Prescriptive Analytics Task
(Redirected from Complex Temporal Predictive Analytics)
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A Prescriptive Analytics Task is a predictive analytics task that is a prescriptive task.
- AKA: Complex Temporal Predictive Analytics.
- Context:
- It can be solved by Prescriptive Analytics System.
- Example(s):
- Predict a good route to my destination (take into many dynamic variables). Provide 'what-if' support.
- Predict a time-table that satisfies many constrains.
- …
- Counter-Example(s):
- See: Business Analytics, Business Rules, Prescription, Planning Task.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/prescriptive_analytics Retrieved:2015-10-23.
- Prescriptive analytics is the third and final phase of business analytics (BA) which includes descriptive, predictive and prescriptive analytics. [1] Referred to as the "final frontier of analytic capabilities," [2] Prescriptive analytics automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions. [3] The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics answers the questions what happened and why did it happen. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting - such as sales, marketing, operations, and finance - uses this type of post-mortem analysis. The next phase is predictive analytics. Predictive analytics answers the question what will happen. This is when historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. The final phase is prescriptive analytics, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities. All three phases of analytics can be performed through professional services or technology or a combination. In order to scale, prescriptive analytics technologies need to be adaptive to take into account the growing volume, velocity, and variety of data that most mission critical processes and their environments may produce. One criticism of prescriptive analytics is that its distinction from predictive analytics is ill-defined and therefore ill-conceived.
2012
- (Evans & Lindner, 2012) ⇒ James R. Evans, and Carl H. Lindner. (2012). “Business Analytics: The Next Frontier for Decision Sciences.” In: Decision Line, 43(2).
- QUOTE: … What is the risk of losing money in a new business venture? Prescriptive analytics uses optimization to identify the best alternatives to minimize or maximize some objective. Prescriptive analytics is used in many areas of business, including operations, marketing, and finance. ...