Difference Between Data Mining and Big Data Analytics

Data mining and big data analytics are the two most commonly used terms in the world of data sciience. Most of the newbie considers both the terms similar, while they are not. Previously, we described the difference between data science and big data, apart from publishing specific topics on big data and data mining. Data mining is the approach to find unknown relationships in data. Data Science deals with structured and unstructured data. By principle, anything which relates to data cleansing, preparation and analysis is within the scope of Data Science. We commonly face few of the terminologies, such as :

1. Big Data : huge volume of data which cannot be processed, store effectively with traditional applications.
2. Machine learning (ML) : old terminology was artificial intelligence to include any “automation”. Machine learning uses a training dataset to build a model that can predict values of target variables. Data Mining uses the predictive force of machine learning by applying various algorithms to Big Data.
3. Data Analytics : is all about gaining some insight on a dataset. Some Data Analytics tools can be used to obtain the desired result.

 

Data Analytics uses different analytical models. Data Analytics also known as data analysis and the terminologies is present since 1960s. Data analytics is the art of exploring the facts from the data with specific to answer specific questions. Data analytics refers to the process of examining information sets for drawing potential hypothesis and conclusion about the data. Data analytics transform the raw or unstructured data into a meaningful format. The transformed information can be utilized to cleanse, transform or model the data to support the process of decision making, derive conclusions and implement predictive analytics. It is the systematic method of discovering, analyzing and interpreting data in a multi-dimensional field which helps to make best data driven decisions. Data analytics comprises of quantitative and qualitative techniques to identify data, which include exploratory, descriptive, data mining, predictive analysis and so on.

Data Mining is a part of Data Analytics which aims to reach an extensive conclusion or hypothesis and became “popular” since the 90s. Data Mining is the sequential procedure which involves identifying and discovering the hidden patterns and information from a large set of data by using mathematical methods for discovering patterns. Data mining searches for the data sets and converts the raw data structures into actionable insights by formulating or recognizing various patterns of data through the computational algorithms and logics. Data Mining studies are mostly on structured data. Four most useful data mining techniques:

1. Regression (predictive)
2. Association Rule Discovery (descriptive)
3. Classification (predictive)
4. Clustering (descriptive)

and there are many more.

As conclusion, we can say that the data analytics originated from business analytics or business intelligence models. While data mining uses scientific and mathematical techniques to find patterns and trends. Data mining is closer to machine learning than data analytics.

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