Author: Tom Perkins

Data Analysis Methods

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:

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.

Example on how to use intelligent alerts from datapine

**click to enlarge**

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.

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How To Analyze Data? Top 15 Data Analysis Techniques To Apply

Your Modern Business Guide To Data Analysis Methods And Techniques

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.

Data Analysis Process

The Data Analysis Process is nothing but gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.

Data Analysis consists of the following phases:

Data Requirement Gathering

Data Collection

Data Cleaning

Data Analysis

Data Interpretation

Data Visualization

Data Requirement Gathering

First of all, you have to think about why do you want to do this data analysis? All you need to find out the purpose or aim of doing the Analysis of data. You have to decide which type of data analysis you wanted to do! In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.

Data Collection

After requirement gathering, you will get a clear idea about what things you have to measure and what should be your findings. Now it’s time to collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis. As you collected data from various sources, you must have to keep a log with a collection date and source of the data.

Data Cleaning

Now whatever data is collected may not be useful or irrelevant to your aim of Analysis, hence it should be cleaned. The data which is collected may contain duplicate records, white spaces or errors. The data should be cleaned and error free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.

Data Analysis

Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements.

Data Interpretation

After analyzing your data, it’s finally time to interpret your results. You can choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart. Then use the results of your data analysis process to decide your best course of action.

Data Visualization

Data visualization is very common in your day to day life; they often appear in the form of charts and graphs. In other words, data shown graphically so that it will be easier for the human brain to understand and process it. Data visualization is often used to discover unknown facts and trends. By observing relationships and comparing datasets, you can find a way to find out meaningful information.

Summary:

Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making

Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis

Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization

What is Data Analysis? Research | Types | Methods | Techniques

What is Data Analysis?

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.

Why Data Analysis?

To grow your business even to grow in your life, sometimes all you need to do is Analysis!

If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business to grow more. All you need to do is analyze your business data and business processes.

Data Analysis Tools

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of tools used for data analysis in research.

Types of Data Analysis: Techniques and Methods

There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:

  • Text Analysis
  • Statistical Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis

Text Analysis

Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools. It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.

Statistical Analysis

Statistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.

Descriptive Analysis

analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.

Inferential Analysis

analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.

Diagnostic Analysis

Diagnostic Analysis shows “Why did it happen?” by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems.

Predictive Analysis

Predictive Analysis shows “what is likely to happen” by using previous data. The simplest data analysis example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course it’s not easy like this because you have to think about other circumstances like chances of prices of clothes is increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house!

So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it.

Prescriptive Analysis

Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analysis are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.

Data Analysis: What, How, and Why to Do Data Analysis for Your Organization

Being a data-driven business is important, but what does that mean exactly?

Data-driven businesses make decisions based on data, which means they can be more confident that their actions will bring success since there is data to support them.

So what is data analysis?

In simple words, data analysis is the process of collecting and organizing data in order to draw helpful conclusions from it. The process of data analysis uses analytical and logical reasoning to gain information from the data.

The main purpose of data analysis is to find meaning in data so that the derived knowledge can be used to make informed decisions.

How is data analytics used in business?

Data analytics is used in business to help organizations make better business decisions. Whether it’s market research, product research, positioning, customer reviews, sentiment analysis, or any other issue for which data exists, analyzing data will provide insights that organizations need in order to make the right choices.

Data analytics is important for businesses today, because data-driven choices are the only way to be truly confident in business decisions. Some successful businesses may be created on a hunch, but almost all successful business choices are data-based.

What are examples of data analysis?

Data analysis is a somewhat abstract concept to understand without the help of examples. So to better illustrate how and why data analysis is important for businesses, here are the 4 types of data analysis and examples of each.

Descriptive Analysis: Descriptive data analysis looks at past data and tells what happened. This is often used when tracking Key Performance Indicators (KPIs), revenue, sales leads, and more.

Diagnostic Analysis: Diagnostic data analysis aims to determine why something happened. Once your descriptive analysis shows that something negative or positive happened, diagnostic analysis can be done to figure out the reason. A business may see that leads increased in the month of October and use diagnostic analysis to determine which marketing efforts contributed the most.

Predictive Analysis: Predictive data analysis predicts what is likely to happen in the future. In this type of research, trends are derived from past data which are then used to form predictions about the future. For example, to predict next year’s revenue, data from previous years will be analyzed. If revenue has gone up 20% every year for many years, we would predict that revenue next year will be 20% higher than this year. This is a simple example, but predictive analysis can be applied to much more complicated issues such as risk assessment, sales forecasting, or qualifying leads.

Prescriptive Analysis: Prescriptive data analysis combines the information found from the previous 3 types of data analysis and forms a plan of action for the organization to face the issue or decision. This is where the data-driven choices are made.

These 4 types of data analysis can be applied to any issue with data related to it. And with the internet, data can be found about pretty much everything.

But how do you get that data from the web into a usable format for your team to derive insights from? We’ll tell you in the next section about data analysis methods.

What are the methods of data analysis?

Since our expertise at Import.io is in data from the web, we’ll discuss the methods of analysis for data from the web. The steps leading up to web data analysis are: identify, extract, prepare, integrate, and consume. In traditional manual data analysis each of these steps take a substantial amount of time to perform.

Identifying the data you need can be challenging with the vast amount of data on the web. You may choose a data source that isn’t reliable or miss crucial data sources that should be part of your research. Reliable and complete data is necessary for accurate data analysis.

Extracting data from the web has traditionally required a web scraper that is coded to scrape data from a certain website according to certain parameters. For example, traditional Twitter sentiment analysis might use a web scraper that is coded to scrape tweets that mention your brand name. Creating and running these web scrapers takes time. And even once it’s finished, it’s possible the data could be incomplete or inaccurate. The parameters for which tweets will be scraped could be missing a rule, resulting in missing crucial data.

Preparing data for analysis requires many steps that each take a long time to do manually. The data must be cleansed, standardized, transformed, etc. This is where a lot of the outdating happens. By the time the data is ready, it is not as recent and there is newer data out there.

Integrating the data with your data analysis software can be an issue depending on which software your organization uses. And it needs to be integrated so that it can be consumed.

How to make data analysis more efficient for your organization

You know that the main purpose of data analysis is to make business decisions that are backed by data, so why would you let this process take so long that the insights are outdated by the time you get them?

Import.io knows that traditional web scraping and data analysis methods are time consuming to the point where their value is diminished by the time they take. That is why we created Web Data Integration.

Web Data Integration automates all 5 steps of web data analysis, allowing you to get insights from data while it’s fresh. Rather than outdated insights as a base for your business decisions, you can use insights from real-time data.

Web Data Integration is not only quicker than traditional web data analysis, but is also more accurate and reliable. Rather than using hand-coded rules to extract the web data, WDI has built-in quality control, so the data will always be complete, accurate, and reliable.

Make data analysis more efficient for your organization by eliminating inefficient processes. Get data insights in minutes rather than hours, days, weeks, or months.

Contact a data expert to learn how your organization can utilize Web Data Integration.