Machine learning–driven analytics: Key to digital transformation
The use of analytics and AI is rapidly expanding, powering the personalized experiences contemporary consumers and business users demand. Taking the methodical approach can help organizations quickly and effectively realize the benefits of AI related to transforming traditional business intelligence and analytics.
We are in the throes of the digital economy, where both the volume and the sources of data are proliferating at an exponential rate—from consumer devices, appliances, cars, and industrial assets to the Internet of Things—and the list keeps growing.
Big data has simply become bigger than what human workers can handle on their own, particularly with global data stores. According to IDC, companies will be storing more than 100 trillion gigabytes of data by 2025—10 times the amount created in 2016. With all that data, organizations now realize they not only can discover more about consumers, patients, markets, machinery, and anything else that generates data, but they also can predict their behavior. Further, they can take actions that change outcomes for the better, whether reducing customer churn, finding cost efficiencies, or improving diagnosis accuracy.
Suffice it to say that data by itself does not hold much value unless enterprises can harness it to derive real-time, anywhere/anytime insights regardless of where that data resides—whether at the edge, in the cloud, or on premises. Enterprises that will survive and thrive in this digital economy are those that have a pervasive strategy in place across data, analytics, and AI—the three interconnected categories of technology, along with infrastructure, that are fueling digital transformation. Key to this transformation will be the use of autonomous analytics and machine learning, enabling enterprises to drive greater automation of tasks and derive insights at breakneck speed.
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