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.
Because data analysis relies on methods and techniques to take raw data and mine them for insights that are relevant to a business’s primary goals, this practice has increasingly become very crucial for modern business growth.
In this guide, you will learn what data analysis is, types of data, methods and techniques in the easiest to understand language for beginners.
We have also included a video that walks you through the data analysis process step by step and added more information on what a data analyst does.
Without further ado, let’s quickly take a look at some of the most important questions on data analysis.
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.
Simply put, data analysis is the practice of working with data to get useful information, which can then be used to make informed decisions and accurate conclusions.
- 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 is important for business
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.
Expert insight: Almost every business activity and businesses today are data driven. From digital marketing to driving sales, customer management and customer product launch, every business needs the right data to better target its customers.
Better Customer Targeting
As a business owner, you don’t want to waste your business’s precious time, money and resources putting together advertising campaigns targeted at a particular group or demographic that are not interested in the goods and services you offer.
Data analysis helps you see where you should be focusing most of your advertising efforts.
Data analysis today is used by small businesses, off and online retail companies, in medicine, and even in the world of sports.
Who or what is a data analyst and what do they do?
A data analyst is a person or an individual who perform data analysis and analyze data (typically using data analytics tools and software) to gather all useful information and provide all necessary details needed for businesses or organizations to make the right decisions.
Since many individuals, organization and businesses rely on helpful data and facts to make more strategic decisions for better performance, a data analyst is required to study available data, review data and determine how to solve problems using data, learn critical insights about a business’s customers, gather all of their findings and interpret the information with insights into what should be done.
What does a data analyst do?
A data analyst reviews data to identify key insights into a business’s customers and ways the data can be used to either solve problems or grow the business. Data analyst basically gathers, cleans, and studies data sets to help solve problems.
They also communicate this information to company leadership and other stakeholders.
“Ultimately, the work of a data analyst is to study data and provide insights to the organization that can transform how the business moves forward and grows successfully,” said Dr. Susan McKenzie, senior associate dean of STEM programs at Southern New Hampshire University (SNHU).
According to McKenzie, professional data analysts have strong mathematical and statistical skills, as well as:
- Analytical skills to gather, view and analyze information.
- Numerical skills to measure and statistically analyze data.
- Technical skills including software and scripting languages to organize and present data.
What is Data Analysis in Research?
In research, data analysis is a process used by researchers to reduce and turn data into a story for better interpretation and insights. The data analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense.
LeCompte and Schensul, Professor of education and sociology at the University of Colorado and senior scientist at the Institute for Community Research
During the data analysis process in research, three essential things take place.
- Data organization.
- Summarization and categorization together contribute to becoming the second known method used for data reduction. This helps in finding patterns in the data provided, for easy identification and linking.
- Data analysis.
Researchers usually do these in both top-down or bottom-up style. For more information on data analysis in research, skip to the additional resources section.
Types of Data and Their Means of Collection for Analysis
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 data types.
There are two broad and major types of data:
- Qualitative data
- Quantitative data
Qualitative Data
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 qualitative data collection 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.
What is qualitative data?
Simply put, Qualitative data is the descriptive and conceptual findings or information collected through questionnaires, interviews, or observation. Analyzing qualitative data will allow you to explore various ideas and be able to explain quantitative results.
Sarah Ivan, MPP, former Senior Analyst at NICHQ
This type of data can’t be measured or counted. It is primarily used to determine how people feel about something, their motivations, opinions and perceptions.
Qualitative data is gathered from surveys, observations or user interviews and can sometimes be biased.
Quantitative Data
Generally, this method of data collection and analysis is measured in terms of numbers.
The data here present themselves in terms of measurement scales and extend themselves for more statistical manipulation.
This type of data can be counted, measured, and compared.
The aim of quantitative data is to answer questions such as ‘How much’, ‘How often’, ‘How many’, and ‘How long’.
The act of collecting, analyzing, and interpreting quantitative data is known as performing statistical analysis, and statistical analysis helps uncover patterns and trends in data
Qualitative Data vs Quantitative Data: What’s the difference?
The main difference between qualitative data and quantitative data is their mode of collecting data, processing the information and presenting the results. While quantitative data collection retrieves numerical data such as what, where or when, qualitative data, often presented as a narrative, collects the stories, actions and experiences of individuals.
Example:
- Quantitative Data: About 841,180 new businesses are registered every day in the USA, and 18.4% of them fail in their first year.
- Qualitative Data: During a business discussion, small business owners express that there is no time and resources allocated to properly grow their new business to be successful.
This video from YouTube will further help you understand better the difference between quantitative and qualitative data.
Types of Data Analysis (With Examples)
There are several types of Data Analytics methods and techniques that are in use across all industries depending on the individual business and technology needs.
However, there are five major types of data analysis.
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 (also called “Descriptive 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.
The data in this type of analysis is usually descriptive; like surveys and observational data.
Many data analysts like to refer to statistical analysis as a descriptive analysis, even though statical analytics has two categories which are: 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.
- Diagnostic analysis answers the question, “why did it happen?”
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.
- Predictive analysis answers the question of, “what might happen in the future?”
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 Analytics
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.
- Prescriptive analysis answers the question of, “what should we do about it?”
It emphasizes more actionable insights instead of data monitoring.
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).
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 (A Step-by-step video tutorial and guide for beginners)
Here is a video tutorial showing step by step data analysis process and how to do data analysis as a beginner.
Further Readings on ITT Visual Information Solutions
ENVI software for image processing and analysis.
IDL interactive data visualization solution.
References and additional resources
- Question Pro. “Data analysis in research: Why data, types of data, data analysis in qualitative and quantitative research, https://www.questionpro.com/blog/data-analysis-in-research/” Accessed June 24, 2022.
- National Institute for Childrens Health Quality. “Qualitative Data Collection: 7 Things Researchers Need to Know to Get it Right, https://www.nichq.org/insight/qualitative-data-analysis-7-things-researchers-need-know-get-it-right.” Accessed June 24, 2022
- World Economic Forum. “The Future of Jobs Report 2020, http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf.” Accessed January 23, 2022.
