Data is your most important business asset. And, yet, it’s 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 combines various techniques and tools, from data mining and text analytics to business intelligence (BI) and data visualization. They all play an important role in helping companies improve their processes and decision-making.
In this article, we’ll provide a definition of data analysis, the different types of data you can analyze, and why data analysis is important for making smarter decisions. We’ll also outline the various analysis techniques, and the steps you need to take to get started with data analysis.
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 collection, 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 meaningful and actionable insights.
There are two types of data that companies can analyze:
Traditionally, companies have focused more on quantifiable data, since 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.
As we mentioned, there are two types of data that you can collect and analyze: quantitative and qualitative data. In short, quantitative analysis lets you know what’s wrong, and qualitative analysis tells you why.
Quantitative data refers to quantifiable and measurable aspects, and therefore, is expressed in numbers. It responds to questions like “how many?”, “how often?” and “how much?”. This data is close-ended and concise, making it ideal for data analytics.
Examples might include:
Qualitative data, on the other hand, is expressed in words. It allows you to understand the “why?” behind the numbers and provides insight into behavior and patterns.
Examples might include:
To analyze these different types of data, you’ll need to use different data analysis techniques, which we’ll go over in more detail below.
There are several data analysis methods that you can use to discover useful information in quantitative and qualitative data, including:
The goal of text analysis is to transform natural 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, by sorting feedback by topic or sentiment. Governments can also use text analysis to sort open-ended survey responses, for example, and make investment decisions.
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.
Government data mining is particularly useful for identifying potential terrorists or other abnormal activities.
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 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 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.
Predictive analysis uses historical data to make inferences about future events.
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 outcomes for each one. This way, you can make a decision based on what you hope to achieve.
This is the most advanced form 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.
From detecting potential terrorists to improving services and products, data analysis is helping everyone become better decision makers and strategists. 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 also 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, and 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 if you work for a private healthcare company and you receive a bunch of complaints about failed booking appointments 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 government 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). But you can also detect weaknesses and strengths of your competitors, 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.
Companies are often deluged with data containing a lot of valuable insights. Unfortunately, the insights from raw data are not clear-cut. First, you’ll need to process this data before you can transform it into actionable insights.
The data analysis process involves a series of 7 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 must clean your data set ‒ 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 free sentiment analyzer ‒ or build 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 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 text 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 monitor social media mentions. 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 analysis to data visualization in just a few steps.
Other data visualization tools on the market include Tableau and Google Data Studio. However, these tools don’t include in-built text analysis solutions like MonkeyLearn Studio.
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 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?
If you want to get started with data analysis, you’ll find that there are different alternatives: building your tools or using a SaaS (software as a service) solution.
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 take 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.
Start your 14-day free trial, 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 a CSV file with your data, or connect to one of the available 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 text mining tools, businesses can sift through large amounts of qualitative data in just minutes. This frees 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. Ready to get started? Request a demo and discover how you can power up your data.
Automate business processes and save hours of manual data processing.