Big Data was mentioned thousands of times throughout our blog; however, we cannot over-stress the importance of it in the context of new-industrial era. As we mentioned early the big data does not only imply the quantitative point of it. In order to get full insight out of big data one must leave the manual operation and execute the operation with machinery power aka machine learning. However, the capabilities of ML are limited in terms of ever-growing data. Hence, automation of machine learning where analyzed data and the analysing agents are constantly getting closer. That is, discourse where data is analyzed is automated in order to get full out of the capabilities of the 21st century paradigm.
What is Automation Exactly?
Exact definition is deceptive when it comes to automated analytics for there are a few other implementations of automation where exact sense of it does not hold. That is, having a credit card from your bank may seem like automated analytics as the bank collects data and gets the insight from it to decide whether to give a credit card or not. Although, this procedure is not done by human agents but by computer programs, we cannot call this as automated analytics since the procedure is a mere automated I/O practice where sought inputs (credit score etc.) are looking for single output (credit card). The automated analytics is much more complex than that since the procedure itself derives from the machine learning descendants of predictive analytics, descriptive analytics, and prescriptive analytics.
Hence, the full automation term becomes logical as the automation progress becomes the subject of itself. Although, this may seem a little complicated at first, both the idea behind it and the output of it are simple. The manual workforce needed for operating analytical procedure is both complex and open for errors; however, automating it via machine learning procedures simplifies the analytical insights. Thus, full automation takes the seat and controls everything needed to analyze the data so what is left to human agent is only progress reporting. In fact, procedure forms the descriptive statistics, predictive analysis, and prescriptive reporting by itself; thus, leaving only commenting on it by human agents.
The necessity arose from full automation in analysis area is intuitive at first sight as data gets bigger (LINK), handling it gets harder. Thus, what is complimentary becomes essential as the current state of the big data needs not only automation via machine learning procedures but also full automation via deeper learning procedures. Full automation becomes essential part of the analysis progress since the analyses made without handling big data are obsolete. Hence, data analyses made through automation -full automation, are new requirement of 21st century paradigm.
How Enhencer Provides Full Automated Analytics
When consumers want real-time responses for their needs, the automated analytics became the necessity for insights. Personalized and tailored models for every applicant is a must currently, nonetheless, the necessary actions and decisions cannot be made with human agents deciding and acting according to every other need of applicants. Thus, Enhencer’s ability to sense the need of applicant hence starting the analysis procedure is a real life-saver. Therefore, descriptive, predictive, and prescriptive analysis made through fully automated systems are the backbones of the structural analysis made in full efficiency. Consequently, way Enhencer presents the full automation is in line with the needs of the current prerequisites of the big data discourse, leaving only remarking on what is brought by the program.