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What is Data Analysis? Types, Methods and Techniques Explained – A Complete Beginners Guide to Data Analysis

A beginners guide to data analysis - what is data analysis, types, methods and how to analyze data

The main purpose for analyzing data is to extract useful information from raw data and then take a decision based on facts from the analyzed data, since data analysis relies on methods and techniques to take raw data and mine them for insights that are relevant to the business’s primary goals.

What is Data Analysis?

Data analysis is a process of collecting, inspecting, cleaning, transforming, and modeling data to discover useful information and get helpful conclusions for business decision-making. A person who evaluates data using analytical or statistical tools to discover useful information is known as a Data Analyst. The process of presenting data in visual form is known as Data Visualization.

Why Data Analysis?

Data-driven businesses and organizations constantly make decisions based on data and facts, this way, they can be more confident in taking actions guaranteed to bring success, since there is available data to support them.

image of data and analytical research to explain why businesses do data analytics

Since many individuals, organization and businesses rely on helpful data and facts, through research to make more strategic decisions that helps their businesses and organization operate more effectively so as to avoid making avoidable mistakes, a data analyst is required to perform data analysis and analyze data (typically using data analysis tools), gather all useful information and provide all necessary details needed to make the right decisions.

Data analysis today is used by small businesses, off and online retail companies, in medicine, and even in the world of sports.

Methods and Techniques

While a variety of methods are used for data analysis including data mining, text analytics, business intelligence, combining data sets, and data visualization, they are all based on two major categories namely; Qualitative and Quantitative Analysis.

This video from YouTube will further help you understand better the difference between quantitative and qualitative data analysis methods.

Difference between quantitative and qualitative data

Qualitative Data Analysis

Qualitative analysis is a method of data analysis that mainly answers questions seeking answers. Questions such as why, what or how, are usually addressed via quantitative techniques such as questionnaires, standard outcomes, attitude scaling and more.

These types of analysis are mainly in the form of texts documents and narratives, and can sometimes include the use of audio and video representations.

Quantitative Data Analysis

Generally, this analysis is measured in terms of numbers. The data here present themselves in terms of measurement scales and extend themselves for more statistical manipulation.

Types of Data Analysis

There are several types of Data Analytics methods and techniques that are in use across all industries based on business and technology needs.

vector image of a group of analysts working on different types of data analytics for business to explain the types of data analysis

However, the five major types of data analysis are:

  • Text Analysis.
  • Statistical Analysis: Inferential and Descriptive Analysis.
  • Diagnostic Analysis.
  • Predictive Analysis.
  • Prescriptive Analysis.

1. Text Analysis

Also referred to as data mining, text analysis is a technique to analyze texts to extract machine-readable facts and discover a pattern in large data sets using databases or data mining tools. The main objective of text analysis is to create structured data out of unstructured and free content into business information.

2. Statistical Analysis

As the name implies, statistical analysis is the technique of performing several statistical operations such as collection, Analyzing, interpretation, presentation, and modeling of data to quantify the data, know what happened from past data and then apply statistical methods.

To perform statistical analyses, various tools such as SAS (Statistical Analysis System), SPSS (Statistical Package for the Social Sciences), Stat soft, and more are required.

illustration image of a business data statistics

The data in this type of analysis is usually descriptive; like surveys and observational data. Many data analyst like to refer to it as a descriptive analysis, even though there are two categories of this type of Analysis namely; Descriptive and Inferential Analysis.

Descriptive Analysis

Descriptive analysis is the simplest and most common use of data in businesses today, because it answers the “what happened” type of questions by summarizing past data, usually in the form of dashboards. It analyses a complete data or sample of summarized numerical data and shows the mean and deviation for continuous data – percentage and frequency for categorical data.

A major function of descriptive analysis in business is to track Key Performance Indicators (KPIs) which describe how a business is performing based on chosen benchmarks.

Business applications of descriptive analysis include:

  • KPI dashboards
  • Monthly revenue reports
  • Sales leads overview

Inferential Analysis

Inferential analysis analyses data samples from complete data. With inferential Analysis, it becomes possible to find different conclusions from the same data, simply by selecting different samples.

3. Diagnostic Analysis

While descriptive analysis shows what happened, Diagnostic Analysis tries to know “Why it happened” by finding the cause based on the insight found from descriptive analytics and then narrows it down to find the causes of those outcomes.

vector image of office workers analyzing and researching business data for diagnostic analysis

Also referred to as root cause analysis, with processes like data discovery, mining and drill down, diagnostic analysis is a step further to statistical analysis to provide more in-depth information to answer the questions.

A major function of this type of analysis is to identify behavior patterns of data.

If you encounter a new problem in your business process, this Analysis helps you find similar patterns of that problem and may have chances to use similar prescriptions for the new problems.

Business applications of diagnostic analysis include:

  • A freight company investigating the cause of slow shipments in a certain region
  • A SaaS company drilling down to determine which marketing activities increased trials

4. Predictive Analysis

Predictive analysis is used to make predictions based on current or past data. It uses the data summarized from descriptive and diagnostic analyses to make logical predictions of the outcomes of events to know what is likely to happen.

stock market prediction analysis illustration for predictive analysis

With predictive analysis, it becomes important to note that forecasting is only an estimate; the accuracy of predictions depends on quality and detailed data.

Business applications of predictive analysis include:

  • Risk Assessment and Fraud Detection
  • Sales Forecasting and Marketing Campaigns optimization
  • Using customer segmentation to determine which leads have the best chance of converting
  • Operations Improvement: Forecasting inventory and managing resources helps improve business operations. For example, airlines use predictive analysis to set ticket prices.

5. Prescriptive Analysis

Prescriptive Analysis combines insight from descriptive, diagnostic and predictive analysis accordingly to determine what line of action to take to either solve a current problem or make a strategic business decision. It emphasizes more actionable insights instead of data monitoring.

Prescriptive Analysis business growth steps chart concept image

While descriptive analytics aims to provide insight into what has happened, Diagnostic analytics to explain why it happened and predictive analytics helps model and forecast what might happen, prescriptive analytics aims to determine the proper solution or outcome among various choices, since the parameters are known.

A perfect example of prescriptive analytics is Artificial intelligence (AI), since the AI systems consume a large amount of data for continuous learning and then use the information, data or pattern learned to make informed decisions. Currently, most of the big data-driven companies (Apple, Facebook, Netflix, etc.) are utilizing prescriptive analytics and AI to improve decision making.

Data Analysis Process (Step-by-step video tutorial)

Here is a video tutorial showing step by step data analysis process and how to do data analysis as a beginner.

A beginner’s guide to data analysis process

Further Readings on ITTVIS

ENVI software for image processing and analysis.
IDL interactive data visualization solution.

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