Data is your most important business asset. And, yet, most of it is probably sitting around unused. On average, up to 55% of data collected by companies goes unused for analysis.
But why is that?
Perhaps you don’t know what to do with it. Or, you don’t know what you want to achieve by going through all your data. Maybe, you know how valuable it is, but you don’t have the tools to organize, process, and analyze your data.
Modern-day data analysis methods combine various techniques and tools, from data mining and text analytics to business intelligence (BI) and data visualization. There are simple data analysis tools, like Excel, to advanced AI data analysis tools that can extract impressive insights with very little human intervention. They all play an important role in helping companies improve their processes and decision-making.
In this article, we’ll explain the different types of data you can analyze, describe the data analysis process, and why data analysis is important for making smarter decisions. We’ll also outline the various data analysis techniques and the steps to analyze data.
Data analysis is the process of cleaning, analyzing, interpreting, and visualizing data using various techniques and business intelligence tools. Data analysis tools help you discover relevant insights that lead to smarter and more effective decision-making.
You’ll often see the terms data analysis and data analytics used interchangeably. But, there’s a slight difference between the two.
Data analytics is the overarching discipline and refers to the whole process of data management: data collecting, storing, organizing, and analyzing. It includes the 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.
There are two types of data that companies can analyze:
Traditionally, companies have focused more on quantifiable data because it’s easier to analyze through aggregation, regression analysis, and statistical analysis. However, with breakthroughs in linguistic and structural data analysis techniques, companies are now able to mine qualitative data for even more granular insights – adding an extra layer of analysis to their data.
Quantitative data deals with whole numbers, percentages, and statistics data analysis – data that can be expressed as a quantity.
Qualitative data deals with features or characteristics – data that describes something.
This can also be expressed as the two main data analysis types: structured and unstructured data. Quantitative data is usually structured data that is pre-formatted and fits neatly into the columns and rows of spreadsheets.
Qualitative data is usually unstructured data that has no format or pre-configured design, things like text, audio, and images.
In short, quantitative analysis lets you know what’s happening, and qualitative analysis tells you why it’s happening.
Quantitative data responds to questions like “how many?”, “how often?” and “how much?”. This data is close-ended and concise, making it ideal for data analytics.
Structured query language (SQL) is a programming language used to communicate with and organize databases and particularly useful when handling quantitative data. Programs like Google Sheets and Excel are data analysis tools that work well with quantitative data because it must fit into their pre-determined formats.
Examples of quantitative data analysis:
Qualitative data, on the other hand, is expressed in words. It allows you to understand the “why?” behind numbers and statistics and provides insights into behavior and patterns.
Machine learning data analysis platforms, like MonkeyLearn are great for qualitative data because they use techniques like natural language processing (NLP) to break down unstructured data, so that it can be understood and analyzed by machines.
General purpose programming languages, like Python, are ideal for qualitative data analysis because they are much more open-ended and can be used to call or integrate with a variety of data analysis tools.
Examples of qualitative data analysis::
To analyze these different types of data, you’ll need to use different data analysis methods and techniques.
The six top methods for data analysis:
The goal of text analysis is to transform natural spoken or written human language into machine-readable data. By understanding subtle nuances, like emotions and context, text analysis tools can sort qualitative data by topic, emotions, keywords, and intent. First, though, you’ll need to train machine learning algorithms to understand the subtleties of human language before they can make decisions independently.
For example, the word ‘bright’ has different meanings depending on its context:
The sun is very bright
She’s extremely bright
You’ve brightened up my day
By tagging text examples and feeding them to text analysis tools, machines will be able to understand different meanings of the same word and their context when analyzing unseen text.
Businesses can use this type of analysis to detect customer needs. Topic analysis, for example, can automatically sort customer service tickets by topic and immediately route them to the proper department. And sentimentsentiment analysis can automatically read through thousands of social media posts for “opinion polarity” (positive, negative, neutral, etc.) to understand what customers are saying about your brand online.
Data mining, also known as knowledge discovery in data (KDD) is the process of detecting anomalies, patterns, and relationships to predict outcomes.
It involves techniques like clustering (grouping similar objects), sequential pattern mining (finding frequent sequences of objects or events), and anomaly detection (spotting outliers, rare items, or unusual records).
