Big Data Enhances Predictive Analytics by Eliminating its Limitationsby Syntelli S. Marketing Earlier predictive analytics foretold about future events based on the comparison of past patterns or scenarios. This was done by deductive reasoning to provide insights into the future and it works well with the highly structured data like in the case of transactional data. But things changed with unending streams of data generated from sensor-aided machines or instruments or from real-time transactions or in case of stockpiles of data collected from social media or mobile messaging. In all these cases, traditional analytics tools were unable to make predictions because data volumes were too high for the storage and processing capabilities of available hardware. In addition to this, even a variety of non-transactional data that was usually unstructured do not fit into the RDBMS schemas framework.
For a long time predictive analytics remain the exclusive domain of secluded statisticians and data scientists who had no real connection with the everyday business world but with the advent of Big Data, predictive analytics has been considerably changed. Businesses now carry out in-depth analysis to derive value from their huge data sets and to successfully conduct analytics, businesses need appropriate tools, technologies as well as infrastructure to capture, store and retrieve these terabytes of data in high speed that is mostly unstructured. Open source technologies of the big data like Hadoop or the cloud platform allows for fast capture, storage, and retrieval of a variety of data types. Earlier businesses used to discard their vast troves of non-transactional data, as they don't know what to do with it.
However, with the advent of big data businesses are able to include all non-transactional data like multimedia files, social data, and emails into predictive analytics. In data analytics where future forecasts or pattern modeling is involved, the more volume of sample data is beneficial as they help to derive better insights. Thus, big data comes as a source of immense value to the world of predictive analytics. The large volumes of semi or unstructured data that was earlier of no use and was left out of the ambit of predictive analytics have now become mainstream data for future forecasts or insights. With the evolution of big data, businesses are heavily relying upon predictive analytics to develop better customer engagements, increase operational efficiency and optimize business processes.
There is a significant difference between traditional predictive analytics and big data enabled predictive analytics because in this inductive reasoning is employed resulting in data discovery. In big data enabled predictive analytics only highly sophisticated quantitative tools like machine learning, neural networks, computational mathematics or artificial intelligence are used to explore patterns in analyzed data. The smart programming algorithms are allowing ordinary line managers to conduct real-time analytics from their desktops or mobile devices to make critical business decisions.
Created on Dec 25th 2018 03:19. Viewed 382 times.
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