Data warehousing & Business Intelligence



Data warehousing & Business Intelligence

A data warehouse is a relational database that aggregates structured data from across an entire organization. It pulls together data from multiple sources much of it is typically online transaction processing (OLTP) data. The data warehouse selects, organizes and aggregates data for efficient comparison and analysis.

Business intelligence (BI) is a process for analyzing data and deriving insights to help businesses make decisions. In an effective BI process, analysts and data scientists discover meaningful hypotheses and can answer them using available data.



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For example, if management is asking “how do we improve conversion rate on the website?” BI can identify a possible cause for low conversion. The cause might be lack of engagement with website content. Within the BI system, analysts can demonstrate if engagement really is hurting conversion, and which content is the root cause.
The tools and technologies that make BI possible take data stored in files, databases, data warehouses, or even on massive data lakes and run queries against that data, typically in SQL format. Using the query results, they create reports, dashboards and visualizations to help extract insights from that data. Insights are used by executives, mid-management, and also employees in day-to-day operations for data-driven decisions.

BI data can include historical information stored in a data warehouse, as well as new data gathered from source systems as it is generated, enabling BI tools to support both strategic and tactical decision-making processes.
Initially, BI tools were primarily used by data analysts and other IT professionals who ran analyses and produced reports with query results for business users. Increasingly, however, business executives and workers are using business intelligence platforms themselves, thanks partly to the development of self-service BI and data discovery tools and dashboards. The BI market is expected to experience continuous growth as tools increasingly incorporates both artificial intelligence (AI) and machine learning (ML).

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