Machine Learning: Bridging the Gaps in IT Data Silos

In today's complex business world – where many organizations operate in silos, data is plentiful and it's challenging to get a big-picture view of the entire IT landscape – how can enterprises better manage, analyze and interpret tremendous amounts of data? The next big thing in ITOA – machine learning – is providing a viable solution. Machine learning studies how to design algorithms that can learn by observing data, discovering new insights in data, developing systems that can automatically adapt and customize themselves, and designing systems where it's too complicated and costly to implement all possible circumstances, such as search engines and self-driving cars. There's been a significant increase in machine learning applications in ITOA due, in large part, to the ongoing growth of machine learning theory, algorithms, and computational resources on demand. Many organizations are finding that machine learning allows them to better analyze large amounts of data, gain valuable insights, reduce incident investigation time, determine which alerts are correlated and what causes event storms – and even prevent incidents from happening in the first place.