Data analytics combines data preparation, exploration, modelling, visualisation and interpretation to help organisations make better decisions. This A–Z glossary explains common data analytics, business intelligence, statistics, SQL and reporting terms in clear, practical language.
A
Aggregation
Combining detailed values into a summary, such as totals, averages, minimums or maximums.
Anomaly
A data point or pattern that differs noticeably from what is normally expected.
B
Benchmark
A reference value used to compare performance, results or trends.
Business Intelligence (BI)
The processes and tools used to turn organisational data into reports, dashboards and insights for decision-making.
C
Correlation
A statistical measure of how strongly two variables move in relation to one another.
Data Cleansing
Correcting, removing or standardising inaccurate, duplicate, incomplete or inconsistent data.
D
Dashboard
A visual display that brings together important measures, charts and indicators in one place.
Data Model
A structured representation of data, including tables, fields and relationships, designed to support analysis.
Data Warehouse
A central repository designed to store integrated historical data for reporting and analytics.
E
ETL
Extract, Transform and Load: a process for taking data from source systems, preparing it and loading it into a target system.
Exploratory Data Analysis
Initial investigation of data to understand patterns, distributions, relationships and potential issues.
F
Fact Table
A table that stores measurable business events, such as sales, transactions or quantities, often linked to dimension tables.
Forecast
An estimate of future values or outcomes based on historical data and assumptions.
G
Granularity
The level of detail stored or analysed in a dataset, such as individual transactions versus monthly totals.
Grouping
Organising records into categories so they can be summarised or compared.
H
Histogram
A chart that shows how numerical values are distributed across ranges or bins.
Hypothesis Testing
A statistical method used to assess whether evidence in a sample supports a claim about a wider population.
I
Insight
A meaningful conclusion drawn from analysing data that can support a decision or action.
Imputation
Replacing missing values with estimated or substituted values so analysis can continue.
J
Join
Combining records from two or more tables using matching fields or keys.
JSON
JavaScript Object Notation, a common text format for exchanging structured data between systems and applications.
K
Key Performance Indicator (KPI)
A measurable value used to track performance against an important objective or target.
Key
A field used to uniquely identify records or connect related tables.
L
Data Lake
A repository that stores large volumes of structured, semi-structured and unstructured data in its native form.
Lookup
Retrieving a related value from another table, range or dataset using a matching key.
M
Mean
The arithmetic average of a set of numerical values.
Median
The middle value when numerical observations are ordered from lowest to highest.
Metric
A quantitative measure used to evaluate activity, performance or outcomes.
N
Normalisation
Organising or transforming data into a consistent structure or scale, depending on the analytical context.
Null
A missing, unknown or undefined value in a dataset.
O
Outlier
A value that sits unusually far from the rest of the observations and may require investigation.
OLAP
Online Analytical Processing, a method of analysing data across multiple dimensions for fast summarisation and comparison.
P
Percentile
A value below which a specified percentage of observations falls.
Pivot Table
A tool for interactively summarising and rearranging data by categories, measures and filters.
Predictive Analytics
Using historical data and statistical or machine learning techniques to estimate likely future outcomes.
Q
Query
A request used to retrieve, filter, transform or summarise data from a data source.
Quartile
One of four groups created by dividing an ordered dataset into equal parts.
R
Regression
A statistical technique used to model relationships between variables and estimate outcomes.
Relational Database
A database that stores data in related tables connected through keys.
S
Schema
The defined structure of a database or dataset, including tables, fields, data types and relationships.
SQL
Structured Query Language, used to retrieve and manipulate data in relational databases.
Standard Deviation
A statistical measure showing how spread out values are around their mean.
T
Trend
A general direction of change in data over time.
Time Series
A sequence of observations recorded at regular or irregular points in time.
U
Unstructured Data
Information that does not follow a fixed tabular format, such as documents, images, audio or free text.
Unique Value
A value that occurs once within a field or dataset, often useful for identifying records or categories.
V
Variance
A statistical measure of how far values spread from their mean; also used in business reporting to compare actual versus expected results.
Visualisation
Representing data graphically using charts, maps, tables or other visuals to make patterns easier to understand.
W
Window Function
A SQL function that calculates values across a set of related rows without collapsing them into a single summary row.
Weighted Average
An average in which some values contribute more than others according to assigned weights.
X
X-axis
The horizontal axis on a chart, commonly used for categories, dates or an independent variable.
XML
Extensible Markup Language, a structured text format used to store and exchange data between systems.
Y
Y-axis
The vertical axis on a chart, commonly used to display a measure or dependent variable.
Year-over-year (YoY)
A comparison between a value and the equivalent period in the previous year.
Z
Z-score
A statistical value showing how many standard deviations an observation is above or below the mean.
Zero Value
A numeric value of zero, which is different from a blank or null and may carry an important analytical meaning.
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