IT Operational Analytics - The Future is Now
By Guy Warren, CEO, ITRS Group
ITOA is a segment where there has been significant innovation and investment in the last few years. The overlap with big data and analytics means that companies and their products come at the problem from a range of different angles.
Some of the leading big data ITOA products focus on collecting data, indexing and storing it, thereby making it searchable. By adding some basic analytics capabilities and visualisation, these tools allow users to search through their ITOA data, do basic analytics and then graph or chart the data to show trends or insights. Because the output is to the screen and is then understood by a person, the tools are usually not real-time, and lag behind the IT estate generating the data by anything from a few seconds to many minutes.
Other companies come at the problem from an advanced analytics perspective, often analysing streaming data in memory to detect patterns, behaviour and trends or anomalies. These tools are often closer to real-time, but are constrained in the analytics they can do because they rarely have storage to disk, so long running or long time span queries are not possible. Also, unless they are based on distributed computing techniques, they are limited to the amount of data they can handle.
In order to achieve the goals of the 4 stages of ITOA as described by Gartner in their definition of big ITOA, these two techniques will need to come together. ITOA products need to be able to accept large amounts of streaming data, to index it using semi-structured techniques to make it easily searchable and also to analyse it using advanced mathematical techniques. They need to persist the data so that it can be either reviewed later, or incorporated into queries which will exceed the memory of a computer. They will need to provide very fast mathematical analysis of data, especially temporal analysis. And they will need to be able to do this in real-time, and therefore in a scalable, clustered computing paradigm. As Gartner says in stages 3 and 4 of their 4 stage model, the analytics will need to be both pattern and anomaly detection focused, and causal analyse to help people to spot issues quickly and guide them to the most likely cause.
In this 'always on', real-time world that IT now needs to operate in, the output of these insights are increasingly going to be fed back into the IT estate they are analysing to take action, or to the support teams who manage these IT estates in real-time, rather than to a screen for a big data analyst to review and understand.
The future is now.
About Guy Warren
Guy Warren is the CEO at ITRS. Guy Warren brings more than 25 years' experience in financial services and technology businesses. Guy had previously been a customer of ITRS, using Geneos to improve the availability of the real-time systems when he was COO of FTSE Group. He has also managed a financial services product business as the EVP and General Manager of Misys Banking, a leader in the core banking solutions market. Guy has a strong background in large blue chip organisations and more recently working with private equity backed companies. Earlier in his career, he was the CEO of Logica UK, a large professional services company.