The Human Role in Machine-Learning Analytics
The latest in business intelligence and data insights forges a partnership between human end users and machine learning artificial intelligence. This model aims to provide the best of both worlds: the speed and accuracy of algorithms, plus the careful oversight and reasoning of people.
The end results? Companies can now derive insights from deep within their data stores in seconds. And the system is always improving, meaning your company’s data insights will just keep getting better and more relevant over time. That’s the advantage machine learning and analytics from ThoughtSpot brings to businesses of all shapes and sizes.
When people first hear the phrase “machine learning” or even “artificial intelligence” applied to data analysis, they often assume that computers have completely taken over the process. But humans actually remain central to the model. After all, human employees are the end users looking for insights to drive decisions in the first place, right?
Business people can easily go ‘under the hood’ and see exactly what queries were generated, allowing users to remain in the loop on any decisions recommended by the platform.
It’s also simple for end users to provide feedback. Clicking “thumbs up” indicates a specific insight was useful. Selecting “thumbs down” tells the system that an insight was not as relevant as it could be. In other words, humans reinforce SpotIQ’s DataRank machine-learning algorithm with every interaction.
Using SpotIQ, humans can click to launch dozens of advanced algorithms that do the heavy lifting of analyzing cross-sectional data—fast. These algorithms are capable of executing thousands of queries across multiple data sources. The outcome is end users becoming aware of real-time anomalies and patterns, allowing them to swiftly correct problems, investigate discrepancies and make informed business decisions.
Humans can also schedule recurring insight discovery tasks so algorithms are querying data stores, even when end users are away from their desks. The system can also alert employees to new discoveries via mobile chat applications. Humans sleep; but machine-learning and analytics don’t have to.
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