12 Data Analysis Techniques You Need to Know

Do you always have large sets of data to analyze? Are you looking for ways to scan the data for results easily? The good news is there are multiple data analysis techniques that can help you get results in seconds. 

If you are wondering what are data analysis techniques

These are systematized methods formulated to scan large sets of data to deliver vital insights. Analyzing the current and past data of a business gives you a clear picture of the overall performance. In turn, you can set the correct strategies to reach greater heights in the future.

This article brings 12 data analysis techniques you need to know for all your work!

  1. Cluster Analysis
  2. Factor Analysis
  3. Regression Analysis
  4. Segmentation Analysis
  5. Time Series Analysis
  6. Monte Carlo Simulation
  7. Decision Tree Analysis
  8. Artificial neural networks
  9. Sentiment Analysis
  10. Content Analysis
  11. Grounded Theory
  12. Narrative Analysis

The 12 Different Data Analysis Techniques Explained

The right data analysis technique depends on the data type . Read on to learn which technique(s) is best for your current research.

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Cluster Analysis

It is a simple technique of classifying data into groups or categories known as clusters. A cluster analysis identifies structures within a given dataset. Thus, you will get multiple groups, with each group internally containing homogeneous data while being heterogeneous to each other externally.

Cluster analysis can be of varying types, with the two most common being hierarchical and k-means. These can be used to analyze various kinds of real-world data—both qualitative and quantitative

For example, customer groups, locations, cities, etc., in marketing, insurance, geology firms, and more.

Factor Analysis

It is a technique of condensing large datasets with multiple variables into fewer variables. For example, in a set of 50 variables, there may be some that are correlated on several bases. These relations are latent or hidden. Factor analysis brings out these correlations as factors, giving a reduced number of variables.

As a result, you can manage and analyze the reduced set of data with ease . Additionally, you can gain insight into the uncovered or latent patterns. It can be used to analyze both qualitative and quantitative data. 

Factor analysis can be of varying types, with principal component analysis being the most common one.

Regression Analysis

When you talk of analyzing numerical data, regression analysis is the most common technique. It is basically used to determine the relationship between a dependent (main) variable and multiple independent variables (factors impacting the dependent variable).

Running regression analysis can help you learn about the current trends and build future strategies. 

For example, with regression, you can find out whether social media marketing (independent variable) impacts your sales (dependent variable). If there is a positive relationship, there is an impact, and vice versa.

Regression analysis can be of multiple types, depending upon the nature of your data. You must note that this technique is primarily used to analyze numerical data.

Segmentation Analysis

It is a process of dividing segments of data with similar features, interests, needs, etc. Different firms use this process to understand the market and customers better. Thus, they can formulate specific strategies, services, and products, to cater to their needs.

Segmentation analysis can give you an edge over your competitors when it comes to understanding the demographics, behavior, psychology, and geography of customers. Here is a comprehensive guide to understanding the new market segmentation based on various factors.

You must know that this technique is mostly used to analyze qualitative data.

Time Series Analysis

You may often want to analyze data for a given period in order to understand the trend of that particular time. For example, weekly revenues, monthly customer subscriptions, and sign-ups, etc. Time series analysis is primarily conducted to forecast future trends and cycles.

The data for this analysis may be based on regular intervals (daily, weekly, monthly, seasonal) or irregular (trends, variations). This type of research can be seen in multiple practical fields, such as the stock market, forecasting sales, analyzing economic cycles, etc.

It is a quantitative analysis technique and may use methods such as moving average. Here is an example where the prices of gold have been forecasted at intervals of 5 weeks.

Monte Carlo Simulation

The choices that you make can have diverse results that you may be uncertain of. Monte Carlo Simulation is a method to extract possible outcomes for a set of options. The computerized technique combines the same values in varying patterns each time to get different results.

Several professionals use the Monte Carlo method to conduct risk analysis and anticipate the outcomes. In turn, they can formulate better decision policies for the future. 

