Data is often chaotic and dispersed. This requires us to build solid data pipelines to efficiently transform data into a unified and useful format. We can think of this as a path with the following steps:
#### Data
Data begins as a collection of information, facts, and statistics. Often, the data we need is unstructured, comes in various formats, and originates from different sources.
#### Integration
Integration involves combining data from various sources and formats using techniques such as ETL (Extract, Transform, Load) and data modelling. This process creates a unified view of the data which we can then store in a Data Warehouse ready for analysis.
#### Analysis
Analysis involves examining and interpreting data to uncover patterns, trends, and correlations. This process often utilizes programming, statistical methods, and data visualization tools.
#### Insights
Insights are the deep understanding we've derived from our data. Specifically, this refers to patterns and relationships that have been uncovered from our data that didn't previously exist. We can use insights to make important business decisions or inform policy.
