First, it is important to define the term big data and differentiate between data analysis and data analytics.
Big data refers to data sets so large or complex that traditional data processing applications are inadequate. It is characterised by big volume, high velocity (it accrues fast e.g. transactions data) and variety (can be structured or unstructured e.g. videos, emails); what is sometime referred to as the three Vs of big data. The challenges mainly include analysis, capture, storage and visualization.
Data analysis refers to the extensive use of statistics, with or without the aid of computerized tools, to gain insights/knowledge from the data.
Data analytics is a discipline rather than a tool. It uses data analysis and other data science tools to recommend actions or aid decision-making; it is thus concerned with the whole process of analysis to insights generated to decisions being made from the insights.
Big data analytics is the process of examining big data to uncover hidden patterns and other useful information that can be used to make better decisions in the application context, which is mostly a business environment.
Why is big data analysis and analytics essential?
First, it provides business intelligence through standard and unplanned business reports which might answered questions such as how consumers behave the way they do and what individual consumer factors are associated with particular product choices or purchase preferences. Big data analytics can also be proactive through approaches like optimization, predictive modelling and forecasting thus aiding decision making for the future.;
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