It seems everyone is talking about machine learning (ML) these days — and ML’s use in products and services we consume everyday continues to be increasingly ubiquitous. But for many enterprise organizations, the promise of embedding ML models across the business and scaling use cases remains elusive. So what about ML makes it difficult for enterprises to adopt at scale?
Too often, companies are finding out after the fact that customers have stopped using their product or service, without enough notice to have done anything about it. The term customer churn is used to describe the loss of existing customers. These are people or organizations that were using a company’s products and/or services and have decided not to use them anymore, in favor of a competitor. Tracking customer churn is a key business metric for most companies.
Our Head of Product Design and Creative Director, Tony Prysten, has worked in brand, design and advertising roles over the course of his career. Bringing his wealth of experience to Yellowfin, he now shapes the creative and UX experience of our product. Here he shares his thoughts on how design flexibility improves the dashboard experience.
We are now living in a truly hyper-connected environment where vast amounts of data are being transferred, gathered, and consumed daily. There are 3.9 billion internet users globally – a number that is still growing. Just think about the last time you were waiting for a Zoom conference call to start.
One of our API-first brethren in San Francisco recently shared with us how they built their synthetic testing system to monitor uptime and latency. It was a large undertaking involving a huge Redshift warehouse, Datadog and many man months of engineering effort. At the end of it, they could measure latency’s completeness, whether it was functionally correct and, when it moved out of bounds, alert CSM/engineering teams.
