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GuidesResearch methods10 data analysis techniques you should know

10 data analysis techniques you should know

Last updated

14 July 2023


Dovetail Editorial Team

Reviewed by

Cathy Heath

With rapidly changing markets, uncertain economic times, and challenging consumer attitudes, today’s businesses have small margins for error. 

They must make smart and fast business decisions to thrive, so it’s important they understand data analysis. 

In this article, we’ll look at the best data analysis techniques in research for businesses. 

What is data analysis?

Data analysis is the practice of applying statistical and logical techniques to illustrate, recap, condense, and evaluate data to make informed decisions. 

Companies rely on these processes and tools to gather insights to support operational and strategic decision-making. 

You can use various methods to analyze data, and they’re primarily based on two major areas:

  • Quantitative data analysis: This is data that you can count, measure, order, or quantify with a numerical value.

  • Qualitative data analysisThis data focuses on words rather than numbers.

Basically, quantitative research focuses on the numbers, while qualitative research focuses on the why behind the numbers. 

However, while each method provides different types of data, it’s challenging to conduct a successful data analysis without both research types.

The data analysis process

Now you know what data analysis is, let’s look at how to perform it step-by-step. 


The first step of the data analysis process is determining why you need an analysis and your goals for this undertaking.  

When trying to find your purpose for research, consider: 

  • The metrics you need to track

  • What type of data you want to use 

  • What data you plan to analyze


You need to collect the data from your sources, which can include surveys, questionnaires, observations, case studies, and focus groups

Make sure to organize the data so it is easier to analyze. 


Obtaining a significant amount of data is great, but it’s not necessarily all usable. 

You need to clean up the data before analyzing it. Remove duplicates, fix basic errors to avoid any false results, and ensure you have the largest amount of data to analyze. 


Here’s where you use data intelligence tools to interpret, categorize, and understand your data. 

The goal is to have reliable, relevant information for the situation you’re analyzing. It should help you understand the question you asked. 


Now you have the results, interpret them and figure out what they mean for your company. These findings will help you decide on the best course of action. 


This step involves representing data and information through charts, graphs, bullet points, maps, and other visual methods. 

It’s a powerful way to highlight valuable insights, patterns, or trends that others can read and understand quickly.

Presenting your data in this way is especially useful when comparing datasets and observing relationships between them. 

Why is data analysis important?

Data analysis is an important tool for any business that wants to: 

  • Understand its customers better

  • Improve sales

  • Figure out ways to target customers

  • Reduce costs

  • Discover problem-solving solutions

When conducted appropriately, data analysis enables businesses to:

  • Make better-informed business decisions and avoid spending money on ineffective, unproven strategies for products and services

  • Use the data to examine their processes, fuel marketing campaigns, and ensure promotions engage the right audiences

  • Collect large amounts of valuable customer data and feedback

  • Discover meaningful patterns to optimize their services and products

  • Identify opportunities to streamline operations, maximize profits, and reduce costs

  • Use insights to determine which processes lead to better results and which ones don’t

  • Manage risks, anticipate problems, protect against fraud, and raise quality standards

Quantitative data analysis techniques

Researchers typically use quantitative data for three purposes: 

  • To measure the differences between certain types of groups

  • To assess relationships between variables

  • To test hypotheses in a significant way

The techniques to measure quantitative data include the following:

Regression analysis

This quantitative research method studies several variables where the relationships have one or more independent variables and a dependent variable

Simple linear regression analysis

This tool models the relationship between two continuous variables: One dependent variable and one independent variable. 

The objective of this method is to predict an output variable’s value based on the value of an input variable. 

Multiple linear regression analysis

The multiple linear regression analysis technique uses two or more independent variables to determine the outcome of a dependent variable. 

Hypothesis analysis

This technique allows businesses to test their assumptions and estimate the relationship between two statistical variables. 

