The Future of Metrics for I&O
As both technology and standards continue to rise, the methods we use to measure efficiency in IT infrastructure and operations (I&O) will rise with them.
There’s a constant stream of news covering the rapid, even exponential advance of technology — big data analytics, deep learning AI, solid state memory drives, etc. We’ve heard how these products and approaches will revolutionize I&O efficiency (which they undoubtedly will), but can IT professionals be depended upon to reliably measure this change?
With the near incomprehensible complexity of IT systems of today, it’s hard to imagine that IT professionals will be able to manually compute their efficiencies with bespoke algorithms as in days past. Even if they had the ludicrous amount of time such a process would require, there would be no way of being sure that their results were error-free.
Luckily, manual reporting is already being relegated to the past: the methods we use to measure I&O have evolved, and business leaders will soon set clear standards for their performance. Here’s a look at three metrics capabilities we expect to take root in the near future.
Metrics will soon be increasingly attuned to the customer experience. The ways in which customers interact with brands has grown quite complex. People engage via mobile devices, desktop, social media, apps, in-store — in a word, multi-channel — but companies have had a difficult time evaluating the performance of end-to-end experience.
The challenge is double: for one, each channel is both a part of the aggregate customer journey and a standalone silo, which makes evaluation difficult. The other factor is that the metrics used for legacy systems differ greatly from those surrounding digital business. How can companies incorporate both systems seamlessly and condense that information into a readable format?
Multi-modal metrics use advanced algorithms to quantify the success of the customer experience and generate data that is both comprehensible and actionable — at best, specific solutions can be recommended that maximize performance while minimizing cost. This is multi-channel marketing in reverse, where the evaluation of the experience is just as seamless as the journey itself.
IT professionals are also having an increasingly tough time with exception management. Manually detecting every potential problem has become too cumbersome — moreover, if a problem has already been encountered and a solution found, there should be no need to re-open the issue.
Autonomous metrics offer rules-based, business-level exception management. The idea behind analytics and dashboards is that humans shouldn’t have to examine every silo and meter when problems arise — that’s too much work. Rather, IT professionals will be able to set automated thresholds and define operating solutions for when they are exceeded. These metrics won’t just notify you that a problem has occurred — they will inform you how the issue was already resolved.
Answers on Demand
This kind of metric closely coincides with autonomous metrics, but takes the logic one step further. Where autonomous metrics display information about ongoing exception resolution, an “answers on demand” feature would use predictive analytics to not only identify potential problems and exceptions ahead of time, but actually provide useful solutions based on extrapolative analytics.
IT professionals should be able to rely on advanced algorithms for detailed information about system performance and pitfalls now, along with several attractive pathways with which to resolve the issue in the future.
Even today, business and IT leaders are using business value dashboards and analytics to digest massive swaths of data and uncover the best courses of action. Plying more advanced metrics, these leaders will be able to deeply plumb even the most complex of systems and plan efficiently, well in advance of a problem.
(Main image credit: r2hox/flickr)