December 2012 marks the end of a time period in the Mesoamerican Long Count calendar. Some believe this is because the world will end. Unix time ends on Tuesday, 19th January 2038. So assuming we are all still here next February, should we believe that the world will end in 2038? Did the POSIX committee know something that we don’t? Only time will tell…
Whilst here at Gartner Towers we may lack the popular following of Nostradamus, we do try to anticipate how the industries and markets that we cover will change over time – Our current prophecies for Product Support can be found in “Predicts 2012: Product Support Market Will Weather the Cloud-Based Storm and Emerge Driving Value“.
Prediction can be very useful. Although often it isn’t. It can also be highly distracting. But providing it is based upon an appropriate evidence base and a statistically relevant analytical model constructed to take account of likely failure modes, inter-dependencies and historical performance data then it can even, dare one say it, be useful.
Predictive Support services are slowly beginning to come to market. The ability to predict and prevent system failures and problems will become paramount in the future as analytics excellence becomes the battleground for support providers. The relative accuracy of analytical models and their ability to narrow the predicted window of failure to something usable will differentiate support offerings. Predicting system failures 3 seconds in advance is practically useless. Predicting system failures 30 seconds in advance is marginally better. A predictive warning of 3 minutes plus opens up a whole heap of non-egg-boiling-related possibilities. Predicting that an issue will occur between 2pm and 4pm next Wednesday afternoon is incredibly useful.
The following graphic shows some of the many potential ingredients of the predictive support analytical pie…
Note: Some “ingredients” are only available from specialist suppliers and consequently not all analytical pies will taste the same. Ommiting some of the ingredients may or may not affect the culinary integrity of the pie and its ability to satisfy those with a hunger for prevention-based services 🙂
Analytical models will incorporate a wide variety of data feeds. The hunger and perceived need for more and more data upon which to perform statistical analysis will lead to high levels of over monitoring and over collection in the short term with a gradual scaling back of data requirements as providers learn what it is that they actually need to track in order to predict issues with the levels of accuracy that they actually need. Organizations that are overly focused on developing the perfect analytical model with 100% accurate predictions at the component level will be overtaken by providers willing to play the odds and offer commercial terms based around less detailed / granular models that deliver sufficiently accurate predictions to be able to initiate appropriate actions to avoid or mitigate service impacting events.
First generation predictive models won’t necessarily prevent incidents. This is particularly true in the software support arena where it is currently impractical to swap out a defective piece of code during run-time. However, predictive analytics still has a massive role to play in software support. One of the biggest problems facing providers when supporting complex software environments is the lack of evidence surrounding any particular failure or crash. When it all hangs, the data that you need to help troubleshoot the issue and prevent it happening again is typically lost. Prediction will enable the automatic initiation of low level logging immediately prior to system failures. This will capture valuable data that will speed the diagnosis and resolution phase as well as providing a basis upon which to develop preventive actions.
But prediction isn’t just about avoiding system outages. It has many many more uses than this. Some of these uses relate to the customer experience, others will help improve the operational performance of the support provider and enable it to make better commercial decisions. “Emerging Technology Analysis: Predictive Support Services” describes 9 use cases for predictive analytics within a support services context in detail.
The real question about prediction is not how you can achieve it. You can. But what you would do with those predictions if you could make them? The mathematicians, statisticians and analytical modellers will deal with the technicalities of creating meaningful and accurate predictions. Business leaders must then decide what it is that they intend to do with them thereafter!
Prediction is just another tool. And we should always remember that a fool with a tool is still a fool. But if we use the tool wisely then perhaps just maybe the future will be ours…
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Category: support-processes support-strategy technologies-underpinning-support
Tags: gartner-product-support-maturity-scale hardware-maintenance predictive processes-and-methodologies software-support support support-technologies support-value-chain trkfam
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