Applications of data mining include predicting how markets will behave, allowing businesses to anticipate customer needs and be proactive.
Statistical analysis, used on its own, involves exploring and presenting huge amounts of quantitative data to detect trends and patterns. While statistical analysis includes a wide range of techniques, we can split them into two main categories:
Descriptive statistics data analysis describes describes, summarizes, and visualizes the basic features of data, through charts and reports. There are four main measures of descriptive statistics that form the basis of nearly every quantitative data analysis:
Inferential statistics data analysis is used to draw conclusions about an entire population (whether a class of students or the entire population of a country), based on samples that accurately represent the population.
The most common methodologies of inferential statistics are hypothesis tests and estimation theories.
Statistical analysis is a great way for organizations to carry out market research. Regression analysis, for example, is a statistical technique that calculates the relationship between two (or more) variables, like the number of visitors to your site and money spent on marketing. This will help you detect the precise impact of a change in marketing spend.
Diagnostic analysis, also known as root cause analysis, aims to answer the question: “Why did something happen?” It’s an exploratory type of analysis that identifies anomalies and uncovers patterns and stories in your data.
Maybe you’ve noticed a sudden drop in customer complaints. But, why is that? Did you improve your customer support process, hire new staff, or improve a certain feature? These are all valid questions and diagnostic analysis will help you determine whether there’s a high or low correlation between data points and possible causes.
Building a predictive analysis model can be a true game-changer for organizations. This type of analysis allows you to combine demographic data, purchase data, and other sources of information to answer: “What is likely to happen in the future?”
By using this type of analysis, you can anticipate customer needs, predict customer churn, and determine how many leads can be converted to sales. For governments, predictive analysis is increasingly important because it allows them to target likely problems before they snowball.
Prescriptive analysis combines all the data and insights that you have and turns them into actionable insights. In a nutshell, it shows the best course of action to follow in a given scenario.
This type of analysis works by analyzing different scenarios and determining the most likely outcome of each. This way, you can make a decision based on what you hope to achieve.
This is the most advanced type of data analysis. Governments use prescriptive analysis to gain a better understanding of the likelihood of worst-case scenarios, where the cost of human error is high. Based on the results from this type of analysis, they can create an effective response plan.
Prescriptive analysis is also ideal for marketing campaigns to combine all customer analytics, past marketing data, and competitive analysis to decide the best course of action.
Read more about data analysis examples and applications.
From preparing for worst-case scenarios to improving services and products, all types of data analysis can help businesses make better decisions and create data-driven strategies.
By gaining first-hand insight into what’s wrong and why, leaders can define more effective strategies to improve processes, prevent problems, detect growth opportunities, and decide where to focus investments.
Among other things, data analysis allows you to:
Analyzing historical and real-time data can reveal trends and patterns, and help you predict what will happen next.
Companies providing a service, for example, might analyze customer sentiment on social media to detect unhappy customers and predict when they’re at risk of churning. Then target these customers with promotions or perks in an attempt to retain them.
Another great way to predict customer behavior is by sending out surveys at each stage of the customer journey. Analyzing the open-ended responses from a batch of survey responses can help you identify problem areas, predict if customers are about to churn, and even anticipate your customers’ needs.
Data analysis can help you improve daily processes and increase your team’s productivity while boosting public satisfaction.
By analyzing customer support tickets, you can detect keywords and topics, read for degree of urgency, and automatically route them to the correct teams right away. This will also help you uncover common customer complaints and questions, which might incite you to create a self-service knowledge base. Maybe, complaints mention how slow first-response times are, in which case you might implement a chatbot to solve up to 80% of routine queries.
Imagine you work for a private healthcare company and you receive a bunch of complaints about failed appointment bookings via your app. By carrying out data analysis you can detect the solution to the problem right away, and fix it before you receive even more complaints.
By using data analysis to uncover problems that lead to delays and productivity loss, you can take concrete steps to create solutions.
Instead of relying on intuition or experience, data gives you solid evidence to support decisions.
Customer feedback, for instance, is a goldmine of real-time insights that you can use to build data-driven initiatives, product roadmaps, and streamlined services. How does the public feel about your response to climate change? Is there a feature that you could add to improve your app? What frustrates customers most about your service?
