Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics, businesses today are working with slimmer margins for error.
Companies that want to not only stay in business but also thrive can improve their odds of success by making smart choices. And how does an individual or organization make these choices? They do it by collecting as much useful, actionable information as possible, then using it to make better-informed decisions!
This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on ignorance. Organizations need information; they need data. This need for data is why the discipline of data analysis enters into the picture.
Now, before getting into the details about the data analysis methods, let us first understand what is data analysis.
What is Data Analysis?
Although many groups, organizations, and experts have different ways to approach data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data, and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.
It’s not uncommon to hear the term “big data” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “What is data?”
What is the Data Analysis Process?
The data analysis process, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process consists of:
- Data Requirement Gathering: Ask yourself why you’re doing this analysis, what type of data analysis you want to use, and what data you are planning on analyzing.
- Data Collection: Guided by the requirements you’ve identified, it’s time to collect the data from your sources. Sources include case studies, surveys, interviews, questionnaires, direct observation, and focus groups. Make sure to organize the collected data for analysis.
- Data Cleaning: Not all of the data you collect will be useful, so it’s time to clean it up. This process is where you remove white spaces, duplicate records, and basic errors. Data cleaning is mandatory before sending the information on for analysis.
- Data Analysis. Here is where you use data analysis software and other tools to help you interpret and understand the data and arrive at conclusions. Data analysis tools include Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI.
- Data Interpretation: Now that you have your results, you need to interpret them and come up with the best courses of action, based on your findings.
- Data Visualization: Data visualization is a fancy way of saying, “graphically show your information in a way that people can read and understand it.” You can use charts, graphs, maps, bullet points, or a host of other methods. Visualization helps you derive valuable insights by helping you compare datasets and observe relationships.
Types of Data Analysis
There are a half-dozen popular types of data analysis available today, commonly employed in the worlds of technology and business. They are:
- Diagnostic Analysis: Diagnostic analysis answers the question, “Why did this happen?” Using insights gained from statistical analysis (more on that later!), analysts use diagnostic analysis to identify patterns in data. Ideally, the analysts find similar patterns that existed in the past, and consequently, use those solutions to resolve the present challenges hopefully.
- Predictive Analysis: Predictive analysis answers the question, “What is most likely to happen?” By using patterns found in older data as well as current events, analysts predict future events. While there’s no such thing as 100 percent accurate forecasting, the odds improve if the analysts have plenty of detailed information and the discipline to research it thoroughly.
- Prescriptive Analysis: Mix all the insights gained from the other data analysis types, and you have prescriptive analysis. Sometimes, an issue can’t be solved solely with one analysis type, and instead requires multiple insights.
Statistical Analysis: Statistical analysis answers the question, “What happened?” This analysis covers data collection, analysis, modeling, interpretation, and presentation using dashboards. The statistical analysis breaks down into two sub-categories:
- Descriptive: Descriptive analysis works with either complete or selections of summarized numerical data. It illustrates means and deviations in continuous data and percentages and frequencies in categorical data.
- Inferential: Inferential analysis works with samples derived from complete data. An analyst can arrive at different conclusions from the same comprehensive data set just by choosing different samplings.
- Text Analysis: Also called “data mining,” text analysis uses databases and data mining tools to discover patterns residing in large datasets. It transforms raw data into useful business information. Text analysis is arguably the most straightforward and the most direct method of data analysis.
Next, we will get into the depths to understand about the data analysis methods.
Data Analysis Methods
Some professionals use the terms “data analysis methods” and “data analysis techniques” interchangeably. To further complicate matters, sometimes people throw in the previously discussed “data analysis types” into the fray as well! Our hope here is to establish a distinction between what kinds of data analysis exist, and the various ways it’s used.
Although there are many data analysis methods available, they all fall into one of two primary types: qualitative analysis and quantitative analysis.
- Qualitative Data Analysis: The qualitative data analysis method derives data via words, symbols, pictures, and observations. This method doesn’t use statistics. The most common qualitative methods include:
- Content Analysis, for analyzing behavioral and verbal data.
- Narrative Analysis, for working with data culled from interviews, diaries, surveys.
- Grounded Theory, for developing causal explanations of a given event by studying and extrapolating from one or more past cases.
- Quantitative Data Analysis: Statistical data analysis methods collect raw data and process it into numerical data. Quantitative analysis methods include:
- Hypothesis Testing, for assessing the truth of a given hypothesis or theory for a data set or demographic.
- Mean, or average determines a subject’s overall trend by dividing the sum of a list of numbers by the number of items on the list.
- Sample Size Determination uses a small sample taken from a larger group of people and analyzed. The results gained are considered representative of the entire body.
We can further expand our discussion of data analysis by showing various techniques, broken down by different concepts and tools.
Artificial Intelligence and Machine Learning
AI is on the rise and has proven a valuable tool in the world of data analysis. Related analysis techniques include:
- Artificial Neural Networks
- Decision Trees
- Evolutionary Programming
- Fuzzy Logic
Mathematics and Statistics
This is the technique where you find number-crunching data analytics. The techniques include:
- Descriptive Analysis
- Dispersion Analysis
- Discriminant Analysis
- Factor Analysis
- Regression Analysis
- Time Series Analysis
Graphs and Visualization
We are visually oriented creatures. Images and displays attract our attention and stay in our memory longer. The techniques include:
- Charts, which break down into the following types:
o Area Chart
o Bubble Chart
o Column Charts and Bar Charts
o Funnel Chart
o Gantt Chart
o Line Chart
o Pie Chart
o Radar Chart
o Word Cloud Chart
- Frame Diagram
- Rectangular Tree Diagram
- Maps, which in turn break down into four distinct types:
o Flow Map
o Heat Map
o Point Map
o Regional Map
- Scatter Plot
How to Become a Data Analyst
If you want to pursue a career in data analytics, you should start by first researching what it takes to become a data analyst. You should follow this up by taking selected data analytics courses, such as the Data Analyst masters certification training course offered by Simplilearn.
This seven-course Data Analyst Certification Training is run in collaboration with IBM and will make you an expert in data analysis. You will learn about data analysis tools and techniques, working with SQL databases, the R and Python languages, creating data visualizations, and how to apply statistics and predictive analytics in a commercial environment.
You can even check out the PG Program in Data Analytics in partnership with Purdue University and in collaboration with IBM. This program provides a hands-on approach with case studies and industry-aligned projects to bring the relevant concepts live. You will get broad exposure to key technologies and skills currently used in data analytics.
Several years ago, IBM predicted that the demand for data analysts would soar 28% by the end of 2020, resulting in more than 360,000 new positions this year. As per the reports of Salary.com, data analysts earn an annual average of USD 75,724, hitting over USD 85,000 in the high end of the range.
So, if you want a career that pays handsomely and will always be in demand, then check out Simplilearn and get started on your new, brighter future!