Data Analysis

What is Data Analysis and why do you need it?

Data analysis is defined as a process of cleaning, transforming, and modelling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

A Company needs to analyze their business data and business processes in order to grow in an exponential way.

Befits of Data Analysis:

• Customer Acquisition and Retention
• Personalize the customer experience
• Focused and Targeted Campaigns
• Identification of Potential Risks
• Innovative Products

Data Analysis Process

The Data Analysis Process consists of acquiring data using a suitable application or tool that allows you to analyze the data and identify patterns. You can make judgements or draw ultimate conclusions based on the knowledge and facts.

Data Analysis consists of the following phases:

• Data Requirement Gathering
• Data Collection
• Data Cleaning
• Data Analysis
• Data Interpretation
• Data Visualization

Data Requirement Gathering

First and foremost, consider why you want to do this data analysis. All you have to do now is figure out what the purpose or goal of the big data is. You must choose the sort of data analysis you wish to perform! You must identify what to analyze and how to evaluate it in this phase, as well as why you are researching and what methods you will use to do this analysis.

Data Collection

After gathering requirements, you’ll have a better idea of what you need to measure and what your findings should be. It’s now time to start collecting data depending on the criteria. Remember that once you’ve acquired your data, you’ll need to process or arrange it for analysis. You must keep a record with a collection date and the source of the data as you gather data from multiple sources.

Data Cleaning

Now, whatever data you’ve gathered might not be valuable or related to your analysis goal, therefore it should be cleared. There may be duplicate records, white spaces, or inaccuracies in the data obtained. The data should be error-free and tidy. This step must be completed prior to The analysis since the result of Analysis will be closer to the intended outcome if data is cleaned first.

Data Analysis

The data is ready for analysis once it has been collected, cleared, and processed. As you alter data, you may discover that you already have all of the information you require, or that you need to obtain more. You can utilize data analysis tools and software to assist you comprehend, analyze, and develop conclusions based on the requirements during this phase.

Data Interpretation

After you’ve analyzed your data, it’s time to interpret your findings. You can describe or explain your data analysis in a variety of ways, including simply in words, a table, or a chart. Then, based on the findings of your data analysis, determine the best line of action.

Data Visualization

Data visualization is quite frequent in everyday life; it usually takes the shape of graphs and charts. To put it another way, data is presented graphically to make it easier for the human brain to comprehend and digest it. Unknown facts and patterns are frequently discovered through data visualization. You can discover useful knowledge by observing relationships and comparing datasets.