Slack often makes decisions based on customer feedback, from social media, personal interviews, and online surveys, to understand how business teams use and interact with their product. They then translate this insight into new features and enhanced functionalities.
Through data analysis, you can learn what’s working well (and what’s not) for your own business. But you can also detect the weaknesses and strengths of your competition, uncovering opportunities for improvement, or unique angles for your marketing strategy.
Online reviews provide great insight into your competition. This public data source allows you to answer questions like: what do customers love or hate about your competitors’ products or services?
Crazy Egg, a website optimization tool, analyzes sentiment in their competitors’ reviews to understand what motivates people to choose a product or switch to another company.
Knowing exactly what customers need and expect allows you to create better products and experiences for them and stand out in a competitive market. But how do you analyze data?
The data analysis process includes setting goals, collecting, cleaning, and analyzing your data. Then using the results to make striking visualizations and draw conclusions for immediately actionable insights.
There are huge amounts of useful data from emails, chatbots, internal reports, product reviews, social media, and all over the web. It can be downright intimidating, and insights from raw data aren’t clear-cut. Fortunately, with the right steps to analyze your data and the right data analysis tools, you can be on your way to actionable insights in no time.
Start your data analysis process in just seven steps:
Every data-driven strategy should be linked to specific goals. What outcomes do you want to achieve? What specific issues would you like to address?
Whether you’re trying to gauge public sentiment, speed up response times in customer support, increase signups, or launch a new feature, having a set of clear objectives is key. Also, it will determine the type of data that you’ll need to collect and track.
Your goals should be aligned with the overall business objectives and follow the SMART criteria, meaning they should be specific, measurable, achievable, relevant, and time-bound.
Once you’ve set up your goals, it’s time to collect relevant data from various sources. Qualitative data sources can be found both internally and externally:
Collected data that belongs to your company, which can be found in:
In most cases, it’s quite simple to export data from these tools into a CSV or Excel file. Even better, you can connect these data sources directly to data analysis tools through APIs or integrations step 4).
Data generated outside your organization:
While it may seem more difficult to capture this data, there are tools that can help you get the job done:
Before analyzing your data, you first need to clean your dataset ‒ especially when working with unstructured text.
This means that you need to get rid of the noise that usually appears in text-based data. For example, if you are classifying a series of product reviews, you should remove special characters, punctuation marks, stopwords (and, too, she, they), HTML tags, etc.
Correcting spelling, removing abbreviations, and applying lowercase to all your text will also help when data cleaning.
Finally, if your customer feedback contains large chunks of text, we recommend using a tool to split your data into opinion units (or, smaller chunks of text). That way, you can analyze individual clauses and gain more granular results.
Paragraphs are likely to contain more than one sentiment and refer to more than one topic, like in this example:
Instead of investing hours manually tagging customer data, you can use text analysis tools and get the job done in minutes, no matter the size of your dataset.
Depending on your goals, you may opt for pre-trained text analysis solutions ‒ like this sentiment analyzer that analyzes text for opinion polarity:
You can even build fully customized models to suit specific needs.
No-code, low-code SaaS tools, like MonkeyLearn, are a user-friendly data analysis solution and allow you to start immediately. And they’re often simple to connect to your existing stack with APIs or integrations.
Through APIs, you can connect machine learning models directly to your data source and analyze data in real-time.
Data analysis software helps you understand and interpret qualitative data to achieve your goals.
This is the most important step in the data analysis process when you extract value from your data. Once you’ve integrated data analysis tools, you’ll be able to put them to work on your data.
You can choose text analysis tools to help you sort customer reviews by topic and sentiment, just like we did in this analysis of customer reviews mentioning Facebook.
Or, you can connect sentiment analysis tools to your helpdesks to analyze social data. This allows you to sort mentions by positive and negative, and prioritize those that are most urgent.
The text analysis models you choose will all depend on the problem you’re trying to solve.
Data visualization tools present data analysis results in a way that’s attractive, clear, and easy to understand.
By using different data sources to create interactive charts, reports, and dashboards, you summarize your data and find insights, patterns, and relationships that may not be evident in a spreadsheet.
Business intelligence (BI) software and visualization tools help you simplify your data and create engaging stories to share with other team members and stakeholders.
