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In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.
Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery, improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.
With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.
To help you understand the potential of analysis, the meaning, and how you can use it to enhance your business practices, we will answer a host of important analytical questions. Not only will we explore data analysis methods and techniques, but we’ll also look at different types of data analysis while demonstrating how to perform analysis in the real world with a 15-step blueprint for success.
What Is Data Analysis?
Data analysis is the process of collecting, modeling, and analyzing data to extract insights that support decision-making. There are several methods and techniques to perform analysis depending on the industry and the aim of the analysis.
All these various methods for data analysis are largely based on two core areas: quantitative methods and qualitative methods in research.
To explain the key differences between qualitative and quantitative data, here’s a video for your viewing pleasure:
Gaining a better understanding of different techniques for data analysis, and methods in quantitative research as well as qualitative insights will give your information analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis processes.
Why Is Data Analysis Important?
Before we go into detail about the categories of data analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.
Let’s start with customers, arguably the most crucial element in any business. By using data analysis to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more.
In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.
From a management perspective, you can also benefit from analyzing your data as it helps you make business decisions based on facts and not simple intuition. For example, you can understand where to invest your capital, detect growth opportunities, predict your incomes, or tackle uncommon situations before they become problems.
Like this, you can extract relevant information from all areas in your organization, and with the help of a dashboard software, present the data in a professional and interactive way to different stakeholders.
7 Essential Types Of Data Analysis Methods
Before diving into the seven essential types of data analysis methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive analysis up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.
a) Descriptive analysis - What happened.
The descriptive analysis method is the starting point to any analytic process, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights to your business.
Performing descriptive analysis is essential, as it allows us to present our data in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, but it will leave your data organized and ready to conduct further analysis.
b) Exploratory analysis - How to explore data relationships.
As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there's still no notion of the relationship between the data and the variables. Once the data is investigated, the exploratory analysis enables you to find connections and generate hypotheses and solutions for specific problems. A typical area of application for exploratory analysis is data mining.
c) Diagnostic analysis - Why it happened.
One of the most powerful types of data analysis. Diagnostic data analytics empowers analysts and business executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.
Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics, e.g.
c) Predictive analysis - What will happen.
The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Like this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.
With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge on the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.
e) Prescriptive analysis - How will it happen.
Another of the most effective types of data analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.
By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key business areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics, and others.
Without further ado, here are the seven essential types of data analysis methods among with some use cases in the business world:
1. Cluster analysis
The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.
Let's look at it from a business perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.
2. Cohort analysis
This type of data analysis method uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this data analysis methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.
Cohort analysis can be really useful to perform analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up to your site. For this you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.
A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide. In the bottom image you see an example of how you visualize a cohort analysis in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.
3. Regression analysis
The regression analysis uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how they developed in the past, you can anticipate possible outcomes and make better business decisions in the future.
Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Like this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.
If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.
4. Neural networks
The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of data-driven analytics that attempts, with minimal intervention, to understand how the human brain would process insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.
A typical area of application for neural networks is predictive data analysis. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced data scientist.
Here is an example of how you can use the predictive analysis tool from datapine:
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5. Factor analysis
The factor analysis, also called “dimension reduction,” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal analysis method for streamlining specific data segments.
A good example to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, place where they bought the product, frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes to the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.
If you want to start analyzing data using factor analysis we recommend you to take a look at this practical guide from UCLA.
6. Data mining
A method of analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, data patterns, and trends to generate and advanced knowledge. When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.
An excellent example of data mining is datapine intelligent data alerts. With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs, you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.
In the following picture you can see an example of how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions and revenues, the alarms will notify you if the goal was not completed or if it exceeded the expectations.
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7. Text analysis
Text analysis, also known in the industry as text mining, is the process of taking large sets of textual data and arranging it in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your business and use it to develop actionable insights that will propel you forward.
Modern data analyst tools and techniques accelerate the process of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant for your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article.
By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. One of the most effective data analysis tools and techniques you will ever invest in.
How To Analyze Data? Top 15 Data Analysis Techniques To Apply
Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different types of data analysis methods, it’s time to dig deeper into how to perform your analysis by working through these 15 essential techniques.
