A data warehouse is an environment that combines an integrated decision support database with software to perform extraction transformation loading processes from a variety of operational and external sources. These technologies are combined to support historical, analytical, and business intelligence (BI) requirements.
A business intelligence (BI) process collects, stores, and analyzes the data produced by a company’s activities. BI encompasses data mining, process analysis, performance benchmarking, and descriptive analytics. BI produces reports, analysis, and other supports analytical best practices.
Three steps can help any organization develop the right architecture for business intelligence, analytics, or a re-design of a data warehouse The best place to
Successful re-architecture of data warehousing / business intelligence / analytics environments pose business challenges in addition to information technology issues Many large corporations across a
Rather than apply new words or phrases to poor data architecture concepts, learn how a data strategy should influence effective data architecture, especially for analytics
All successful business intelligence / analytics endeavors are based on a formal strategy and use a best-practices based methodology, even in agile environments. Many companies
Data extraction, transformation, and loading processes enable many activities in information technology projects. Understanding the concepts and practices of ETL is essential for all data
Today’s digital world and fast paced business environment demands pervasive, timely analytics. Organizations must deliver speed, agility and ease of use for data access and
Investing in new sources of data can help to unravel complex customer behavior and improve key analytical insights Introduction Big Data and Analytics are all