Getting into the Know about the Types of Data Analytics Methodologies
What methodologies are applicable to Data Analytics?
Data Analytics have its own principles; hence it means it will surely have its own methodologies when looking to arrive at any logical conclusion in its analysis. So what are the ideas you have built on about the numerous types of data analytics methodologies?
Well, there is the fact that at a high level, data analytics methodologies separates itself into the Exploratory Data Analysis and the Confirmatory Data Analysis.
The Exploratory Data Analysis take it this way, is like a detective work of sorts whereby its aim is to understand patterns and relationships present in data. You have to find and understand what links data sets together to be able to form a predictive analysis or conclusion if you will.
So bringing it to the logic of detective work, a detective looks to find patterns when investigating a crime in order to connect the dots or cross the Ts. This is basically what the Exploratory Data Analysis does.
The CDA otherwise known as the Confirmatory Data Analysis can be compared to a judge or a jury running a court trial whereby statistical techniques are used to determine whether hypotheses on data sets are true or false.
In the court analogy, it is like a judge or jury listening to statements in court during hearing and at the end of the day coming to a conclusion on which statement sounded truer or was likely to be false.
Data Analytics methodologies can also be categorized into quantitative or qualitative data analysis, where in the quantitative data analysis concerns itself with numerical data with quantifiable variables. These variables are then compared or measured statistically.
For the qualitative approach or methodology, it is rather interpretive and focuses on understanding the content of non-numerical data. After all, things like text, audio, video or images, ideas cannot be measured.
Now, we have talked about the high level of data analytics methodologies, bringing it to the application level, we look at the methodologies involved in Business Intelligence which revolves around the computing and analysis of data sets that provide business executives or corporate employees actionable information like key performance indicators.
Prior to present times, business intelligence developers who worked in the Information Technology sector often created data queries and reports for end users but with technological advancements, organizations now use self-service Business Intelligence tools to help its executives, business analysts run their own queries and build reports
Some of these methodologies involve data mining which allows the arrangement of data sets to identify trends, patterns and relationships.
In using predictive analytics, the application of it is to predict consumer behavior, equipment failures and future events as well.
In the use of machine learning for data analytics, its methodology involves using automated algorithms to churn through data sets quicker than the data scientists would when using conventional analytical modeling.
Data analytics methodologies support a wide variety of business uses and in the case for E-commerce based companies, clickstream analysis is used to identify website visitors likely to buy or apply for specific product or service. For banks, it helps analyze withdrawal and spending patterns in case of situations like fraud or identity theft.