Null hypothesis

The null hypothesis claims no relationship exists between the two sets of variables you’re analyzing. Instead, any difference is because of chance alone, and an underlying causative relationship does not exist.

Alternative hypothesis

The alternative hypothesis claims an effect on the population, and it’s the statement you test when trying to disprove the null hypothesis. 

Qualitative data analysis techniques

Qualitative data refers to non-numeric information such as recordings, images, documents, transcripts, and other notes. 

As a result, we can divide analysis techniques into these categories:

Content analysis

This research tool determines the presence of certain themes and concepts within different types of qualitative data. 

Content analysis can reveal patterns in communication that indicate the message, purpose, and effect of the content. 

It can also determine the intent of the content producers and the impact it has on target audiences. 

Identify data sources

The first step in categorizing existing data is to determine the type of content you need to analyze, where the data comes from, and who owns the data. 

These sources include books, social media posts, newspapers, and videos or photos. 

Determine data criteria

This step determines what makes a particular text appropriate to the study. 

For example, does the text mentions a specific topic? Is it even related to the issue? Or does it fall within a particular data range? 

Discourse analysis

This method analyzes the structure of texts longer than one sentence. It takes into account their linguistic and sociolinguistic context. 

As a result, discourse analysis helps companies interpret the true intent and meaning of communication, clearing up any misunderstandings. 

How to choose the right data analysis technique

Because many types of data analysis tools and methods are available, figuring out which techniques to use can be tricky. 

It depends on the type of data you have and what you want to achieve. It’s critical to note that your results can be meaningless or incorrect if you try to use a method that does not support your data. 

Consequently, ensure that you clearly understand the data type you’re using and determine which technique supports it.

How much time is required for data analysis?

The time involved in analyzing data depends on several factors, including the amount of data you have. If you want to analyze data faster, follow these steps:

Define your goals

Before analyzing your data, set clear goals and figure out what you want to gain. This can help you figure out what data you need to collect and the type of analysis to perform. 

Decide how you will measure your goals

After defining your goals, figure out how to measure these goals. For instance, consider whether you need qualitative or quantitative data or both to get the desired results.

Collect the data

Once you understand your goals and how you want to measure them, you can begin collecting appropriate data. 

While you should collect quantitative and qualitative data, you want relevant data for the questions you’re trying to answer. 

Companies usually store quantitative data in databases, and you can find qualitative data in customer emails, support tickets, product reviews, survey responses, and social media data. However, simply finding this data is not enough. 

You also need to look for high-quality data. It’s critical to take the time to prepare the data by removing unnecessary elements that usually appear in unstructured text.

Analyze the data

Once you’ve gathered the data you need, you can analyze it. Create comprehensive charts to better understand what these figures can mean for your company. 

Using the right tools and processes means gathering appropriate data and getting results faster.

How have methods of data analysis changed over time?

Today's competitive marketplace means businesses must sift through massive amounts of data faster and analyze it for appropriate action. Fortunately, as technology continues to evolve, so do data analysis methods:

Descriptive analytics

Descriptive analysis examines content or data to answer what happened or what is happening. 

Companies need to review raw historical data and present it in an easy-to-understand, accurate view of past behaviors or patterns. 

When businesses understand what happened, they can see how it might influence their future.

Predictive analytics

Predictive analysis is the process of accurately forecasting what could happen moving forward. 

This process uses machine learning, artificial intelligence, data analysis, and statistical models to find certain data patterns. These can predict future behavior, forecast demands, and identify trends based on various variables. 

Prescriptive analytics

Prescriptive analysis refers to the advanced process of analyzing content and data to recommend the best strategy moving forward. 

Put more simply, it looks to answer the question, "What should we do?" 

While this is a relatively new form of analytics, companies have successfully used it to optimize customer experience and deliver the right projects within the appropriate time frames. 

Data analysis ensures companies can keep their fingers on the pulse, continually meet customer needs, and improve their bottom line.

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