What Is Data Analysis? Examples & Why It Matters

What Is Data Analysis? Examples & Why It Matters

Up to 55% of data collected by companies goes unused for analysis. That’s a large chunk of insights companies are missing out on.

In this guide, you’ll learn how to analyze data, as well as why it’s essential. You’ll also discover which data analysis techniques and tools to use for different types of data.

Let’s dive in.

What Is Data Analysis?

Data analysis tools are used to extract useful information from business data, and help make the data analysis process easier.

You’ll often hear the term data analytics in business, which is the science or discipline that encompasses the whole process of data management. Not only does data analytics include the data analysis itself, but also data collection, organization, storage, and tools and techniques used to deep-dive into data, as well as those used to communicate the results ‒ for example, data visualization tools.

Data analysis, on the other hand, focuses on the process of turning raw data into useful statistics, information, and explanations.

Why Is Data Analysis important?

In short, analyzed data reveals insights that tell you where you need to focus your efforts.

Data is everywhere: in spreadsheets, your sales pipeline, social media platforms, customer satisfaction surveys, customer support tickets, and more. In our modern information age it’s created at blinding speeds and, when data is analyzed correctly, can be a company’s most valuable asset.

It can help businesses improve specific aspects about their products and services, as well as their overall brand image and customer experience. Product teams, for example, often analyze customer feedback to understand how customers interact with their product, what they’re frustrated with, and which new features they’d like to see. Then, they translate this insight into UX improvements, new features, and enhanced functionalities. Through data analysis, you can also detect the weaknesses and strengths of your competition, uncovering opportunities for improvement.

Top 6 Data Analysis Techniques

There are a number of useful data analysis techniques you can use to discover insights in all types of data, and emerging data analysis trends that can help you stay ahead of your competitors.

Types of data analysis:

Text Analysis

Text analysis, also text analytics or text mining, uses machine learning with natural language processing (NLP) to organize unstructured text data so that it can be properly analyzed for valuable insights. Text analysis is a form of qualitative analysis that is concerned with more than just statistics and numerical values.

By transforming human language into machine-readable data, text analysis tools can sort text by topic, extract keywords, and read for emotion and intent. It tells us “What is happening” as specific, often subjective data. It offers more in-depth and targeted views into why something may be happening, or why something happened.

You can use text analysis to detect topics in customer feedback, for example, and understand which aspects of your brand are important to your customers.

Sentiment analysis is another approach to text analysis, used to analyze data and sort it as Positive, Negative, or Neutral to gain in-depth knowledge about how customers feel towards each aspect.

Try out this sentiment analyzer, below, to get an idea of how text analysis works:

Test with your own text



Descriptive Analysis

Descriptive data analysis provides the “What happened?” when analyzing quantitative data. It is the most basic and most common form of data analysis concerned with describing, summarizing, and identifying patterns through calculations of existing data, like mean, median, mode, percentage, frequency, and range. 

Descriptive analysis is usually the baseline from which other data analysis begins. It is, no doubt, very useful for producing things like revenue reports and KPI dashboards. However, as it is only concerned with statistical analysis and absolute numbers, it can’t provide the reason or motivation for why and how those numbers developed.

Inferential Analysis

Inferential analysis generalizes or hypothesizes about “What happened?” by comparing statistics from groups within an entire population: the population of a country, existing customer base, patients in a medical study, etc. The most common methods for conducting inferential statistics are hypothesis tests and estimation theories.

Inferential analysis is used widely in market research, to compare two variables in an attempt to reach a conclusion: money spent by female customers vs. male or among different age groups, for example. Or it can be used to survey a sample set of the population in an attempt to extrapolate information about the entire population. In this case it is necessary to properly calculate for a representative sample of the population.

Diagnostic Analysis

Diagnostic analysis, also known as root cause analysis, aims to answer “Why did 'X' happen?”. It uses insights from statistical analysis to attempt to understand the cause or reason behind statistics, by identifying patterns or deviations within the data to answer for why.

Diagnostic analysis can be helpful to understand customer behavior, to find out which marketing campaigns actually increase sales, for example. Or let’s say you notice a sudden decrease in customer complaints: Why did this happen? 

Perhaps you fired a certain employee or hired new ones. Maybe you have a new online interface or added a particular product feature. Diagnostic analysis can help calculate the correlation between these possible causes and existing data points. 

Predictive Analysis

Predictive analysis uses known data to postulate about future events. It is concerned with “What is likely to happen.” Used in sales analysis, it often combines demographic data and purchase data with other data points to predict the actions of customers. 

For example, as the demographics of a certain area change, this will affect the ability of certain businesses to exist there. Or as the salary of a certain customer increases, theoretically, they will be able to buy more of your products.

There is often a lot of extrapolative guesswork involved in predictive analysis, but the more data points you have on a given demographic or individual customer, the more accurate the prediction is likely to be. 

Prescriptive Analysis

Prescriptive analysis is the most advanced form of analysis, as it combines all of your data and analytics, then outputs a model prescription: What action to take. Prescriptive analysis works to analyze multiple scenarios, predict the outcome of each, and decide which is the best course of action based on the findings.

Artificial intelligence is an example of prescriptive analysis that’s at the cutting edge of data analysis. AI allows for prescriptive analysis that can ingest and break down massive amounts of data and effectively teach itself how to use the information and make its own informed decisions.

