Data analysis is an essential activity for every business regardless of its area of specialization. However, the kind of analysis techniques and methods you apply significantly impact your company’s growth. Before you even think of data analysis methods, you need first to understand the nature of the data you are dealing with to make the most appropriate choice.  Once you have identified the nature of your data, you need to understand that presenting the information from a visual perspective enables 65% of your readers to remember the presented message within the next three days. In addition, data analysis is a wide field involving multiple aspects that you must evaluate keenly. Let’s learn about it! 

What is Data Analysis? 

Data analysis refers to collecting, analyzing, and cleaning unstructured data content using different methods and techniques to identify patterns and uncover in-depth insights about the development of the business. The insights generated from the data analysis process aid in making prudent development decisions that impact business growth.  There are different data analysis methods and techniques that you can apply in processing and analyzing your business data. In addition, the techniques can help you analyze your business competition, and identify your primary customers, giving you a clear picture of the available market opportunities. However, you need to have a clear understanding of the kind of data you intend to analyze to be able to get reliable results.  You need to understand the difference between qualitative data and quantitative data. What is the difference? 

Qualitative Data

This is unstructured data that is usually difficult to analyze, given that it has not been structured. Note that the data should be presented orderly to make it easier for the machine to read and understand. However, qualitative data analysis tends to generate more detailed results that can be used to elevate your business performance.  Qualitative data is known to have the ability to go beyond instances that happened and identify the reasons why something happened the way it is. This data type mainly deals with feelings, opinions, and emotions to generate detailed insights. 

Quantitative Data

Quantitative Data is the kind of data that can be easily quantifiable, such as numbers, numerical data, percentages, and statistics. This type of data is mainly organized in a presentable way such as structured databases and spreadsheets making it easier for machines to digest the content. Analyzing this data is simple since everything is straightforward.  The data calculations are mainly meant to help you identify what happened, such as the specific number of customers within a particular region. 

Top Data Analysis Methods & Techniques

Before discussing methods and techniques you need to know best tools to analyze data.

Best Data Visualization Tools to Analyze Data

  1. Google Sheets
  2. Microsoft Excel
  3. ChartExpo
  4. Tableau
Below are the data analysis methods and techniques applied in analyzing quantitative and qualitative data sets.
  • Regression Analysis

The regression analysis technique is applied in calculating statistical probability, especially in uncovering the relationship between different data variables. The data variables applied, in this case, are known as covariates. Also, the analysis uses independent and dependent data variables. The independent variables are most likely to impact the final outcome, while the dependent variable focus on making predictions.  If you aim to make predictions about the future of your business in terms of sales and revenue, you need to consider features such as the time of the year, weather, and many others. Also, you need to check on any release that your close competitor has made to determine the way forward. 
  • Monte Carlo Simulation

This risk analysis hack utilizes quantitative data to calculate any possible outcomes from the business using the data generated from past business operations. Monte Carlo Simulation mainly focuses on identifying the range of general business outcomes, whether positive or negative. As a result, it helps to determine whether a business can take certain risks or not depending on the possible returns.  The strategy changes from time to time depending on the market condition since the prevailing market situation determines the possible outcomes. Also, it identifies some market factors that are not certain at the moment, although they may turn out to be certain in the future. 
  • Churn Analysis

Churn refers to the loss of customers or employees within a business setting. Churn analysis is applied by a business to identify why either customers or employees are leaving the company and prioritize other competitor brands by creating an employee satisfaction survey. In addition, it allows you to predict some of the things that you can do to mitigate the situation and safeguard the reputation and well-being of your business.  This method mostly uses the survey results and closed-loop feedback methodology to collect as much information as possible to understand the reasons behind whatever is happening within the business. It identifies any possible portholes within your strategy that you can fill and return your business on the right track in terms of development. 
  • Sentiment Analysis

Sentiment analysis is a data analysis technique that utilizes the power of machine learning to classify text messages into different categories such as positive, negative, or neutral. This method is mostly applied in analyzing customer surveys and feedback. You only need to train the sentiment about the language used within your business and let it understand every communication that takes place across all the business platforms.  This technique has the ability to read multiple texts within the shortest time possible and classify them accordingly. It has the ability to grasp the emotions and feelings operating behind every comment that is written by customers and rank it accordingly. 
  • Predictive Analysis

The predictive analysis focuses on using past customer data to predict more about the future of the business. It utilizes quantitative data such as the Net Promoter Score, demographics, and past sales to evaluate the future of your brand. If you realize that a particular demographic is aging into varying interests, it’s likely to impact your customers’ choices in the long run.  Besides, this strategy can go the extra mile in evaluating survey questions, and social media sentiments giving you a more concise view of things regarding your business. Note that you need to understand why customers are taking this kind of decision and how you can convince them to purchase your products and support your business development. The predictive analysis primarily used qualitative data to determine how customers can respond to various marketing strategies. 


If you incorporate Visualization in your data analysis activities, you will likely spend 39% less time than when you do the work manually. Learning about analysis methods and techniques is essential since it gives you clear insights into the best strategies that you can utilize to ensure that you collect vital insights from your data. Also, it enables you to understand how to navigate different data formats, such as quantitative and qualitative data formats.   

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