This post is from the Numerify blog and has not been updated since the original publish date.
Proactive Analytics for IT: Reactive Operational Metrics Just Aren't Enough
Many IT departments find themselves constantly putting out fires, reacting to problems on a day-to-day basis. However, tending to issues as they arise often swallows valuable time and resources. That's where proactive analytics come into play. By getting more holistic and predictive insights into your IT environments, you can anticipate future issues in people, process, and technology so that you can avoid blind spots and better serve your customers. Why Aren't Operational Metrics Enough? In today's data-driven landscape, operational metrics are an essential part of running all parts of the business. It's true that frequent measurement of and exploration into operational metrics can help IT organize themselves around meeting their KPIs, such as uptime, performance, number of open issues, average time to fix problems, and time between failures. However, relying solely on operational metrics isn't enough to keep your business running smoothly. According to a 2015 survey, "only 57 percent of critical IT issues are detected and addressed before they impact the business." The same survey also found that 78 percent of IT executives believe the best way to improve IT operations excellence is to "use better tools for measurement, analysis, and detection of issues"—tools such as predictive analytics. IT research and advisory firm Gartner predicts that by 2022, "40 percent of all large enterprises will combine big data and machine learning functionality to support and partially replace monitoring, service desk and automation processes and tasks." What Does It Mean to Be Proactive?
Being proactive means that your business takes charge of its own destiny. When executives have questions in the boardroom, being proactive means that you don't have to wait two weeks for the data analysts to crunch the numbers and get back to you. User-friendly and role-specific dashboards with the ability to drill down and slice and dice data can help even non-technical users get the answers they need fast. Another advantage of proactive analytics is that you can prioritize the issues that are truly most important to your business. When you're generating reactive reports based on historical data, the IT department chooses which tasks to prioritize based on how technically interesting they are, or using the "first in, first out" philosophy. Proactive analytics helps you understand which incidents and issues have the most impact on your business, so that you can address them more strategically. Making your business truly proactive is easier said than done, of course. The proactive approach requires a comprehensive set of analytical capabilities that includes the following:
- Visibility: Understanding what's going on within your business, such as the workload of each of your team members, and identifying problems as they arise.
- Investigation: Looking into which problems are occurring in your business and why. For example, how do escalations and reassignments lead to process inefficiencies?
- Correlation: Finding the root causes across processes and discovering hidden relationships, such as how outages may be directly or indirectly tied to changes and configuration items.
Prediction: Prescribing actions and preventing problems, such as changes that are likely to cause issues in production, before they occur. How Does Predictive Analytics Help Make IT More Proactive? Predictive analytics can warn IT organizations about potential systemic failures—before they have the chance to wreak havoc on your operations. By gathering this information and taking action, you can shift from a reactive to a proactive organization. Machine learning can help you discover systemic risk factors, both individually and also how they interact with each other. You can then use these risk factors to predict and prevent incidents before they occur, and to investigate and eliminate the offending root causes. For example, a top financial services company used Machine Learning to identify risk factors across people, process, and technology that predicted when a change to an application is likely to cause expensive downtime. They found that Change Failure Rates were significantly higher when implemented by groups that infrequently made changes that require downtime. With this insight, they were able to select and train individuals within these groups on change implementation best practices. Predictive analytics can help you by finding correlations across the "golden triangle" of people, processes, and technology. Although many IT departments tend to focus on technological root causes, the actual problem may lie in any combination of these three elements. Final Thoughts Proactive analytics drive productivity and accountability across your operations, processes, service providers, teams, and projects. Strong proactive analytics solutions bring analytics to IT organizations of all sizes and industries. They align with the needs of businesses and help them improve their operations by delivering clear, precise insights. By using proactive analytics, businesses can help IT optimize costs, drive their innovation, and elevate their service experience. Finding hidden connections between events, routing support tickets to the right people, and more: machine learning and AI technologies are a crucial part of proactive analytics.