AI used to require huge computing power, making it difficult for businesses to implement. However, with the rise of more advanced data analysis tools, there are many exciting options available.

Data Analysis Use Cases

Data analysis can be said to go back at least around 5000 years to Sumerian population censuses, but until fairly recently it was mostly concerned with quantitative data. However, with advanced, computer-aided tools, businesses can now analyze qualitative data for even more powerful insights. Take a look at these real-world applications of data analysis.

Some of the top applications for data analysis:

Automatically analyze survey responses

Text analysis allows you to conduct surveys aimed at qualitative results, beyond simple Yes/No or multiple choice questions. You can ask open-ended questions, have them organized by subject or theme, and automatically analyzed for the opinion and feeling of the responder.

Anstice, a research and marketing consultancy, analyzed 12,000 multi-paragraph responses with text analysis solution MonkeyLearn to gauge public opinion around building a large infrastructure project, for example.

Not only did this save them hundreds of employee hours, Anstice was able to deliver powerful insights that shaped business decisions.

Product teams often send out in-app surveys to get instant feedback from users. Resulting in thousands of closed and open-ended responses. While analyzing the quantitative data is straightforward, you’ll need to analyze the open-ended responses using text analysis techniques like aspect-based sentiment analysis. This way, product teams can quickly discover new features that customers are requesting, and problems they’re having with the product (Bugs, UX, Reliability, etc.), so that they can respond swiftly.

Learn how to analyze survey data, both quantitative and qualitative, for in-depth insights. Or, request a personalized demo, specific to your use case.

Analyze customer support tickets and automatically route them

Advanced data analysis tools can automatically read through customer support tickets, whether from chatbots, emails, phone calls, etc., rate them for urgency, and automatically route them to the correct department or individual employee.

Archer, a tech-based financial services company, needed to respond quickly to customer inquiries due to their time-sensitive nature. By training a topic classification model to their precise needs, Archer were able to increase initial response time by 65% and increase the ticket volume of each employee by 20%.

Categorize potential customers

Using customer analysis techniques, like survey analysis, inferential and diagnostic analysis, and social listening (real-time social data analysis), you can identify and understand your current customers. Then break them into subgroups, identify how your products meet their needs, and use the data to build prescriptive analysis models and locate new potential customers.

Text analysis tools, like keyword extraction, can dig into granular insights about your customer base to follow subjects and themes, and discover new topics, right as they emerge.

Examine the success or failure of marketing campaigns

Use diagnostic analysis to understand why certain marketing campaigns work and others don’t and text analysis to follow real-time reactions to your campaigns on social media or online reviews.  

What are the keywords used to describe your brand and your products, and how do they change over time? Analyze your customer service tickets to understand how they may relate to individual campaigns. Or monitor social media to maximize your customer engagement.

Predict customer churn

Use predictive analysis to calculate customer churn by analyzing demographic and purchase data. Are your main customers aging out of your brand? Or are demographics changing in a geographic area where your business is high? When you use predictive analysis to keep a constant eye on your customers you can pivot your business to avoid churn or locate a potential new location for growth.

How to Analyze your Data

You’ll need to implement a data analysis process to get the most out of your data. While it can be complex to perform data analysis, depending on the type of data you’re analyzing, there are some hard and fast rules that you can follow. They include setting goals, collecting, cleaning, and analyzing data, then visualizing it in striking dashboards to make it easy to spot patterns and trends.

Below, we’ve outlined the seven steps you’ll need to follow to analyze your data:

1. Define Your Goals: Setting clear objectives is key and will help determine the type of data that you’ll need to collect and analyze.

2. Collect Your Data: Data is everywhere, and you’ll want to bring it all into one place ready for analysis. Whether you’re collecting quantitative or qualitative data, Excel is a great platform for storing your data, or you could connect data sources directly to your analysis tools via APIs and integrations.

3. Clean Your Data: It’s likely that unstructured data will need to be cleaned before analyzing it to gain more accurate results. Get rid of the noise, like special characters, punctuation marks, stopwords (and, too, she, they), HTML tags, duplicates, etc.

4. Integrate Data Analysis Tools: Most tools can easily be integrated via APIs and one-click integrations. If using an API, you might need a developer’s help to set it up. Once connected, your data can run freely through data analysis tools.

5. Analyze Your Data: Now that you’ve connected data analysis tools, you’ll need to choose the analysis type you want to perform (which you probably defined in step one).

6. Visualize Your Data: Dashboards are a great way to aggregate your data, and make it easy to spot trends and patterns. Some data analysis tools have in-built dashboards or you can connect to your existing BI tools.

7. Draw Conclusions: Gain actionable insights and make data-based decisions by digging into your data from every angle.

Get Started with Data Analysis

There is almost no end to the possibilities of data analysis when you know how to do it right. Whether quantitative or qualitative, there are a number of analytical solutions and pathways to get real insights from your data.

Performing text analysis on your unstructured text data can offer huge advantages and potential advancements for your company, whether it comes from surveys, social media, customer service tickets – the list goes on and on. There is a wealth of information to be gathered from text data you may not have even considered.

Start analyzing your unstructured data

MonkeyLearn offers dozens of easy-to-use text analysis tools that can be up and running in just a few minutes to help you get the most from your data. Schedule a demo to see how it works.

Inés Roldós

January 9th, 2021

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