Choosing the Right Data Analysis Techniques

What Data Analysis Should I Use?

Data analysis is a crucial part of the business process. It helps businesses acquire actionable, relevant information for making decisions that reduce operational costs and risks.

Before you begin data analysis, think about what questions you are trying to answer. This will dictate which techniques you should use. The most common data analysis tools are graphical presentations and descriptive statistics.


Descriptive data analysis is the first step in any statistical study. It involves summarizing raw data and presenting it meaningfully to help researchers and analysts understand its characteristics. It also provides valuable insights into the primary properties of a dataset, including its frequency distribution, central tendency, dispersion, and identifying position.

Descriptive techniques can include measures of central tendency such as mean, median, and mode, as well as methods to measure the spread or variability of a dataset such as range, standard deviation, and variance. They can also include measures of position such as percentiles and quartiles.

Descriptive analytics is relatively accessible and something you’re likely already using in your organization – for example, reporting on engagement metrics like web traffic or social media mentions. It is the most basic form of analytical work and identifies patterns, but doesn’t dig any deeper into cause and effect. This lays the foundation for other more advanced forms of data analysis.


Predictive analytics is about forecasting future outcomes based on past data. Its benefits are immediate and concrete: ecommerce sites use predictive analytics to offer each customer specific products, based on their purchase history and browsing habits; human resources departments can detect when employees are thinking of quitting, and then persuade them to stay; IT security teams can go beyond simply identifying where malware has infected systems to predict where cybercriminals will attack next.

Performing predictive data analysis can be done manually or automatically, using machine-learning algorithms. You start by deciding on the business benefit you want to achieve, then identify what data will be needed and collect it. You can then combine this data and make statistical models that help you determine what would happen if one variable changed. A popular example is the 2020 US presidential election, which FiveThirtyEight used predictive modeling to forecast. The models are usually backed by evidence to ensure accuracy.


Exploratory data analysis (EDA) is an investigative process that involves looking at a dataset’s inherent qualities with an inquisitive mindset. It consists of using a variety of techniques, including summary statistics and data visualization, to uncover patterns and trends in the data. It can also help identify outliers and determine relationships between variables. EDA is typically carried out as a preliminary step before undertaking extra formal statistical analyses or modeling.

This type of analysis helps a company understand the characteristics and structure of its data. It can also reveal any potential issues with the dataset, such as missing values or inconsistent information. Then, the analyst can take steps to correct these issues before proceeding with further analysis.

In addition to identifying trends and patterns, EDA can also help a company determine which statistical models and techniques will be appropriate for their data. This ensures that the results of future analysis will be valid and accurate.


When your data is large, it may be impossible to analyze all of it. That’s when sampling comes in. Sample analysis involves using a smaller subset of your data to infer information about the larger population. This type of analysis is subject to error, or standard deviation, but the greater your sample size, the less distortion there will be in your estimation.

The types of statistical analysis that you choose will depend on what your goals are, the type of data you have and the method by which it was collected. In general, you should use descriptive techniques to describe your data and predictive techniques to predict future trends. For example, if you notice that one product has a higher rate of returns, hypothesis testing can help identify the cause. This allows you to improve your products and service, provide better training for your employees and give customers what they want.

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