Data Analysis: What it is, Types & Examples

Today we collect data at various points of processes and transactions, which has huge potential to change the way we work for the better. However, this data analysis can add value to the business only when it’s analyzed to gain insights into how to improve your products and services.

Data analysis allows you to know and interpret information to identify points of value. But what is data analysis in simple words?

What is data analysis?

Data analysis is the science of examining a set of data to draw conclusions about the information to be able to make decisions or simply to expand the knowledge on various subjects.

It consists of subjecting data to operations. This is done to obtain precise conclusions that will help us achieve our objectives, such as operations that cannot be previously defined since data collection may reveal specific difficulties.

Currently, many industries use data to draw conclusions and decide on actions to implement. It is worth mentioning that science also uses data analysis to test or discard existing theories or models.

Daniel Burrus, business consultant and speaker on business and innovation issues, says in reference to data analysis: “A lot of this will help humans work smarter and faster because we have data on everything that happens”.

Uses of data analysis

It is used in many industries regardless of the branch. It gives us the basis to make decisions or confirm if a hypothesis is true.

  • Marketing: Mainly, researchers perform data analysis to predict consumer behavior and help companies place their products and services in the market accordingly. For instance, sales data analysis can help you identify the product range not-so-popular in a specific demographic group. It can give you insights into tweaking your current marketing campaign to better connect with the target audience and address their needs. 
  • Human Resources: Organizations can use data analysis to offer a great experience to their employees and ensure an excellent work environment. They can also utilize the data at hand to find out the best resources – the ones whose skill set matches with the organizational goals.
  • Academics: Universities and academic institutions can perform data analysis to measure student performance and gain insights into ways education can be further improved.

Techniques for Analysis

To make sense out of raw data, it is essential to analyze it. We must resort to various techniques that depend on the type of information being collected, so it is important to have defined the method to be used before implementing it.

  • Qualitative data analysis: Researchers collect qualitative data from the underlying emotions, body language, and expressions. It is based on the interpretation of verbal responses. The most common ways of obtaining this information are through open-ended interviews, focus groups, and observation groups, where researchers generally analyze patterns in observations throughout the data collection phase.
  • Quantitative data analysis: Quantitative data is presented in numerical form. It is based on tangible results.

Data analysis focuses on reaching a conclusion based solely on what is already known to the researcher. The way you collect your data should relate to how you plan to analyze and use it. You also need to make sure you collect accurate information that you can trust. 

There are many data collection techniques, but the most commonly used method by experts is online surveys. It offers significant benefits such as reducing time and money as compared to traditional methods of data collection.

At QuestionPro, we have an accurate data analysis tool that will help you make better decisions in a professional way.

Advantages of data analysis

  • Make faster and more informed business decisions backed by facts.
  • Identify performance issues that require action.
  • Gain a deeper understanding of customer requirements, which in turn creates better business relationships.
  • Increase awareness of risks to implement preventive measures.
  • Visualize different dimensions of the data.
  • Gain competitive advantage.
  • Better understand the financial performance of the business.
  • Identify ways to reduce costs and thus increase profits.

Here are example questions of different types of data analysis. You can include them in your post-event surveys aimed at your customers:

The qualitative analysis focuses on opinions, attitudes, and beliefs.

Questions start with: Why? How? 

Example of qualitative data analysis: Panels where a discussion is held and consumers are interviewed about what they like or dislike about the place.

The quantitative analysis focuses on hard data and information that can be counted.

Data is collected by asking questions like: How many? Who? How often? Where?

Example of quantitative data analysis: Surveys focused on measuring sales, trends, reports, or perceptions.

Steps to Step Guide

Step 1: Define your questions

Start by selecting the right questions. Questions should be measurable, clear, and concise. Design your questions to qualify or disqualify possible solutions to your specific problem.

Step 2: Establish measurement priorities

This step is divided into two sub-steps:

  1. Decide what to measure: Analyze what kind of data you need.
  2. Decide how to measure it: Thinking about how to measure your data is just as important, especially before the data collection phase, because your measurement process supports or discredits your analysis later on.

Step 3: Collect data

With the question clearly defined and your measurement priorities established, now it’s time to collect your data. As you collect and organize your data, remember to keep these important points in mind:

Before collecting new data, determine what information could be collected from existing databases or sources.

Determine in advance a storage and file naming system to help all team members collaborate. This process saves time and prevents team members from collecting the same information twice.

If you need to collect data through surveys, observation, or interviews, develop a questionnaire in advance to ensure consistency and save time.

Keep the collected data organized with a log of collection date and add any source notes as you go along.

You may want to read: What is primary and secondary research?

Step 4: Analyze the data

Once you’ve collected the right data to answer your Step 1 question, it’s time to conduct a deeper analysis. Find relationships, identify trends, sort and filter your data according to variables. As you analyze the data you will find that you have the exact data you need.

Step 5: Interpret the results

After analyzing the data and possibly conducting further research, it is finally time to interpret the results. Ask yourself these key questions:

  • Does the data answer your original question? How?
  • Does the data help you defend any objections? How?
  • Are there any limitations to the conclusions, any angles you haven’t considered?

If your interpretation of the data holds up under all of these questions and considerations, then you have likely reached a productive conclusion. The only remaining step is to use the results of the data analysis process to decide how you are going to act.

With these five steps in your data analysis process, you will make better decisions for your business because your choices are supported by data that has been well collected and analyzed.

Make the right decisions by analyzing data the right way!

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