MonkeyLearn Studio, the only all-in-one text analysis solution, will take you from data collection, to analysis, and, ultimately, data visualization in just a few steps.
The below is a MonkeyLearn Studio dashboard showing aspect-based sentiment analysis of Zoom customer reviews. Aspect-based sentiment analysis first categorizes reviews by topic (in this case, Usability, Support, Reliability, etc.), then by sentiment, so that you end up showing which aspects of your business really shine for the customer, and which may need some work:
Other data visualization tools on the market include Tableau and Google Data Studio. However, these tools don’t include built-in text analysis solutions. MonkeyLearn Studio allows you to do it all, right in the dashboard.
Compare the results in your dashboard to the goals that you defined at the beginning of the process.
Granular insights that you gain from text analysis can be simplified in a dashboard and provide answers to questions that you set out to answer at the beginning of your analysis.
For example, why are customers switching to a competitor? How can I improve customer retention? Is a new campaign receiving good or bad press, and why?
When you’re ready to get started with data analysis, you’ll find that there are a couple of options: build your tools from scratch or use a SaaS (software as a service) solution. It’s the eternal build vs. buy debate.
There are many open-source libraries that developers can use to build machine learning models for data analysis. While they’re free, they’ll take time to implement and require complex and costly infrastructure, not to mention large upfront investments. You’ll probably end up hiring a team of developers and data scientists if you decide to build your own solution.
SaaS tools, on the other hand, are a simple and cost-effective alternative. These tools are cloud-based and ready-to-use, allowing you to perform a variety of tasks, from text analysis to data visualization, in next to no time.
Some of the benefits of buying SaaS tools over building your text analysis software include:
Lower costs & easily scalable. While open-source software is free, you’ll need a team of machine learning experts to set up the infrastructure, build complex algorithms, and test your tools. SaaS tools, on the other hand, are ready to use solutions and no-code, low-code options make it easy for non-technical users to get started with data analysis tools. That means you don’t need to hire expert staff, and you won’t waste time waiting for a solution to be built. You can easily scale up or down with SaaS tools, which offer various plans depending on the amount of data you need to analyze.
Quick to set up. In such a competitive market, businesses need to move fast to stay relevant. With SaaS tools, you don’t have to spend months developing and fine-tuning your machine learning models. Instead, it takes less than a month to implement out-of-the-box data analysis solutions, so you can start gaining insights and creating better customer experiences right away.
Maintenance costs included. When you build your own data analysis software, you need to take into account maintenance costs. You’ll need to keep a permanent team of experts on to tweak hyperparameters, update models, and maintain on-site infrastructure. If you choose to buy cloud-based data analysis software, you won’t have to worry about additional high maintenance costs. They’re included in the monthly license fee. And, since SaaS data analysis tools are in the cloud, there’s no physical software to maintain or repair.
Boasting an intuitive no-code interface, MonkeyLearn Studio allows you to start analyzing and visualizing data right away.
Request a demo then follow the steps below:
1. Choose a template. Go to the dashboard and choose a template that best fits your use case. Or, create your own. Each template combines different pre-trained text analysis models depending on the type of analysis you want to carry out.
You can also build your custom models in a simple graphic user interface – completely code-free. This is the best option If you require data analysis tools that can understand domain-specific vocabulary.
2. Import your data. Upload an Excel or CSV file with your data, or connect to one of the many data integrations (such as Twitter, Zendesk, or Gmail).
3. Run the analysis.
Once you’ve uploaded your data, your analysis will begin.
4. Visualize your data.
You’ll be able to visualize the results of your analysis in real-time, in a customizable and striking dashboard.
Data analysis helps businesses explore customer data and find insights to support and guide their decision making.
With data analysis tools, businesses can sift through large amounts of qualitative data in just minutes. This frees up staff from repetitive and time-consuming tasks and helps them obtain real-time insights about their customers. Knowing exactly what makes your customers tick helps you design better experiences for them, and provides you with a competitive advantage.
Start gaining more granular insights by combining different data analysis techniques and connecting your results to data visualization tools.
SaaS tools, like MonkeyLearn Studio, make it easy to build custom data analysis solutions and integrate them with your apps. Take a look at MonkeyLearn Studio’s public dashboard, play around a bit, and see just how easy it is to use.
Ready to get started? Request a demo and discover how you can power up your data.
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