What is Prescriptive Analysis; Steps to do this

As the digital infrastructure continued to grow and became smarter, the need for having a definitive model to store crucial data became imminent. Thus, the idea of data analytics came into perspective. Data analytics is not all about the collection of raw data from different nodes or sections of the organization, it is more focused on deriving fundamental data which can help in dedicated decision making.

Therefore, there are different types of analytics performed in the IT sector such as Data Analytics, Diagnostics analysis, and Prespective analysis. But this article is more focused on Prespective analysis, what is it, and various steps through which it can be performed.

What is Prescriptive Analysis?

Prespective analysis is all about generating recommendations to make decisions and come about verifiable statistics about a certain something that is based on the computerized findings. All these findings are derived from the algorithmic models that are either customized or generalized based on the various requirements of different organizations and scenarios.

Prespective analysis can work on both sides of the coin, such as it could be about making recommendations to the employees who are currently enrolled in a training session or the other way around. In other scenarios, it can be about making recommendations to the instructors on how they can improve the overall design of the course.

Prespective analysis is currently not that popular among the fields of learning and development mainly because of certain limits and complications prescribed by machine learning. Therefore, currently, the scope is not that bright but in the future, there is some hope.

Before diving into the working of the Prespective analysis, let’s first have a brief analysis of its design and how it is different from analytical designs;

Difference between predictive and Prespective analytics

Prespective analysis in general is the extension of the predictive analysis. If you let go of the Prespective analysis for some time and only deal with the forecast being produced by the predictive analysis then it would be enough to produce automated recommendations which are the direct working of Prespective analysis.

Prespective analysis in the afterglow of things requires some serious and complicated machine learning processes to achieve the work it does. If an organization settles on the predictive analysis then the chances are that the solutions presented by it may or may not be feasible or an even might or might not happen.

But with Prespective analysis, there is a margin of uncertainty there even if classic recommendations are presented by this analysis because human behavior is unpredictable. A degree of caution is needed for the analysis models that rely too much on human behavior as it is fairly unpredictable.

Working of the Prespective analytics

For the sake of generated automated recommendations and decisions, you will require a specific and unique algorithm model and clear direction of utilizing it too. A decision can't be made without taking into account the very thing it is directed towards or what problems need to be solved. Prespective analysis this way though begins with this kind of problem.

There can be various examples that can be referenced or applied to the concept of this specific analysis model. For example, suppose there is a new course regarding a few updates made over the years regarding the up-gradation of security standards among IT landscapes.

The IT instructor delivering the course thinks that not everyone is fit to undergo the course and then understand every key aspect until unless they carry a special skill within the cybersecurity designing or development. Prespective analysis can verily help the instructor to come about a deliberate solution regarding the problem being faced in here. An algorithm could be developed that can help in the scanning of all the willing candidates for taking up the new course.

And then by validating the required skillset for the course only those who fit with the requirements can be cleared out to take on the course. While those who don’t clear up based on the weak skillset can be referred to take on an extra course to upgrade their current skillset.

Quality of the data Vs. Decision rendered

The accuracy of a stat, recommendation, or future decision made by the Prespective analysis is only going to be as good as the quality of the data or information fed into it. It on the other hand also depends on the type or quality of the algorithm model used to analyze or cross-reference data.

Although it might feel feasible to use the same working or successful model for one particular company for every other similar industry out there yet there is a probability that it won't work. Because what works for a particular organization will require a bit of tailoring to be properly adaptive to the new threshold or set of challenges provided by the new organization.

Therefore, it is advised that ambient customization should be done to better acclimate a working algorithm model to another industry.

The strategy of Data governance

Using Prespective analysis without the proper validation from the organization, people or digital resources from where the data is taken goes against ethics. Generation of stats, decisions, and recommendations using the specified algorithm models surround some questions regarding the privacy or fairness of this ordeal. Did the data retrieved from various endpoints come with the consent of the people, devices, or corporates whose data it is? Was some kind of authorization provided and most importantly who has access to this data and at what levels?

If the data that is collected doesn’t depict the original intent or quality which was intended then the stats, decisions, or recommendations unveiled would also be faulty and less accurate. Therefore, it is recommended that a data governance policy be taken into consideration for validating the algorithm models being used for interpretation of data that was provided by consent from the collectors. 

The job roles and salary can be different for different modes of analysis, in the case of Prespective analysis job roles could be working closely with data collection and engineering unit and having a keen sense of programming for developing accurate algorithm models. The average salary of these professionals can range from $60000-120000 depending on the job role.

The data analysis training is in session and can prove to be extremely effective for young personnel looking to improve their skillfulness for working as professional data analysts and boosting their career dramatically.