Would have, could have, should have and as we all know, we’re always wiser afterwards. This will probably never change. But algorithms and intelligent analytic methods now offer the possibility of optimising decision-making. This may not make you smarter before than after, but it does make you smarter than some of your competitors.
Manufacturing companies now operate in dynamic markets with enormous pressure from competition, regulation and customer demands with increasingly complex structures. Aspects that do not simplify decision-making. Predictive analytics have been offering the possibility of showing future scenarios for years. Prescriptive analytics go one step further and enable optimised decision-making.
Facts instead of mumbo-jumbo
Prescriptive analytics is a dynamic, data-based approach that uses mathematical algorithms as analytical methods. The goal of prescriptive analytics is to suggest the right decision options in order to achieve maximum benefits or minimise risks.
Structured and unstructured data from internal and external sources as well as complex algorithms form the basis for this. The methods used include artificial intelligence, statistics, probability calculation and simulations. The data can come from different sources such as the classic data warehouse. Prescriptive analytics also uses the unstructured data from NoSQL databases from the Big Data environment.
The biggest advantage of predictive analytics is that the data science process does not stop at evaluation or prediction, but includes action. This step from the processing of information to a clear application is fundamental to the success of data-based work – and distinguishes prescriptive analytics from the other three widely used types of analytics.
Gartner’s four levels of analytics maturity
In its analytics maturity model, Gartner shows the four types of analytics based on their use: from retrospection to prediction. Depending on the desired result, it can be quickly identified which analytics method would be most appropriate. Descriptive analytics deal with the core question of what happened in the past and try to understand the effects on the present.
Diagnostic analytics, on the other hand, move more in the direction of generating insights. Here, the focus is on the question of why something happened. The focus is accordingly on the reasons, effects and interactions.
The next stage is predictive analytics. This method looks into the future and delivers statements about the probability of future events based on data mining, machine learning and other statistical models.
The last stage is prescriptive analytics. Here, the focus is primarily on the aspect of optimisation and less on the pure provision of information – the complexity of the method is correspondingly high. The key question here is: How must action be taken so that a future event occurs – or not?
Predictive versus Prescriptive Analytics
Prescriptive analytics go one step further than predictive analytics: in addition to providing information, recommendations for action are generated in order to influence a development, prevent an event or react to an event in the best possible way. The basis for this is comprehensive data collection and sophisticated analytical models. Prescriptive analytics is usually based on the knowledge gained through predictive analytics, which is then run through with various known and possible parameters in order to show possible if/then scenarios.
The better the data collection, the more informed the analyses and thus the decisions. By using state-of-the-art software, you can take the first step towards the future today. You can read about the possibilities here.