5 Predictions About the Future of Predictive Analytics
As predictive analytics develop and their level of precision rises, this already useful tool can save companies even more time and money, and should be an integral part of any IT department’s toolset.
Predictive analytics is becoming one of the most powerful tools at a company’s disposal. This software transforms raw data into concrete “intelligence” on which a company can base its decisions, then uses that information to extrapolate what might happen in the future and when problems might occur.
Looking ahead, this powerful data-crunching tool will only continue to expand its influence in the world of business. Here are five predictions about what the future of predictive analytics will look like.
Predictive analytics shouldn’t be seen as the end goal for your organization, but as a single step in a longer journey. Most companies use descriptive analytics to help them process immediate, incoming data. Predictive analytics can be seen as the next logical step, allowing you to apply that data to make predictions about when something might go wrong and why. Of course, the job isn’t over once you’ve made this forecast — by building the predictive data into concrete solutions for future problems, your predictive analytics become prescriptive.
Predictive analytics is only going to become more essential to the work of everyone in IT as time goes on. Crucial time, money, and other resources are wasted trying to solve the problems that you fail to predict, and your reputation for high-quality, consistent service also suffers from these failures. When everyone in the IT department is prepared for the challenges ahead, consistently optimized service becomes the status quo.
Any form of analytics requires highly complex mathematical equations that you need years of training in order to understand, design, and implement. IT I&O has become so complex that this kind of training is no longer a realistic prospect for most IT departments — in response, analytics tools have become more and more automated. Anyone in the IT department should be able to input the necessary parameters and let the automated tools do the rest.
Along these lines, all tools and programs will become smarter and require less input from the people running them. Predictive capabilities will be built directly into analytics tools so that they can perform their own predictive analytics. For example, software might be able to read all the available data it collects, then determine the best prediction interval on its own.
These tools could also assist capacity planners, who look at historical growth rates and plot those rates going forward. Software could calculate its own historic growth rate and then apply that going forward, saving you from having to do that work yourself.
Big data has always been a crucial component of predictive analytics, and while algorithms and analytics are beginning to steal the spotlight, data collection remains a crucial component of any IT strategy. As the IT world moves away from time series and performance data and towards sources of information like unstructured log file data, big data and the ability to process it will become more and more important. A truly successful IT optimization effort should recognize the value of big data and work to integrate it into all data analytics.
If predictive analytics isn’t already a central part of your IT strategy, it should be integrated as soon as possible. TeamQuest provides the knowledge and services to make your analytics smarter, faster, and safer.
The TeamQuest Predictor can not only help you determine the configurations that best ensure continued IT health, but also protect against risk by predicting what problems might occur and when. In short, automated predictive analytics from TeamQuest is the strongest tool your company could have in its IT arsenal.
(Main image credit: Philips Communications/flickr)