goes to show that users across teams and departments can all benefit from access to AI tools in analytics.
Learn More About AI in BI
Artificial intelligence in data analytics has the potential to improve insight quality and speed, even when business users don’t have a specific query in mind.Watch demo