Big Data Vs Data Analytics: How Are They Different?
Data is all around. Truth be told, the measure of computerized information that exists is developing at a quick rate, multiplying at regular intervals, and changing the way we live. Organizations across various industries in the world are employing the philosophies of big data and data analytics in order to gain more in-depth insights into […]
Data is all around. Truth be told, the measure of computerized information that exists is developing at a quick rate, multiplying at regular intervals, and changing the way we live. Organizations across various industries in the world are employing the philosophies of big data and data analytics in order to gain more in-depth insights into their business and step-up their processes. However, it is not rare for many executives to wonder if big data analytics is same as data analytics. What you need to understand is that the two may be related but there are critical differences between them. To get to the definition, big data is a concept in software engineering that is used when there is the availability of large sets of machines generated data, which in most of the cases is unstructured and not easy to use with traditional concepts. Data analytics, on the other hand, is more concerned with analyzing data, which could be structured or unstructured. Here are some of the key points of difference between big data analytics and data analytics:
Big data analytics deals with massive volumes of data, which can’t be processed properly using the traditional techniques. Big data processing begins with non-aggregated raw data which is so vast that it requires huge storage capacities as well. On the other hand, data analytics involves the application of algorithmic or mechanical processes in deriving insights. It is concerned with looking for reasonable correlations between data sets by running through a certain number of them.
Big data analytics is best suited for industries such as retail, communication and financial services. They can be put to various applications in these sectors for fraud analysis, customer analysis, operational analysis and compliance analysis. Data analytics, however, has the best application in industries such as energy management, healthcare, gaming, and travel. It helps companies to optimize their activities and ensure better cost savings.
Big data analytics identifies and presents the correlation between different variable. This helps companies understand the relationship between variables that might have gone unnoticed. Data analytics, on the other hand, is devoted to gaining actionable insights that can be applied immediately based on existing queries.
An important point of difference between big data and data analytics is how each facilitates in decision making. In the case of big data, massive volumes of data are gathered to predict what would work in the market and what might not. Companies can take a call on what strategies to implement based on these predictions. On the other hand, data analytics is mostly used to examine historical data and identify the variables that have contributed to the business success and the ones that have not.