Let’s look at the subtle distinctions between the phrases analysis and analytics. Because the terms are so similar, some people assume they have the same definition and misuse them alternately. This isn’t entirely right. There is, in fact, a significant distinction between the two, so let’s get started!
Merriam Webster defines analysis as “the separation of a whole into individual modules,” while analytics is “the study of rational evaluation.” While analysis focuses past in time and focuses on the data and statistics of what has occurred, analytics is concerned with modelling the long term future or forecasting a consequence.
Data Analysis:
Consider data analysis to be a portion of the data analytics pie. Cleaning, converting, modelling, and querying data to obtain pertinent information is what data analysis entails. Assume you have a large data collection including data of numerous categories; instead of handling the full data set and risking being swamped, you divide it into smaller parts and investigate them separately. That’s it for analysis.
Analytics:
Analytics is mostly concerned with the future. It investigates the prospective future rather than describing previous occurrences. Analytics entails the introduction of rational and quantitative reasoning to the results of an Analysis. You are just searching for patterns within the information generated from the analysis and experimenting with what you may do with it in the upcoming.
There are two types of analytics: qualitative analytics and quantitative analytics. The goal of qualitative analytics is to predict your future potential and company decisions by combining your instincts and knowledge with analysis. Quantitative analytics is the use of formulae and procedures to the statistics (data) derived from your research.
Assume you are the proprietor of an online apparel company. You are ahead of your competitors and have a strong grasp on what your clients require and desire. You’ve done a thorough research and are certain about the new trends to pursue. You may use your instincts to choose which clothes styles to sell first. This is qualitative analytics, but you may be unsure when to release the latest collection. In that situation, you may estimate the ideal month to announce the fresh entire collection based on prior sales and customer experience data. This is an example of quantitative analytics in action.
