What is the Key Objective Of Data Analysis? Methods, Process Explained
Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics, businesses today are working with slimmer margins for error.
Companies that want to not only stay in business but also thrive can improve their odds of success by making smart choices while answering the question: “What is data analysis?” And how does an individual or organization make these choices? They do it by collecting as much useful, actionable information as possible, and then using it to make better-informed decisions!
This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on bad data. Organizations need information; they need data. This is where data analysis enters the picture.
Now, before getting into the details about the data analysis methods, let us first understand what data analysis is.
What is Data Analysis?
Although many groups, organizations, and experts have different ways to approach data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data, and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.
A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis.
It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “What is data?”
Why is Data Analysis Important?
Here is a list of reasons why data analysis is such a crucial part of doing business today.
• Better Customer Targeting: You don’t want to waste your business’s precious time, resources, and money putting together advertising campaigns targeted at demographic groups that have little to no interest in the goods and services you offer. Data analysis helps you see where you should be focusing your advertising efforts.
• You Will Know Your Target Customers Better: Data analysis tracks how well your products and campaigns are performing within your target demographic. Through data analysis, your business can get a better idea of your target audience’s spending habits, disposable income, and most likely areas of interest. This data helps businesses set prices, determine the length of ad campaigns, and even help project the quantity of goods needed.
• Reduce Operational Costs: Data analysis shows you which areas in your business need more resources and money, and which areas are not producing and thus should be scaled back or eliminated outright.
• Better Problem-Solving Methods: Informed decisions are more likely to be successful decisions. Data provides businesses with information. You can see where this progression is leading. Data analysis helps businesses make the right choices and avoid costly pitfalls.
• You Get More Accurate Data: If you want to make informed decisions, you need data, but there’s more to it. The data in question must be accurate. Data analysis helps businesses acquire relevant, accurate information, suitable for developing future marketing strategies, and business plans, and realigning the company’s vision or mission.
What Is the Data Analysis Process?
Answering the question “What is data analysis” is only the first step. Now we will look at how it’s performed. The data analysis process, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process consists of:
• Data Requirement Gathering: Ask yourself why you’re doing this analysis, what type of data analysis you want to use, and what data you are planning on analyzing.
• Data Collection: Guided by the requirements you’ve identified, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct observation, and focus groups. Make sure to organize the collected data for analysis.
• Data Cleaning: Not all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data cleaning is mandatory before sending the information on for analysis.
• Data Analysis: Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
• Data Interpretation: Now that you have your results, you need to interpret them and come up with the best courses of action, based on your findings.
• Data Visualization: Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps, bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships.
what is the key objective of data analysis in Research?
A huge part of a researcher’s job is to sift through data. That is literally the definition of “research.” However, today’s Information Age routinely produces a tidal wave of data, enough to overwhelm even the most dedicated researcher.
Data analysis, therefore, plays a key role in distilling this information into a more accurate and relevant form, making it easier for researchers to do their jobs.
Data analysis also provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis.
So, to sum it up, data analysis offers researchers better data and better ways to analyze and study said data.