DCN August 2017 - Page 13

centre of attention ‘The more data a business has, the smarter it can become.’ •  Taking remediation beyond error codes to assign problems and advise the appropriate team on next steps. •  Learning application behaviour and correlating it to the underlying infrastructure’s response. For example by setting, behaviour-based thresholds. •  Ranking the relative impact of each system issue, allowing teams to troubleshoot in order of most critical impact on the user experience. This technology is now finding its way down from the most cutting-edge enterprises in ways that put it within reach of mid- sized organisations, as well as through leading APM and cloud management platforms. Predictive analytics for BI requires a paradigm shift from a reactive mindset to a strategic one. This will mean a move from managing day-to-day operations to creating strategic initiatives, a step that will ultimately generate greater profit and revenue. Get used to hearing the term data science as a service analytics application, which are typically the results users interact with. However, the back-end analytics are just as important. Business analytics applications actually increase the importance of comprehensive monitoring and ITOA, meaning that both the front-end and back-end analytics are used coherently. Here’s what is ahead for both predictive analytics and BI. Make life easier Real time ITOA will make jobs easier for system admins and DBAs. In the next few years, data analysis and correlation will be required to happen in real time. This ‘real time ITOA’ means that Big Data and analytics will perform changes both supervised and unsupervised – ITOA will make predictions and fix issues before a problem gets out of hand, without the input from system admins and DBAs. Here are a few ways ITOA on the back-end can support analytics initiatives on the front-end: •  Predicting future IT system states and the impact of those states on BI application performance – such as when will your database be out of space? •  Using the application stack’s models, structures and patterns to pinpoint previously unknown root causes of overall system performance issues. Analytics is becoming increasingly complex, which has led organisations to look at how they can deliver it in the form of Data Science as a Service (DSaaS). DSaaS gives organisations the ability to utilise data analytics and predictive mining in order to provide business insights, without the need for data scientists or other skilled analysts. This brings ITOA to a new level ; more of a strategic discipline. The future of ITOA will involve the management of real-time infrastructures, with an environment that has a mind of its own. By harnessing the power of predictive analytics, BI will drive better performance across IT stacks, and benefit the business as a whole. August 2017 | 13