What is Data Analysis?

The best approach to understand data analytics is to relate it to diamond mining. Diamonds are precious, but getting to the gems you're seeking for requires sifting through several layers. This is where the term "data mining" comes from. Many people incorrectly believe they have data mining and analytics abilities because they know how to acquire data. Data collection and data analysis, on the other hand, are two quite distinct talents.

When collecting data for theses, academic researchers tell their students that gathering a big quantity of data is not a skill; anyone can do it; what is noteworthy is collecting data that is actually useful. That's a lot of effort.

The next sections cover the fundamentals of data analysis as well as three distinct forms of data analysis that you may employ in your company.

“The world is awash in information. It is estimated that 2.5 quintillion bytes of data are produced every day, however in the case of data, more is not always better. What matters is what you can do with data.”

Data analysis is used to create better business judgments and conclusions by analyzing, mining, and filtering through acquired data or information. Data analytics, according to the Harvard Business Review, can "inform you what is occurring, but it will seldom tell you why."

Knowing how to pick the proper data analysis approach is also important for getting the most out of your data. The following are three data analysis methods that your company may start using right now:

1. Text Analytics

2. Business Intelligence

3. Data Visualization

Knowing how to pick the proper data analysis approach is also important for getting the most out of your data.

Text Analytics

Data Analysts sift through text to extract relevant information after gathering data in text form. There are three steps to this:

1. Handle unstructured textual data gathered from the web, databases, or files.

2. Use natural language processing or pattern recognition to extract meaningful numerical indices.

3. Make the data available to statistical and machine learning algorithms to further process it.

When a corporation, such as Facebook, analyses text written by consumers after they provide feedback on a product or service, this is an example of text analytics. When customers delete an ad that appears on their page, purchase items through a Facebook ad, or deactivate their Facebook account, Facebook will ask for feedback. The Data Analysts will then relay that information to the appropriate departments, who will take practical action based on what can be learned from the data.

Text Analytics

This type of data analysis use software to transform data into useful information that can be utilized to make strategic business choices. The acquired data sets are analyzed by business intelligence software and presented in the form of graphs, reports, charts, maps, and dashboards.

Business intelligence, according to our team of analysts and educators, "doesn't tell you what to do; it tells you what was and what is." As a result, business intelligence is used to analyze data in order to spot patterns, get insights, and create targeted marketing.

A large US retailer, used business intelligence to predict whether consumers were pregnant by analyzing data from their loyalty program. They utilized this information to start offering these consumers tailored deals on products like nappies.

Data Visualization

This is simply a method of displaying data as pictures, with the use of statistics, pivot tables, and other tools to make the data more intelligible and approachable. Decision makers may also examine analytics in a consumable format, allowing them to comprehend complex topics or patterns.

It does not instruct you what to do; rather, it informs you of what was and is.

A bar chart that depicts a sales team's rising performance, or a histogram that depicts the distribution of a variable are examples of data visualization. Hans Rosling, a Swedish physician, academic, and co-founder of the Gapminder Foundation, utilizes data visualization to depict subjects like population increase and economic disparities in a meaningful and tangible way.

Finding anomalies and patterns in data to anticipate outcomes might be exploratory and descriptive, or in-depth and specialized. The information obtained may be utilized to dramatically decrease company expenses, boost income, and minimize risk by utilizing one or more data analysis methodologies.