Unlocking the full potential of monitoring through ML integration, anomaly detection, and innovative scoring engines. Machine Learning has been making waves in various industries, but its adoption in the monitoring and observability space has been slower than expected. Many “ML” features remain gimmicky and do not provide actual real world value to users that encourages their further use.
As more and more organizations undergo digital transformation, IT automation is becoming more important and essential to implement. A report by Smartsheet found that more than 40% of those surveyed spend about a quarter of their time working on repetitive tasks, and almost 70% of them say that automation’s biggest opportunity would be that it significantly reduces the amount of time spent on these tasks.
Whoever owns Reliability should define its parameters. But who owns the Reliability of a Product? Engineering? Product Management? Or the Customer success team?
Last month, the Singapore bank DBS experienced a 10-hour outage of its digital services. Not only was it massively disruptive to customers, but it caused the bank’s stock to lose 1.4% of its value in a single day. And it’s not the first time DBS has had to deal with the fallout of an IT snafu; in November 2021, Singapore’s finance regulatory body imposed significant additional capital requirements on the bank after its digital banking services were disrupted for two days.
Endpoint management is critical for IT teams. In a SANS survey they found that 44% of IT teams manage anywhere from 5,000 to 500,000 devices. With so many devices, it can be challenging to keep track of the health and status of each individual endpoint. So how can IT teams ensure that organizational endpoints continually contribute to the company’s success? Endpoint lifecycle management is a valuable process that can enable your team to achieve this.
