Aggregation is one of the most fundamental concepts in Business Intelligence and Data Analytics. At its core, aggregation means taking many data points and combining them into a simpler, more meaningful number. Instead of looking at 10,000 transactions one by one, you summarize them into totals, averages, counts, or other metrics.

In BI tools like Power BI, Tableau, or Looker, aggregation happens every time you create a chart, KPI tile, or pivot table. For example, when you view “Total Revenue for June,” the system aggregates thousands of sales rows into one number. This reduces complexity, speeds up analysis, and helps decision-makers focus on what matters.

There are several common types of aggregations:

• Sum — adding up values (total sales).

• Average — calculating the mean (average order value).

• Count — number of rows or events (number of customers).

• Min/Max — highest or lowest value (highest-selling SKU).

• Median — middle value, useful when data has outliers.

• Percentiles — distribution-based aggregation (P95 latency).

In data modeling, aggregation is usually performed at different grain levels. For example:

1. Transactional grain: each order item

2. Daily grain: daily revenue

3. Monthly grain: monthly revenue

4. Customer grain: revenue per customer

The “grain” determines the detail level of analysis, which is crucial for understanding how metrics roll up across hierarchies like year → quarter → month → week → day.

Aggregations can be pre-calculated (materialized) in a data warehouse for speed or calculated in real time by BI tools. Pre-aggregations are common in platforms like Cube, Druid, BigQuery BI Engine, and ClickHouse to support dashboards that need sub-second performance.

Aggregation also plays a major role in dimensional modeling. Fact tables store raw data, while dimension tables allow grouped aggregations such as “Revenue by Region” or “Conversions by Campaign.”

In machine learning, aggregations help create features. For example, “total purchases in the last 30 days” or “average session length” are aggregate features that go into predictive models.

However, over-aggregation can hide important details. For example, averaging website conversion rate across all traffic sources might mask a drop coming from mobile users. This is known as the “aggregation trap” or Simpson’s paradox.

Ultimately, aggregation is the bridge between raw data and human decision-making. It compresses large datasets into insights that matter, while still allowing analysts to drill down when needed.