While it is true that analytical modelling is calling for nonstop testing of big data, the equation isn’t that straightforward and holds certain potential challenges.
The need of the hour is active experimentation in the big-data zone to help in-progress the analytical model to make precise correlations. But since statistical models have their own risks, their astute application is going to be a must, especially as long as we want the results to be positive.
While a few groups are still hesitant, most full-size organizations have been able to hone their insight to realize that big data calls for incessant experimentation, and are all in support for the alteration. They also know, at the same time, that the practical scenario of the booming field of big data involves certain risks associated with statistical models, especially when their implementation is not flawless.
Statistical Modeling –Practicality and Risks:
Statistical models are simplified tools employed by data science to recognize and validate all major correlative aspects at work in a particular field. They can, however, make data scientists have a fake sense of validation at times.
And despite fitting the observational data quite rightly, various such models have been found to miss the real major causative factors in action. This is why predictive validity is often missing in the delusion of insight offered by such a model!
What May go Wrong?
Even though the application of a statistical model is practical in business, there is always a need to scrutinize the true, fundamental causative factors.
The lack of confidence may prove to be the biggest risk, particularly when you doubt the relevancy of the standard (past) correlations constituting your statistical model in the near future. And obviously, a predictive model of product demand and customer response in a particular zone in which you have low confidence in will never be able to pull in huge investments during a product launch!
What is the Scope?
Even though there are certain risks involved, statistical modelling can never be completely dead. To be able to detect causative factors more quickly and effectively, statistical modelling will need to be based on real-world experimentation. This innovative approach that employs a boundless series of real-world experiments will be highly helpful in making big data business models and economies more authentic and reliable.
So How’s Real-world Experimentation Going to Be Possible?
Exactly the way data scientists have developed advanced operational functions for ceaseless experimentation, big organizations look forward to encouraging their expert business executives to lead the charge in terms of running nonstop experiments and for better output. And to add to their convenience, the big data revolution has already offered in-database platforms for the proper execution of a model and economical yet high-output computing power to make real-world experimentation feasible everywhere, including scientific and business domains.
The basic idea is to prefer spending time, capital and other resources to conduct more low-risk experiments to putting extra effort into building the same models back and back again!
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