Data analysis means data cleaning, modelling, and transforming. It is a process to find business intelligence and decision-making data. The aim of Data Analysis is to derive data from relevant information and to make decisions focused on the analysis of data.
Data analysis is the process to identify the past, present, and future trends that will be affected if we make some decision in our daily life. It is to evaluate and make choices dependent on our experience or prospects. We collect experiences of our past or visions of our future for that. So, it’s nothing but an interpretation of results. When this same thing is done in the business environment, it is called data analysis.
Why Data Analysis?
If your company is not growing, so without committing the same errors, you have to look back and recognize your errors and come up with a plan accordingly. I f your company continues to grow, then you have to strive to make the company expand further. Analyzing your company information and business processes is everything you must do to achieve success. Along with data analysis, data science and big data processing are other aspects of using and manipulating data to find the trends and predict the future.
Data Analysis Types
Following are the types of Data Analysis:
- Statistical Analysis
Statistical analysis illustrates the actions in the context of dashboards leveraging past data. Statistical research requires information processing, analysis, presentation, interpretation, and visualization. It evaluates a collection of information or a data sample.
- Text Analysis
Text Analysis is done utilizing datasets or data mining techniques to explore a sequence in large databases. It has been used to convert raw data into business information. In the industry that is used to make strategic company decisions, BI techniques are available. Overall, it provides a way of extracting and examining information and extrapolating patterns, and finally interpreting the data.
- Predictive Analysis
By using historical evidence, predictive analysis indicates what is going to happen in the future. This means that if I will take a certain action what will be the outcome of that action and what if that action is affected by an external source. Examining and evaluating all the circumstances as well as their potential outcome derived from a particular pattern is called predictive analysis.
- Diagnostic Analysis
Understanding the past and the reasons behind it is Diagnostic Analysis. It is done by discovering the origin from the information found in Statistical Analysis. This research is useful for the determination of data activity patterns. If your business method has a new set of problems, then you should look at this study to find common trends of the problem. And for the new issues, it might be possible to use specific prescriptions.
- Prescriptive Analysis
In order to decide which action to take in a present problem or decision, Prescriptive Analysis incorporates the knowledge from all prior analysis. Prescriptive Analysis is used by most data-driven businesses because predictive and descriptive analytics are not sufficient to enhance data efficiency. They interpret the data on the basis of existing circumstances and issues and make a decision.
Data Analysis Tools
Tools for data analysis make it much easier for consumers to manipulate and process information, analyze the relationships and connections between data sets, and also help recognize perception trends and patterns.
Data Analysis Process
By using an effective usage or method that helps you to analyze the data and find a trend in it, the Data Analysis Process is nothing more than collecting information. You could make the decisions based on the knowledge and evidence, or you can obtain ultimate judgments. Following are the phases of Data Analysis:
- Data Requirement Gathering
Firstly, why is your goal to do data analysis? You have to think about it. Everything you need to find out about the purpose or objective of data analytics. You need to decide what kind of data analysis you would like to do! You have to make a decision what to analyze as well as how to measure it at this stage, you have to comprehend why you are researching and what steps to take to complete the analysis.
- Data Collection
Once the requirement gathering is completed, you will have a clear understanding of what tasks you have to gauge and what the research results should be. Now, it’s time for the data to be collected based on the needs. Once you collect the data, know that it is necessary to process or organize the collected data for the analysis. You also get to keep a log with a collection date as well as the data source.
- Data Cleaning
Whatever information is collected now might not be beneficial or relevant to your research target, so it really should be cleaned up. There may be duplicate documents, white spaces as well as the errors in the data obtained. The data must be cleaned up and free of errors. Before data analysis, this stage must be completed because, provided by data cleaning, the analysis performance would be closest to the intended results.
- Data Analysis
After requirement gathering, data collection, and data cleaning, now your data is subject to review. You will find you have the precise information you need when you process data, or you may require to gather more data. You must use data analysis tools to aid you in understanding, interpreting, and drawing the conclusions depending on the specification.
- Data Interpretation
The next step after the data analysis is to interpret the outcomes. You can now choose the way your data analysis can be represented or conveyed, either easily in words or perhaps in a table or graph. To determine the right plan of action, then use the outcomes of your data analysis process.
- Data Visualization
In your day-to-day life, data visualization is very common; it mostly occurs in the form of charts and graphs. Alternatively, Information is graphically displayed so that it is simpler for the human mind to perceive and interpret it. To uncover hidden information and patterns, data visualization is also used. You may find a way to uncover useful knowledge by analyzing interactions and analyzing databases.
If you further want to get an understanding of Data Analysis or launch your career as a Data Analyst then you should find yourself the best data analysis bootcamp online and get enrolled in it. You can also pursue an in-demand certification or get a degree.