Big Data Analytics refers to the processes, tools and methods used when analysing large complex data sets. This is very important because we know that companies collect more data than ever before partly due the increased use of social media and IoT devices. But without adequate analysis, this data doesn't hold the same value. The cost of not properly utilising this data is that a business can fail to keep up with competitors that are making the most out of big data and using it to analyse consumer behaviour and market trends.
During the data analysis process data all different types of data are consolidated in one place, usually a large data centre. From there it is then sorted through, filtered and verified. Then AI, machine learning and data processing tools such as Adobe Spark, an open-source engine for data analysis are used to analyse the data to find any patterns or information that could be useful.
There are four main types of data analytics that each provide unique insight and benefits.
Descriptive - which focuses on looking at historical data to form concrete statistical information.
Diagnostic - looks at why something has happened or is the way it is, based on past trends and patterns in data.
Predictive - aims to provide insight into what could happen in the future based on statistics and AI predictions.
Prescriptive - uses evidence from the other three to determine an ideal approach for the future.
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ReplyDeleteClear and well structured explanation.
ReplyDeleteGood overview of what big data analytics is and why it matters for businesses, especially the link between data volume, competition, and decision making.
Great explanation analysis and good for better decision making.
ReplyDeleteNice breakdown of the four analytics types clear and easy to follow
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