Learn proven and tested data analysis techniques and methods to truly leverage your business data.

Processing mounds of data and uncovering the insights you want to extract can become tricky.

You’d have to deal with a couple of things:

• Velocity. The rate at which data is coming in.
• Complexity. The data comes from various sources, so creating meaningful associations becomes difficult.
• Veracity. You need to ensure the trustworthiness and quality of your data is up to par.

(We’re just scratching the surface, of course.)

The good news is that using the most appropriate data analysis techniques and methods can help you uncover the insights you want to extract in the most efficient way possible.

Table of contents

• Importance of using the correct data analysis techniques
• What is the data analysis process
• 15 Top data analysis techniques
• How to choose the correct data analysis techniques
• FAQs on data analysis techniques
• Take your data analysis to the next level with Polymer

Importance of using the correct data analysis techniques

One of the fundamentals of data analysis is adopting the right techniques to extract specific insights effectively. 

Using the correct ways to analyze data also offers the following benefits:

Find the correct solution to complex problems

Accurate data analysis using the appropriate techniques helps data analysts uncover suitable and actionable solutions to complex problems.  

The right data analysis technique ensures you use an approach tailored to the data’s specific characteristics and the problem’s nature. 

For example, complex problems usually have multifaceted data. 

Learning how to do data analysis using appropriate techniques helps you uncover correlations, trends, and hidden patterns within the data.

It allows you to extract insights you won’t likely obtain using other analysis methods.

Ensure accurate data for informed decision-making

Appropriate analysis techniques can give you accurate data since it can help you do the following:

• Extract unbiased and reliable insights into your data.
• Reduce uncertainty by quantifying risks, allowing you to make more informed decisions.
• Assess data objectively, removing subjective biases that negatively impact your decision-making. 
• Stay competitive and agile by developing adaptive strategies through insights derived from correct data analysis techniques. 

Optimize data analysis resources

The best-fitting data analysis techniques ensure you get the most from your data. 

You’ll avoid wasting time, costs, and resources implementing analysis methods that won’t give you the necessary data insights to reach your objectives. 

Implementing the right methods also streamlines your data analysis process, helping you extract actionable insights and run your operations more efficiently. 

Facilitate growth and innovation

A business grows by staying ahead of competitors and understanding consumer needs. 

Adopting suitable data analysis methods helps you uncover insights to develop or innovate products and services that address customer needs.

The correct data analysis techniques also help you discover emerging trends and unexpected patterns that spark innovative ideas and drive business growth. 

What is the data analysis process?

The main steps of data analysis include the following:

Identifying the data you need

The first step in analyzing data is to determine the data you need and why you need it. 

Establish the questions you want answered and turn them into a problem statement. 

You must also determine your scope, including:

• The measurement parameters, including the must-have and negotiable parameters.
• The unit of measurement, such as currency, salary, and time. 
• The data types you want to use.

Data collection

Specify your data sources and how you will use them. 

Data can come from surveys, questionnaires, case studies, focus groups, interviews, and other internal or external (or third-party) sources.

Your data collection methods can vary depending on whether you’re collecting qualitative or quantitative data. 

Data cleaning

Raw data comes in various formats and can include duplicates, white spaces, typos, and other errors, making it crucial to clean it up first. 

You’ll see an example of raw data and its cleaned version below.

Image source: https://www.researchgate.net/figure/Data-cleaning-example_fig3_220538287

Cleaning data improves your data’s quality, ensuring effective analysis and accurate results. 

Data analysis

Analyze your data using various techniques, such as regressions, text analysis, statistical analysis, neural networks, and more. 

The data analysis stage helps you find variations, patterns, trends, and correlations in your data to answer your problem statement (or statements). 

Data analysis tools, including Business Intelligence (BI) platforms, data visualization software, data mining apps, and predictive analytics, can analyze and help you understand your data. 

Data interpretation and visualization

To derive insights from your data analysis, you must interpret the results to identify the best course of action according to your findings. 

For instance, your analysis results could show that your customers prefer red product packaging over the blue one, resulting in increased sales within the last three months. 

Based on the results, your best bet would be to use your customers’ preferred packaging color to help improve your bottomline. 

An easier way to understand and share your data analysis findings with stakeholders is to visualize them in graphs, charts, maps, and others. 

Doing so displays trends, patterns, outliers, and other key information from your data into easy-to-understand visual elements.    

Visualizations simplify observing data relationships, comparing data, and extracting actionable insights to help you and business stakeholders make informed decisions.

