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Motivation

A data center for a major payments company had high equipment costs. There was a huge and rapidly growing machine data volume from thousands of feeds, leading to capital expenditure waste from over provisioning to cover anticipated peaks in usage. There were outages which went unnoticed until customers complained, which was hurting user satisfaction. The company was in a reactive mode to their situation.

Solution

A model was deployed to detect patterns and anomalies and take action before the user experience was negatively impacted. This model allowed the company to take all the data, put it together, and correlate events across those streams. This allowed them to provision only equipment that was really required, and monitor thousands of systems and social media feeds at data rates of over 25 terabytes per hour.

Benefits

  • Provision only what’s really required

  • Take action before merchants complain

  • Monitor thousands of systems and social media feeds

  • Estimated $20-$30m savings per year

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