If you keep up with IT trends, you already know that demand for and interest in data science has grown dramatically. With more and more applications for data and Big Data, predictive and prescriptive analytics remain among the most sought-after processes in the market.
However, there is still a lot of confusion about the differences between predictive and prescriptive analytics, and even more questions about how to apply them in IT.
That is why we put together the answers in this article, so you can clear up all your questions about predictive and prescriptive analytics. Read on!
What is the difference between predictive and prescriptive analytics?
Stemming from the same broad scientific field — data science and advanced analytics — predictive and prescriptive analytics even have similar names. Their analytical goals, however, are quite different.
Predictive analytics, as the name suggests, aims to forecast or build predictive models of future events. This type of analysis relies on current and historical data patterns to determine whether those patterns are likely to recur.
Prescriptive analytics, on the other hand, is a process that analyzes data and offers recommendations on how to optimize business practices and achieve the predicted outcomes.
In a sense, prescriptive analytics can be seen as an extension of predictive analytics. While predictive analytics interprets data to forecast what could happen, prescriptive analytics reinterprets the data and possible future scenarios to recommend the best course of action.
Applications of predictive and prescriptive analytics in IT
The applications of predictive and prescriptive analytics are virtually limitless: they can improve operational efficiency in manufacturing, enhance retail products or even help investors allocate their resources more effectively.
In IT, a very common application of predictive and prescriptive analytics is ITOps, or IT operations. Some examples:
Identifying the root cause of application issues
Predictive and prescriptive analytics make it easier to pinpoint the real performance issues in applications, so IT teams can quickly resolve the root cause without wasting time hunting for the problem.
Monitoring application stability
Instead of using several other tools to monitor the stability and health of a single application, continuously feeding data into predictive and prescriptive analytics can serve as a more practical and effective form of monitoring.
Predicting application failures before they happen
The key to predicting application failures is to use predictive and prescriptive analytics to build a predictive model based on historical data about past failures. The model learns failure patterns and watches for similar occurrences, predicting future failures before they happen.
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