15 Top data analysis techniques

While there are various data analysis techniques and methods, they fall under two main types:

• Qualitative data analysis. The qualitative data analysis method extracts data from symbols, texts or words, photos, and observations. 

The method doesn’t use statistics. Instead, it uses content analysis, narrative analysis, and grounded theory techniques to analyze data you can’t quantify.

• Quantitative data analysis. Statistical or quantitative data analysis turns raw datasets into numerical data. 

It involves qualitative analysis methods, such as mean or averaging, hypothesis testing, and sample size determination. 

Now that we know the overarching data analysis methods, let’s go over the top techniques for analyzing data. 

1. Regression analysis

A regression technique leverages historical data to determine the impact on a dependent variable’s value when one or more independent variables change or remain the same. 

Understanding each variable’s relationship and past development lets you anticipate potential outcomes. It equips you to make better future decisions. 

Let’s say you did a regression analysis of your sales in 2022, and the results showed that variables such as customer service, product quality, sales channels, and marketing campaigns affected the overall outcome. 

You could conduct another regression analysis to check if the variables changed or if you discovered new ones that impact your sales in 2023. 

Your sales could increase with improved product quality or customer service. 

You can back up these findings by understanding which independent variable impacted your dependent variable’s (sales) outcome.  

2. Cluster analysis

Cluster analysis involves categorizing a set of data elements that have more similarities than other data elements in separate groups. 

A cluster analysis plot can look like this:

Image Source: https://byjus.com/maths/cluster-analysis/

Cluster analysis helps you find hidden patterns within the data since clustering doesn’t have a target variable. You can also use it to give additional context to datasets and trends. 

Here’s an example. 

Ideally, you would analyze each customer and provide them with hyper-personalized services. 

However, a massive customer base makes this almost impossible to do. 

Clustering provides a solution by allowing you to group your customers based on their purchasing behaviors, demographics, and other factors relevant to your business. 

Cluster analysis helps you understand a customer segment’s common pain points and preferences. 

It helps you optimize your efforts, giving consumers a customer experience that addresses their needs. 

3. Neural network

The neural network method mimics how the human brain works to discern patterns, predict values, and generate insights within the data. 

It uses interconnected node layers to learn from and process information, such as daily data transactions, allowing it to evolve.  

Neural network methods are often used in predictive analytics to find relationships in data and calculate forecasts based on current and historical data. 

4. Cohort analysis

The cohort analysis uses historical data to compare and analyze specific segments in user behavior and groups it with other similar characteristics. 

The data analysis technique can give you a good idea of your target audience or customers’ needs.  

You can use cohort analysis to understand your marketing campaigns’ impact on specific audience groups. 

Common data analysis examples include using cohort analysis to analyze two email campaign variations over time and determine which version performs better. 

You’ll know which email campaign variation drives customer sign-ups, repeat purchases, and higher engagement. 

Software like Google Analytics 4 (GA4) offers cohort exploration. It can analyze cohorts to give insights into user group behavior and performance based on common attributes. 

5. Time series analysis

A time series analysis examines data points over a specific period. 

While you can use the method to track data within a specific time interval continuously, the time series analysis isn’t only for gathering data over time. 

It’s best to use the data analysis technique to understand whether the variables changed during the study or analysis period, how each variable is dependent, and how the result was reached. 

You can use a time series analysis to understand what causes certain market trends and patterns. 

You can also apply a time series forecasting method to forecast future events based on specific data over time. 

For example, you can perform a time series analysis to forecast your sales data on seasonal products, such as swimwear, during summer. 

It lets you gain the necessary insights to predict demand and prepare your marketing efforts and production operations accordingly. 

6. Factor analysis

Dimension reduction or factor analysis describes the variation within observed related variables based on lower unobserved variables known as factors. 

Image source: https://researchmethod.net/factor-analysis/

Factor analysis is designed to extract independent latent variables, which is ideal for streamlining certain segments.

Imagine evaluating your product via customer feedback and factors such as quality shape, current trends, and color. 

The list can go on and on, depending on what you want to track. 

Factor analysis can simplify this by summarizing all the related variables into similar groups, such as sorting by color, quality, and material under a broader category like design. 

7. Conjoint analysis

Conjoint analysis is typically used in surveys to understand how customers value various product or service attributes. 

It’s one of the most effective data analysis techniques to determine consumer preference.  

For instance, implementing a conjoint analysis helps you determine if your customers value price over features or vice versa. 

You can use the results and uncovered insights to develop your pricing strategies, enhance subscription packages, and refine product packaging options (among others).

If you sell baked goods, you can use conjoint analysis to determine if your customers prefer gluten-free or vegan variations over items with regular ingredients. 

