What is Data Analysis? Methods, Process and Types Explained

What Is Data Analysis?

Although many groups, organizations, and experts have different ways of approaching 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 crucial to 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 and marketing 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 number 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, 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 process of data analysis, 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 of data analysis consists of:

• Data Requirement Gathering: Ask yourself why you’re doing this analysis, what type of data you want to use, and what data you plan to analyze.
• Data Collection: Guided by your identified requirements, 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.

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What Is the Importance 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. From a birds eye view, data analysis:

1. plays a key role in distilling this information into a more accurate and relevant form, making it easier for researchers to do to their job.

2. provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis.

3. offers researchers better data and better ways to analyze and study said data.

What is Data Analysis: Types of Data Analysis

A half-dozen popular types of data analysis are available today, commonly employed in the worlds of technology and business. They are: 

• Diagnostic Analysis: Diagnostic analysis answers the question, “Why did this happen?” Using insights gained from statistical analysis (more on that later!), analysts use diagnostic analysis to identify patterns in data. Ideally, the analysts find similar patterns that existed in the past, and consequently, use those solutions to resolve the present challenges hopefully.
• Predictive Analysis: Predictive analysis answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events. While there’s no such thing as 100 percent accurate forecasting, the odds improve if the analysts have plenty of detailed information and the discipline to research it thoroughly.
• Prescriptive Analysis: Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.
• Statistical Analysis: Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:
  • Descriptive: Descriptive analysis works with either complete or selections of summarized numerical data. It illustrates means and deviations in continuous data and percentages and frequencies in categorical data.
  • Inferential: Inferential analysis works with samples derived from complete data. An analyst can arrive at different conclusions from the same comprehensive data set just by choosing different samplings.
• Text Analysis: Also called “data mining,” text analysis uses databases and data mining tools to discover patterns residing in large datasets. It transforms raw data into useful business information. Text analysis is arguably the most straightforward and the most direct method of data analysis.

Next, we will get into the depths to understand about the data analysis methods.

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Data Analysis Methods

Some professionals use the terms “data analysis methods” and “data analysis techniques” interchangeably. To further complicate matters, sometimes people throw in the previously discussed “data analysis types” into the fray as well! Our hope here is to establish a distinction between what kinds of data analysis exist, and the various ways it’s used.

Although there are many data analysis methods available, they all fall into one of two primary types: qualitative analysis and quantitative analysis.

• Qualitative Data Analysis: The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:
  • Content Analysis, for analyzing behavioral and verbal data.
  • Narrative Analysis, for working with data culled from interviews, diaries, surveys.
  • Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.
• Quantitative Data Analysis: Statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:
  • Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
  • Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
  • Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body. 

We can further expand our discussion of data analysis by showing various techniques, broken down by different concepts and tools. 

Top Data Analysis Tools

So, here's a list of the top seven data analysis tools in terms of popularity, learning, and performance.

• Tableau Public
• R Programming
• Python
• Apache Spark
• SAS
• Excel
• RapidMiner

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FAQs

1. What is the role of data analytics?

Data Analytics is the process of collecting, cleaning, sorting, and processing raw data to extract relevant and valuable information to help businesses. An in-depth understanding of data can improve customer experience, retention, targeting, reducing operational costs, and problem-solving methods.

2. What are the types of data analytics?

Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.

3. What are the analytical tools used in data analytics?

The top 10 data analytical tools are Sequentum Enterprise, Datapine, Looker, KNIME, Lexalytics, SAS Forecasting, RapidMiner, OpenRefine, Talend, and NodeXL. The tools aid different data analysis processes, from data gathering to data sorting and analysis. 

4. What is the career growth in data analytics?

Starting off as a Data Analysis, you can quickly move into Senior Analyst, then Analytics Manager, Director of Analytics, or even Chief Data Officer (CDO).

5. Why Is Data Analytics Important?

Data Analysis is essential as it helps businesses understand their customers better, improves sales, improves customer targeting, reduces costs, and allows for the creation of better problem-solving strategies. 

6. Who Is Using Data Analytics?

Data Analytics has now been adopted almost across every industry. Regardless of company size or industry popularity, data analytics plays a huge part in helping businesses understand their customer’s needs and then use it to better tweak their products or services. Data Analytics is prominently used across industries such as Healthcare, Travel, Hospitality, and even FMCG products.