Let’s Discuss Data Analysis
Cleaning, transforming, and modeling data to discover valuable knowledge for business decision-making is known as data analysis. Data analysts' goal is to derive valuable knowledge from data and make decisions based on that information. A basic example of data analysis is when we make a decision in our daily lives, we consider what happened the last time we made that decision or what would happen if we make that decision. This is nothing more than looking backwards or forwards in time and drawing conclusions based on our findings. We do this by gathering memories from the past or fantasizing about the future. So that's what there is about data analysis. Data analysis is what an analyst does now for company purposes.
A solid strategy is needed for any good data analysis project. The data analysis project strategy depicts all of the project's fundamental specifications. The strategy defines the structure of the data, announces the study's objectives, explains the data sources, and specifies the study's procedures. Since it explains the study's methodology and intent to managers, grant authors, and specialists in the area, the plan paper becomes an important aspect of the project.
The project's objective must be included in the data collection project strategy. These priorities show concerned parties what the goals are and what a thorough review of the data can reveal. The project's objectives should be focused around a particular market issue, such as "How do fluctuations in raw material costs impact the company's profits?" or "How do social media messages affect stock prices?"
The next concern the project should address after establishing the project's objectives is the sources of the data to be included in the study. Annual earnings or asset values are examples of quantitative data sources, while assumptions and views are examples of subjective data sources. For example, more quantitative data sources will be used in financial data analysis plans, while more analytical data sources will be used in marketing and leadership evaluations.
Method of Data Collection
The data collection techniques must also be included in the project. Annual surveys, market revenue statistics, and stock price records will also provide objective evidence to analysts. Customer surveys, opinion polling, and face-to-face interviews are all methods for gathering anecdotal results. The strategy must demonstrate why each approach is being used and how it can help the project achieve its goals. If the strategies outlined in the proposal do not align with the project's goals, the project's request for resources to achieve the mission will be denied.
Data Analysis method
After the data collection tasks are finished, the project's next step is to review the data. The tools used to interpret the data must be included in the project schedule. Quantitative approaches, such as mathematical analyses, and qualitative methods, such as assessing emotions or experiences, are also available. The design of the research approaches to be used is often dictated by the project's goals. For example, a data collection project with the goal of determining customer satisfaction with a new product could use both quantitative and qualitative approaches, such as data from customer surveys.
We will discuss the various methods of data analysis and their application in the next post.