You can use the insights to tailor your marketing efforts to your customer’s preferences and needs.  

Additionally, you can use a conjoint analysis method to segment customers based on interests. It helps you create content and messaging that resonate with and bring value to each audience group.

8. Data mining

The data mining method allows you to spot relations, trends, patterns, and dependencies in large datasets to provide additional context, value, and direction. 

Data mining uses statistical and computational methods using systematic, structured techniques to derive meaningful insights. 

The extracted insights are crucial for making strategic decisions, solving problems, forecasting, and more. 

You can apply data mining to segment customers, perform a market basket analysis, detect fraud, predict customer churn, manage inventory, and other use cases. 

9. Decision trees

Decision tree analysis is a helpful technique for strategic decision-making. 

The method displays potential costs, outcomes, and consequences via a tree-like model. The format makes assessing all the factors involved and choosing the best action easier.

A decision tree works like a flowchart. 

It begins with the main decision or problem that branches out based on various potential consequences and outcomes for each decision.  

Each outcome outlines its costs, consequences, and gains. 

At the end of the analysis, you can compare and choose the best course of action based on each outcome.  

Businesses can use decision trees to determine the most cost-effective projects and those that provide long-term revenue. 

Let’s say you want to decide between updating your existing app or building an entirely new one. 

You can do a decision tree analysis to assess and compare each option's total costs, time you need to invest, and potential revenue.   

It can give you a better idea about the more realistic, attainable, and profitable option for your company. 

10. Multidimensional Scaling

The Multidimensional Scaling (MDS) method lets you observe disparities and similarities between brands, people, geographical coordinates, and other data objects. 

To do a multidimensional scaling analysis, you must plot the objects on an MDS map, positioning similar objects and placing dissimilar ones farther away. 

Then, use one or more dimensions via a numerical scale to represent the similarities or dissimilarities between the data objects. 

The sample MDS map below shows the perceived dissimilarities between baked goods. 

Image Source: https://medium.com/@displayr/what-is-multidimensional-scaling-mds-57bf9cfbd3b7

A high number indicates the subject rates the items as highly dissimilar, and a low number means the baked goods are highly similar. 

MDS is useful in market research, especially when evaluating brand positioning and products. 

For instance, if you want to know how your donut brand stands against competitors, you can identify two to three dimensions: ingredients, taste, and buying experience. 

Then, conduct an MDS analysis to spot where your competitors succeed and opportunities for improving your brand or product.  

11. Content analysis

Content analysis is a popular qualitative method of examining concepts, subjects, and certain words for their frequency and presence in various image, text, video, and audio formats. 

For instance, you can use content analysis to determine the number of times your brand is mentioned on social media. 

The method codes text data that is later tabulated and categorized to provide valuable insights. 

Content analysis can be conceptual or relational. 

Conceptual analysis focuses on explicit data, such as the number of times a word is mentioned within a content piece. 

On the other hand, relational analysis looks at the relationship between various words and concepts and how they connect within specific contexts. 

You can use content analysis in marketing to measure and analyze customer behavior and your brand’s reputation. 

12. Text analysis

Text mining or text analysis organizes large textual datasets (big data) for easy management. 

The methods’ stringent data cleansing process allows you to extract only the data relevant to your company. It helps you develop actionable insights to guide your decision-making. 

Use analytics software with machine learning and intelligent algorithms to perform advanced analytical techniques like sentiment analysis. 

The technique helps you understand the emotions and intentions behind a text, whether negative, neutral, or positive. 

Then, you can score the text based on categories and factors relevant to your business.   

Leverage text analysis methods to analyze data from word-based resources like articles, product reviews, and survey responses. 

Doing so helps you extract valuable insights into your audiences’ pain points, preferences, and needs. 

You can use the insights to tailor your marketing campaigns, communications, and services to meet your customers’ requirements.   

13. Discourse analysis

Discourse analysis allows you to uncover the deeper meaning in verbal, symbolic, or written communication within its specific social, political, and cultural context. 

The method combines context and language analysis to reveal how content and its meaning are primarily shaped by its surrounding society and culture. 

For example, conducting discourse analysis on political speeches should include context elements, such as the country’s current political landscape, the politician’s background, and the target audience. 

Discourse analysis can be excellent for market research. 

It helps you understand how certain market ideas and norms work and how customers relate to them. You can use the insights to develop a unique brand tone or mission.

14. Grounded theory analysis 

Traditional data analysis requires deciding on a method, hypothesis, or problem and gathering the data to answer the problem. 

A grounded theory analysis doesn’t need an initial problem statement or hypothesis since its value is in generating new theories. 

