Move. Store. Report.
Operational and transactional systems grow in any large enterprise and form the silos of information that are hard to report on. Building a Data Warehouse enables separation of reporting from transactional data stores, organizes the data suitable for query and allows slicing/dicing. Data from disparate sources is brought together via Extract Transform Load (ETL) and ingestion through Map Reduce (Hadoop) tool-set into the warehouse. Business Intelligence (BI) front-end tools enable the consumers of data query the data.
The analysis of outcomes begins in part with analyzing the volume of expected data from each data source and its projected growth over time. Terabytes are very common.
The rate at which data is being generated determines the rate at which the consumption and ingestion of data must scale to.
Variation in terms of values, cases and business rules also is crucial for understanding and ingesting the data into the data warehouse to allow the business to succeed with good results