📊 Data Analysis Terminology

Raw facts, figures, or information collected for analysis. Data can be numbers, text, images, or any observable information that can be measured or described.

A collection of related data organized in a structured format, typically in tables with rows and columns. Each row represents a record, and each column represents a variable or attribute.

A characteristic, number, or quantity that can be measured or counted. Variables can change or vary across observations.

A quantifiable measure used to track and assess performance or progress. Metrics are specific numbers that represent business activities.

A critical metric that directly measures progress toward strategic business objectives. KPIs are the most important metrics that leadership tracks regularly.

A visual display of key metrics and data points, typically showing real-time or near-real-time information. Dashboards provide at-a-glance views of business performance.

The sum of all values divided by the number of values. The most commonly used measure of central tendency.

The middle value when data is arranged in order. If there's an even number of values, it's the average of the two middle values. Less affected by extreme values (outliers) than the mean.

The value that appears most frequently in a dataset. A dataset can have one mode, multiple modes, or no mode.

The difference between the highest and lowest values in a dataset. Shows the spread of the data.

A measure of how spread out numbers are from the mean. A low standard deviation means data points are close to the mean; a high standard deviation means they're spread out over a wider range.

The average of the squared differences from the mean. Variance is the square of standard deviation. Used to measure variability.

A value below which a certain percentage of observations fall. The 75th percentile means 75% of values are below this point.

Values that divide data into four equal parts. Q1 (25th percentile), Q2 (50th percentile/median), Q3 (75th percentile).

Highly organized data that fits neatly into tables with rows and columns. Easily searchable and analyzable. Stored in relational databases.

Data that doesn't fit into traditional row-column structure. Includes text, images, videos, audio files. Harder to analyze but contains valuable insights.

Data that doesn't fit perfectly into tables but has some organizational structure like tags or markers. Often in formats like JSON or XML.

Data that can be grouped into categories. Values are labels or names, not numbers (though they may be coded as numbers).

Data expressed in numbers that can be measured or counted. Can perform mathematical operations on numerical data.

Data points indexed in time order. Used to track changes over time and identify trends, patterns, and seasonality.

Summarizes historical data to understand what happened in the past. Uses aggregation, data mining, and visualization to describe patterns.

Examines data to understand why something happened. Involves drilling down into data, finding correlations, and identifying causal relationships.

Uses historical data, statistical algorithms, and machine learning to forecast future outcomes. Identifies the likelihood of future events.

Recommends actions to take based on predictions. Uses optimization and simulation algorithms to suggest the best course of action.

A statistical measure that describes the relationship between two variables. When one variable changes, the other tends to change in a predictable way. Measured from -1 to +1.

A statistical method to model the relationship between a dependent variable and one or more independent variables. Used for prediction and understanding relationships.

Dividing a population or dataset into distinct groups based on shared characteristics. Each segment contains similar members, while different segments are dissimilar.

A method of comparing two versions (A and B) to determine which performs better. Randomly split your audience and measure which version achieves better results.

The total amount of money generated from sales of goods or services. Also called top-line or gross revenue.

Predictable revenue generated from subscription-based customers each month. Key metric for SaaS and subscription businesses.

The annualized version of MRR. Total predictable revenue generated in a year from subscriptions.

The percentage increase or decrease in a metric over time. Shows the pace of business expansion or contraction.

The total cost of acquiring a new customer, including all marketing and sales expenses divided by the number of customers acquired.

The total revenue a business expects to earn from a customer throughout their entire relationship. Critical for determining how much to invest in acquisition.

The percentage of customers who stop using your product or service during a given time period. Lower is better.

The percentage of customers who continue using your product over time. The opposite of churn rate.

A customer satisfaction metric based on one question: "How likely are you to recommend us to a friend?" Scored from -100 to +100.

The percentage of users who complete a desired action out of the total number of visitors or users.

A measure of profitability that shows the gain or loss from an investment relative to its cost. Expressed as a percentage.

The revenue generated for every dollar spent on advertising. Specifically measures advertising effectiveness.

The percentage of visitors who leave a website after viewing only one page without taking any action. High bounce rate may indicate poor user experience.

The process of detecting and correcting (or removing) corrupt, inaccurate, or irrelevant data. Essential first step in any analysis.

Data points that are absent from a dataset. Can occur due to errors, non-responses, or data loss. Must be handled appropriately in analysis.

A data point that differs significantly from other observations. Can be due to variability, errors, or truly exceptional cases.

Scaling data to fit within a specific range (typically 0 to 1). Makes variables with different units comparable.

Combining multiple data points into summary statistics. Reduces granularity while preserving important information.

The process of extracting data from sources, transforming it into a usable format, and loading it into a destination system (data warehouse).

The graphical representation of data and information using visual elements like charts, graphs, and maps. Makes complex data easier to understand.

Uses rectangular bars to compare values across categories. Bar length represents the value. Great for comparing discrete categories.

Displays data points connected by lines, showing trends over time. Perfect for visualizing continuous data and time series.

A circular chart divided into slices showing proportions or percentages of a whole. Each slice represents a category's contribution.

Displays values for two variables as points on a grid. Shows relationships, patterns, and correlations between variables.

Uses color intensity to represent data values in a matrix format. Darker/brighter colors indicate higher/lower values.

Shows the distribution of numerical data by grouping values into ranges (bins). Each bar represents frequency within that range.

Visualizes stages in a process showing progressive reduction. Each stage is narrower than the previous, representing drop-off.