Data Warehousing and Business Intelligence

Data warehousing and business intelligence (BI) work together to turn raw data into clear insights. A warehouse stores clean, organized data from many sources so teams can answer questions quickly and reliably.

What is a data warehouse? A data warehouse is a central store designed for analysis, not day-to-day transactions. It keeps historical data, runs fast queries, and supports questions like “What were our top products last quarter?”

Key components and concepts

  • ETL or ELT: move data from source systems into the warehouse. ETL transforms before loading; ELT loads first, then transforms inside the warehouse.
  • Data models: a star or snowflake schema makes it easy to join facts (numbers) with dimensions (descriptions).
  • Data marts: smaller stores focused on a department, such as sales or finance.
  • OLAP and dashboards: fast, multi-dimensional analysis and visual reports.

Data vs data lake A data lake stores raw data at scale, while a data warehouse holds cleaned, structured data. Teams often use both: keep raw data in the lake, and curated data in the warehouse for BI.

How BI uses the warehouse With clean data, BI tools build dashboards, reports, and ad-hoc analyses. Users track key metrics, spot trends, and test ideas. Example: a retailer monitors daily sales by product, channel, and region, then plans inventory.

Getting it right

  • Start with business questions and the metrics that matter.
  • Define data quality rules and data lineage so users trust the numbers.
  • Plan governance and security early, especially for sensitive data.
  • Choose an architecture that fits your size: centralized warehouse, hub-and-spoke, or cloud-native options.
  • Optimize performance with appropriate indexing, partitioning, and summaries.

Example A mid-size retailer combines point-of-sale, online orders, and returns in one warehouse. BI dashboards show best sellers, stock levels, and margins by channel.

Bottom line A solid data warehouse makes BI practical. It turns data into reliable insight that guides decisions.

Key takeaways

  • Data warehousing organizes data for analysis.
  • BI translates data into clear visuals.
  • Start with business goals and maintain data quality and governance.

Key Takeaways

  • Data warehousing organizes data for analysis.
  • BI translates data into clear visuals.
  • Start with business goals and maintain data quality and governance.