The grounded theory method allows you to go through the analysis process by testing and revising to explore the data and generate new theories. 

You can even uncover valuable insights during data collection. 

The method can give you more reliable insights since the generated theories are backed by data, not initial assumptions. 

Doing a grounded theory analysis helps you analyze topics with limited resources and determine potential causes behind company outcomes. 

For example, your marketers and product managers can do a grounded theory analysis using product reviews and survey data to identify the causes of high customer churn rates.  

15. Narrative analysis

A narrative analysis helps you explore the meaning behind people’s stories and how they tell them. 

It looks into the words people use to describe situations, allowing you to derive relevant conclusions about their perspectives on certain topics. 

Narrative analysis data sources include family stories, testimonials, autobiographies, and opinion pieces. 

You can use narrative analysis to analyze customer feelings and behaviors toward specific services, products, and features. 

It helps you gain deep and unique insights into customer preferences and needs. 

However, using a narrative analysis method has limitations. 

The method usually has a small sample size because of the complex and time-consuming process of collecting narrative data. 

Also, stories or narratives are influenced by the subject's experiences, which can be hard to replicate in the resulting study. 

How to choose the correct data analysis techniques

Selecting the right data analysis method and technique depends on your dataset and what you want to get out of it. 

When dealing with quantitative data, you should use a quantitative analysis method (the same applies to qualitative data and analysis methods). 

Quantitative data analysis methods often use range, standard deviation, and range metrics. 

Hypothesis testing is a popular approach since it lets you find relationships, which you can use to predict or explain data. 

In qualitative data analysis, content analysis is the go-to method to describe textual data, while grounded theory is commonly used to interpret or forecast data. 

You can also consider the following tips when choosing the best-fitting data analysis techniques.

• Identify clear objectives, including the insights you want to extract and data analysis questions you want to answer. 
• Know your data by examining its size, quality, and type (including structured and unstructured data) to determine the best method to handle data with these characteristics.
• Determine the dependent and independent variables to help you identify the right data analysis models, techniques, and statistical tests in your data analysis. 
• Consider the scale of your dataset, whether it is ordinal, nominal, ratio, or interval. Doing so lets you determine the appropriate method to analyze data of that scale. 
• Get help from experts or statisticians with experience analyzing datasets and objectives similar to yours.  

FAQs on data analysis techniques

Check out the answers to the common questions about data analysis techniques below. 

What are the three main types of data analysis for businesses?

The three primary data analysis methods include performing descriptive analysis (or diagnostic analysis) that tells us what has already happened, predictive analytics that shows us what can happen, and prescriptive analytics that informs us what should happen in the future. 

How can you analyze data from observations?

To effectively analyze observation-based data, you must consider its context, such as the task, purpose, situation, participants, and setting. 

Analyzing data within a specific context helps you spot relationships, themes, and patterns while avoiding oversimplifying and misinterpreting the data.

What is the difference between data analytics and data analysis?

Data analysis includes extracting meaningful insights from data to help stakeholders make informed and strategic decisions. 

Data analytics has a broader scope, which includes data analysis. 

It involves using data and analytical tools and techniques to discover new insights and predict potential outcomes for the organization’s benefit. 

Take your data analysis to the next level with Polymer

A critical part of the data analysis process is data visualization. 

However, visualizing data can be time-consuming, laborious, and even costly—unless you use the right tool.

That’s where Polymer comes in.

Polymer is a BI software that is easy to set up and use. It allows you to build visualizations and dashboards within minutes and simplify presenting data insights. 

Polymer offers multiple built-in data connectors to Google Ads, Jira, Zendesk, Airtable, Google Analytics 4, and other platforms to pull your business data into the platform. 

You can also your business data saved in CSV or XLS format to the platform. 

After importing your data to Polymer, add insights like graphs, trends, and other visualizations. 

Polymer’s Artificial Intelligence (AI) suggests and auto-generates visualizations based on your dataset with one click. 

Polymer can instantly analyze your data and generate visualizations. You can customize the charts and graphs with the intuitive Block Editor. 

With Polymer’s AI and user-friendly tools, you can build a comprehensive dashboard within minutes.

Set up your sharing settings by adding other users to view or edit your saved reports and visualizations. 

Polymer eliminates the hassle and complexity of visualizing critical business data through AI-powered, automated data analysis. 

It lets you extract insights faster and make timely business decisions cost-effectively. 

Polymer eliminates much of the manual work that goes into analyzing, interpreting, and analyzing your business data. 

‍Try Polymer now to experience the power of modern BI software for seamless data analysis.