7 Different Types of Data Analysis Methods for Aspiring Analysts.
Data analysis can be a complex subject, especially when you are new to the field. Beginners often get confused about the different types of data analysis they can do or struggle with finding the correct method for their problem.
With my background as both a data analyst and digital consultant, I know the importance of finding the right method of data analysis for different kinds of projects to save time and increase efficiency.
Therefore, I will be sharing about the different types of data analysis methods, including what is data analysis, the importance of data analysis and how you can choose the right method of analysis.
This will give you a clear idea of which data analysis method to pick for your analysis project.
This article shares the 7 different types of data analysis methods.
What is Data Analysis
Data analysis is the process of examining, cleaning, transforming, and modelling data to uncover useful information, draw conclusions, and support decision-making. Think of it as being a detective, but instead of solving crimes, you are solving problems using data.
Importance of Data Analysis
Learning data analysis is crucial because it helps you make informed decisions based on evidence rather than guesswork. Whether you want to understand customer behaviour, track business performance, or conduct research, data analysis is a valuable skill that can open many doors for you.
Resource for you: The Importance of Data Analytics in Ecommerce
7 Must-Know Data Analysis Methods
#1 Exploratory Analysis
Exploratory analysis is all about investigating data to find patterns, trends, and relationships. It is like exploring a new city to see what is interesting.
This method identifies patterns and anomalies, often using visual tools like charts and graphs to make the data easier to understand. It is fairly easy to learn, especially with user-friendly tools like Excel or Tableau, and it is common, used in the initial stages of data analysis projects.
Common Examples:
- Discovering popular products in a retail store: By examining sales data, you can identify which products are best-sellers.
- Identifying trends in social media activity: By analysing engagement metrics, you can see which types of posts perform best.
Resource for you: 5 Essential Exploratory Data Analysis Fundamentals Every Analyst Needs to Know
#2 Descriptive Analysis
Descriptive analysis summarises data to show what has happened, much like writing a summary of a book.
It uses statistics like mean, median, and mode to provide a snapshot of data. This method is very easy to understand, involving basic statistical calculations, and is extremely common, often used in reports and dashboards.
Common Examples:
- Summarising sales performance over a year: This can show overall trends and highlight periods of high or low sales.
- Reporting on website traffic statistics: It provides insights into visitor numbers, page views, and user behaviour on a site.
#3 Diagnostic Analysis
Diagnostic analysis digs deeper into data to understand why something happened, similar to a doctor diagnosing an illness.
This method looks for root causes and often involves comparing data sets to find correlations. It is moderately easy, requiring more detailed investigation, and is quite common, especially in troubleshooting scenarios.
Common Examples:
- Analysing why sales dropped in a particular month: By comparing sales data with other variables like marketing spend or economic conditions, you can identify possible causes.
- Investigating reasons for increased customer complaints: Examining complaint data alongside product or service changes can reveal the source of the issue.
#4 Predictive Analysis
Predictive analysis uses historical data to predict future outcomes, akin to forecasting the weather.
This method involves using models and algorithms, such as regression analysis, to make predictions. It can be challenging, requiring knowledge of statistics and modelling, but it is common in business planning and strategy.
Common Examples:
- Predicting future sales based on past trends: By analysing past sales data, you can forecast future sales and plan inventory accordingly.
- Forecasting customer behaviour: Using data on past customer actions, you can predict future behaviours and tailor marketing efforts.
#5 Inferential Analysis
Inferential analysis makes inferences about a larger population based on a sample of data, like tasting a spoonful of soup to decide if the whole pot needs more salt.
This method uses samples to make generalisations and involves techniques such as hypothesis testing and confidence intervals. It is moderately easy, requiring an understanding of sampling and probability, and common in research and surveys.
Common Examples:
- Generalising survey results to a broader population: By analysing survey responses from a sample group, you can infer the opinions of the larger population.
- Testing a new product feature on a small group before a full launch: The results from the test group can indicate how the larger market might react.
#6 Prescriptive Analysis
Prescriptive analysis suggests actions to achieve desired outcomes, like having a GPS that not only shows you the way but also suggests the best route.
This method provides actionable recommendations using optimisation and simulation techniques. It can be complex, involving advanced modelling, but is common in advanced business and operational planning.
Common Examples:
- Optimising supply chain logistics: By analysing various logistics scenarios, you can determine the most efficient supply chain strategy.
- Personalising marketing strategies for individual customers: By examining customer data, you can recommend specific products or services tailored to individual preferences.
#7 Causal Analysis
Causal analysis identifies cause-and-effect relationships, similar to understanding how eating habits affect health.
This method focuses on causation, not just correlation, and is often used in scientific research. It is challenging, requiring rigorous testing and validation, but common in research and policy-making.
Common Examples:
- Studying the impact of educational programmes on student performance: By analysing data from different student groups, you can determine if the programme directly improves performance.
- Investigating the effects of a new medication on health outcomes: Through controlled studies, you can assess whether the medication causes the observed health changes.
Infographic of the 7 Data Analysis Methods
How to Choose the Right Method for Your Needs
To choose the right data analysis method, start by defining your goal. Understand what you want to achieve with your data analysis. Are you looking to understand past performance, predict future trends, or find the cause of an issue?
Next, consider the type and quality of data you have. Some methods require large amounts of high-quality data.
Finally, assess your skills. Choose a method that matches your skill level.
Beginners might start with descriptive and exploratory analysis before moving on to more complex methods.
This post is all about the 7 different types of data analysis methods.
If you love to see more content like this, connect & follow me on social media to stay updated with the latest information.
Other posts you may like:
- How to choose the perfect analytics course for beginners?
- 5 Common Ecommerce Analytics Use Cases that Every Business Owner Should Know
- 9 Easy Steps to Create a Customised Excel Automated Dashboard