For example, with this technique, you can predict what your profit will be if your sales increase to 10,000 units/month. It is conducted to analyze quantitative data.

Decision Tree Analysis

A decision tree analysis is used to make decisions and choose the most advantageous options for your business. It is a diagrammatic layout of the possible risks and rewards you will get with every decision you make.

The diagram or flow chart starts with qualitative decisions and ends with quantitative results. Once the layout is ready, go backward and calculate the value of each decision to find out the most profitable option. 

Here is how you can construct a decision tree and extract valuable results.

You can use decision tree analysis for evaluating options like developing a new product, strategy, policy, etc.

Artificial Neural Networks

It is a computerized technique of analyzing data that cannot be analyzed with statistical methods. Artificial neural networks work and process data like the neurons of a human brain. They develop better techniques and patterns as more data is introduced.

Artificial neural network analysis is used in a wide range of fields ranging from finance and communication to education and marketing. 

For example, email service providers can remove spam messages from an individual’s inbox, e-commerce platform personalize recommendations for customers, etc.

You can use this artificial intelligence technique for both qualitative and quantitative analysis. Here is a tutorial to understand the use of artificial neural network analysis with SPSS.

Sentiment Analysis

Analyzing qualitative data can be challenging, but sentiment analysis makes it super-easy. You can basically use this technique to scan and understand the emotions conveyed through textual data. 

For example, businesses can use sentiment analysis to understand the expectations, feedback, and needs of their customers. There are mainly three types of sentiment analysis:

  • Fine-grained: It is used to categorize the responses from an individual on the basis of positive, neutral, and negative aspects.
  • Emotion Detection: This technique picks out the specific emotion of an individual from a text. It may be happy, excited, neutral, sad, angry, etc.
  • Aspect-based: Going to the next level, this technique can detect the emotion along with the factor connected with that emotion.
Data Analysis Techniques

Content Analysis

Content analysis is another technique to analyze qualitative or textual data. It summarizes the text and converts it into quantitative data. In this process, the data is analyzed based on themes, concepts, specific words, etc. 

The text used here is usually taken from social media, books, reviews, and other such recorded sources.

You can apply this technique when dealing with surveys, feedback, interview results, etc., and other such communications. Two prime types of content analysis are:

  • Conceptual Analysis: Scanning the text for specific words, phrases, sentences, etc., that lead to a standard answer.
  • Relational Analysis: It goes a level further and examines the text for concepts that are related to each other.

Grounded Theory

It is a research method that you can conduct to develop a theory from a set of qualitative data. In this technique, several categories, concepts, and propositions are identified to conduct the analysis. 

You may use grounded theory to analyze interviews, recordings, surveys, documents, etc. There are three steps in this process:

  • Open Coding: It is the first step wherein the data is categorized on the basis of concepts that you had identified earlier.
  • Axial Coding: Once the categories have been determined, this step helps determine relationships or form hypotheses with these categories. 
  • Selective Coding: In this step, one main or central category is identified and related to the other categories.

Narrative Analysis

It is a technique to evaluate an individual’s or a group’s story or experience. For example, you can use narrative analysis to understand the ideas and narratives of your employees as well as customers. 

The data for this qualitative research is collected through interviews, documents, biography, images, observations, etc.

The participants can narrate a story about their past, present situation, or future expectations. You can evaluate the data based on the people connected with the story, motive, turning points, places, context and categorize them. 

Here is a guide to coding or categorizing contextual data for analysis.

In Conclusion

Analyzing or evaluating a large set of data is virtually impossible without a machine or a computerized method. Thanks to the multiple data analysis techniques, you do not have to deal with all the calculations. Just enter the details, and your smart device will do the work for you!

From evaluating quantitative data to qualitative, you can get your hands on various statistical, artificial intelligence, and textual analysis techniques. So, what is the right data analysis technique for you? It depends on your type of data.

In this article, you get details about 12 essential data analysis techniques to ease your work. These can help you analyze data for academics as well as organizational research to help you level-up your game!






Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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