How to easily analyze my data?

To understand data analysis, it is important to respect the different steps of this process, the types of data you may encounter and also the different analyses that exist to choose the most optimal one.

Photo by Markus Spiske on Unsplash

What is the goal of data analysis?

Researchers use data analysis to present accurate and reliable results, often for business. As much as possible, they seek to avoid statistical errors and find a way to deal with everyday challenges such as extraction, missing data, data modification, data mining, or graphical development.

Data analysts take raw information - numbers or sets of quality data - and use it to tell narratives that help companies make better business decisions. Your primary goal in a career as a data analyst is to take large amounts of complex data, extract insights, and help solve problems and provide answers for all your questions.

Data analysis can also be used to respond quickly to emerging market trends and gain a competitive advantage over rivals. The ultimate goal of data analysis is to increase business performance.

The term data analytics refers to the process of examining data sets in order to draw conclusions about the information they contain. Data analysis techniques allow you to take raw data and discover patterns to extract valuable insights from them.

Data analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, summarize and evaluate data. An essential component of ensuring data integrity is accurate and appropriate analysis of research findings.

What are the 6 steps to analyzing data?

6 steps to analyze your data:

  • Identify the right questions
  • Collect the right information/data
  • Clean your data
  • Visualize the data
  • Choose the appropriate method to analyze your data
  • Present the results and conclusions

The definition of analysis is the process of breaking something down into its parts in order to learn what they do and how they are interconnected.

Data lifecycle management refers to the definition and the structuring of the steps that follow information within a company to extend its lifespan. Therefore, this data management will require the use of resources offered by information technology for automatic processing.

What are the types of data analysis?

Data analysis can be separated and organized into 8 types, arranged by increasing difficulty to analyse.

  • Descriptive analysis
  • Exploratory Analysis
  • Inferential analysis
  • Predictive analysis
  • Causal analysis
  • Mechanistic analysis
  • Prescriptive analytics
  • Cognitive analytics

For all these analyses, you have to choose the most appropriate techniques and methods.

What are the 3 main types of data?

There are three types of data to analyze:

  • Short-term data: This is usually transaction data
  • Long-term data: One of the best examples of this type of data is certification or accreditation data
  • Useless data: Unfortunately, too many of our databases are filled with really useless data.

Data can be qualitative or quantitative. Once you know the difference, you can learn how to use them. Qualitative data represent characteristics or attributes, these data describe a subject and you can not measure it, the possibilities are limited. Quantitative data can be measured, they are not simply observed, it is digital data.

For example, qualitative variables could be a product, the gender of the consumers… Quantitative variables could be their age or weight, a result, it answers the questions “who” and “how many”.

6 types of data most commonly used in data analysis:

  1. Nominal data
  2. Category data
  3. Ordinary data
  4. Dichotomous data
  5. Continuous data:
    • Interval data
    • Ratio data
  6. Discrete data

How do I choose the right tool to analyze my data?

When you have questions about your variables and you do not know how to analyze these data, there are many statistical software packages that can help you to do your analysis more easily. It is often difficult to find the software that will suit you best for this data analysis. It’s easy enough to find articles and comparison sites that show the differences between 2 to 5 software packages to help you find the one that suits you best. These articles often compare the statistical capabilities of softwares but do not give any information about the helpful methods for the customer and their questions.

Our decision-support tool lists 24 different statistical solutions across six business areas. We start with needs in order to propose the best tools — the ones that will provide most or all of the functionalities you need to carry out a thorough analysis of your data. This is completely free and does not require registration.


Try our decision-making assistant!

Find out which statistical solution is best suited to your requirements and which features you will need.


Should I learn R, Python or SQL to analyze my data?

R, SQL and Python are very common programming languages in data science. Before choosing the one you will use for your analyses, it is important to know their advantages and disadvantages depending on your level.