What is prescriptive analytics?
Some say that it will change the way people look at the course of events, and some say that it can predict 100 years into the future and prevent traffic accidents to the Third World War. Are we entering into an era inspired by the "Person of Interest"? How can anyone make such subjective predictions? How can these predictions be accurate? There are so many questions from so many angles already that bombard the very concept of prescriptive analysis.
What is prescriptive analysis after all?
The prescriptive analysis is the practice of using available data and analytics to improve future decisions. It falls in the area of business analytics (for now), and it can improve the outcome of the results. Since people will get more data to evaluate their present situation and decision making circumstances, it will surely give them a better chance at creating favorable outcomes.
We have heard that some like to describe prescriptive analysis as something that happens after predictive analytics. Well, that is not entirely true. Prescriptive analytics is more of an umbrella term that includes myriads of analytical processes that can contribute to decision improvement. Descriptive analysis provides the users an insight into the past occurrences and gives them a way to forecast what might happen. On the other hand, prescriptive analytics allows the users to find the best possible outcome among a score of different choices. However, both of them can only work when all the parameters are known.
The truth about prescriptive analysis
A prescriptive analysis is a process-intensive task. The system needs to analyze all potential future decisions and their interactions to understand their individual as well as compound influences. After an extensive session of weighing the "pros and cons," one can conclude as to which given option is the most favorable among the lot.
Although the prescriptive analysis is being lauded as the Oracle of the 21st-century inventions, it is not foolproof. Anyone maintaining the database and forwarding the data to the systems for analysis is automatically restricting the options or possibilities for analysis. The perspective analysis is always subject to some form of distortion that can upset the predictive process. Aside from the conventional data limitations, there are unseen external forces, which can upend the process as well. The entire analysis and the subsequent results depend on how well the current decision model captures the influences of the decisions in the analysis.
How can developers use the bulk of data and prescriptive analysis?
Machine learning is no longer the only way to utilize and analyze available data load. Terabytes of data regularly flow into thousands of databases across the world. The cogent analysis of the same can help developers and corporations to develop better AI assistants, traffic accident avoidance plans, simulation programs, and streaming analytics to detect patterns in data in real-time. It surely makes prescriptive analysis the most probably future possibility for big data.
Right now, the database experts and administrators are looking towards using prescriptive analysis in two ways.
Inform decision logic with analytics: decision logic requires data to make a practical and balanced decision. The timeliness of data will determine how the decision logic will function. Now, the decision logic can be of an application or a person. In either case, the prescriptive analysis provides the much-required input to the subsequent analytics process. It can be simple aggregative analytics or a more complex predictive model.
Evolve decision logic: decision logic needs to evolve or improve to be always effective continuously. When DBAs or database managers do not update the data, the decision logic can become outdated. It will start accumulating flaws over time and lose its effectiveness. Measuring the effectiveness of decision logic and analyzing them allows the developers to work on current decision logic to make them better. It includes adjustment of decision logic to incorporate a new audience, or it may involve reviewing email conversion rates in the event of a new offer.
The complexity of the decision logic or the analysis process does not define the prescriptive analysis process. It instead involves data and analytics, which can contribute to better decision logic and better predictions for the future. More technologies are developing each day that can collect data more efficiently and accurately for analysis, as well as contribute actively to the improvement of decision-making processes.
Sujain Thomas is a data expert. She has been working with complex database architecture and big data analytics for almost eight years now. She has been helping many leading corporations optimize their customer service and product introductions as per big data analysis and predictive analysis.