1. Collaborate your needs
Before you begin analyzing your data or drill down into any analysis techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.
2. Establish your questions
Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important data analytics techniques as it will shape the very foundations of your success.
To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions.
3. Data democratization
After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with data democratization.
Data democratization is a process that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.
Once you have decided on your most valuable data sources, you need to take all of this information into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external data sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your business.
4. Clean your data
After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This process is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.
There are many things that you need to look for while cleaning your data. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of data. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.
Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors.
Most importantly, the aim of cleaning your data is to prevent you from arriving at false conclusions that can damage your business in the long run. By using clean data, you will also help business intelligence tools to interact better with your information and create better reports for your organization.
5. Set your KPIs
Once you’ve set your data sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.
KPIs are critical to both analysis methods in qualitative and quantitative research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.
To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI: transportation-related costs. If you want to see more go explore our collection of key performance indicator examples.
6. Omit useless data
Having bestowed your data analysis techniques and methods with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.
Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.
Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.
7. Build a data management roadmap
While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.
Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.
8. Integrate technology
There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.
Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present the information in a digestible, visual, interactive format from one central, live dashboard. A data analytics methodology you can count on.
By integrating the right technology for your statistical method data analysis and core data analytics methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.
For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing data, glance over our selection of dashboard examples.
9. Answer your questions
By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.
10. Visualize your data
Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the business to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.
The purpose of analyzing your data is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard.
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This visual, dynamic, and interactive online dashboard is designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.
In detail, this dashboard displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports.
The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the business exponentially.
11. Interpretation of data
We already dedicated an entire post to data interpretation as it is a fundamental part of the data analysis process. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations.
To help you through the process of data interpretation, here we list three common practices that you need to avoid at all costs when looking at your data:
- Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing data interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation.
- Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem for a business, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire data analysis process is finalized.
- Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.
12. Build a narrative
Now that we’ve discussed and explored the technical applications of data-driven analysis, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.
The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools, you should strive to tell a story - one with a clear-cut beginning, middle, and end.
By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.
13. Consider autonomous technology
Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.
Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.
At the moment, these technologies are revolutionizing the data analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.
14. Share the load
If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.
Modern data dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.
Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.
15. Data analysis tools
In order to perform high-quality data analysis, it is fundamental to use tools and softwares that will ensure the best results. As the analysis industry grows, so does the offer for services and features that you can exploit. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.
- Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Like this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible for beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge data analysis, KPIs visualization, live dashboards, and reporting, as well as artificial intelligence technologies to predict trends and minimize risk.
- Statistical analysis: These tools are usually designed for data scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the analysis industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better business decisions. SPSS also works as a cloud service that enables you to perform analysis anywhere.
- SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these ones are popular among data scientists as they are extremely effective to unlock the value of the information in these databases. Without a doubt, one of the most used SQL software's in the market is MySQL Workbench This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, along with administration tools and visual performance dashboards to keep track of KPIs.
- Data Visualization: Data visualization tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits for business purposes. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.
Data Analysis In The Big Data Environment
Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.
To inspire your efforts and put the importance of big data into context, here are some insights that you should know – facts that will help shape your big data analysis techniques.
- By 2023 the industry of big data is expected to be worth approximately $77 billion.
- 94% of enterprises say that analyzing data is important for their business growth and digital transformation.
- Companies that exploit the full potential of their data can increase their operating margins by 60%.
- We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.
Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.
Key Takeaways From Data Analysis
As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.
7 Essential Types of Data Analysis Methods:
- Cluster analysis
- Cohort analysis
- Regression analysis
- Factor analysis
- Neural Networks
- Data Mining
- Text analysis
Top 15 Data Analysis Techniques:
- Collaborate your needs
- Establish your questions
- Data democratization
- Clean your data
- Set your KPIs
- Omit useless data
- Build a data management roadmap
- Integrate technology
- Answer your questions
- Visualize your data
- Interpretation of data
- Consider autonomous technology
- Build a narrative
- Share the load
- Data Analysis tools
We’ve pondered the data analysis meaning and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.
Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting.