What’s Ahead for Business Intelligence and Predictive Analytics

Featured article by Cameron Jones, Principal Product Manager, Blue Medora

Many analysts are quick to weigh in on analytics and business intelligence (BI). A Gartner report predicts that by 2018, more than half of large organizations globally will compete using advanced analytics and proprietary algorithms, especially as the firm sees analytics as the fastest growing segment of the BI market. Analytics simultaneously drives business transformation, becoming the core of business data today. But what will analytics look like in the future, and what impact will this have on BI?

The more data a business has, the smarter it can become. This new way of using intelligence to search for competitive advantage is leading businesses to collect more and more data. For many, the next logical step is to create a formal approach and develop IT Operations Analytics (ITOA) functions to deliver the insight they need.

But many analytics projects focus first and foremost on front-end ITOA – which are typically the results users interact with – and neglect back-end of the analytics application. However, the back-end analytics are just as important. Business analytics applications typically increase the importance of comprehensive monitoring and ITOA, which uses both the front-end and back-end analytics coherently. Here’s what is ahead for both predictive analytics and BI:

Jobs Will Become Easier for System Admins and DBAs

In the next few years, we will see data analysis and correlation increasingly happen in real time. This “real time ITOA” means that Big Data and analytics will perform both supervised and unsupervised changes – ITOA will make predictions and fix issues before they become bigger problems, 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:

1. 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.
2. Using the application stack’s models, structures and patterns to pinpoint previously unknown root causes of overall system performance issues.
3. Taking remediation beyond error codes to assign problems and advise the appropriate team on next steps.
4. Learning application behavior and correlating it to the underlying infrastructure’s response. For example by setting, behavior-based thresholds.
5. Ranking the relative impact of each system issue, allowing teams to troubleshoot in order of most critical impact on the user experience.

Predictive analytics for BI requires a shift from a reactive to a strategic mindset. This will mean a move from managing day-to-day operations to creating strategic initiatives, which will ultimately generate greater profit and revenue.

Organizations Turning to Data Science as a Service

Analytics is becoming increasingly complex, which has led organizations to look into how they could provide Data Science as a Service (DSaaS). DSaaS allows organizations to utilize both data analytics and predictive mining to provide business insights without needing a data scientist or other skilled analysts. This allows ITOA to be a more strategic discipline.

The future of ITOA will require the management of real-time infrastructures that have environments with a mind of their own. By harnessing the power of predictive analytics, BI will drive better performance across IT stacks and benefit the business as a whole.