Traditional Data Analytic Solutions can be very HARD to build and have critical LIMITATIONS!
Many companies have limited industry expertise, experience long development timelines, and incur substantial costs in the design and development of data analytic infrastructures often missallocating valuable resources and detracting focus from the business process with a 70-80% failure rate *
Recently, Gartner noted a study that purported to show that "between 70% and 80% of DW/BI projects fail",
due in part to poor communication and high complexity ...
STAR Schemas widely utilized in corporate environments; their weakness lies with inevitable upstream system changes and strict data cleansing requirements
OLAP (online analytical processing) data structures, usually STAR schema based, offer fast analysis of historical data from multiple perspectives; yet too often only 20-30% of the processed data reveals true value
Existing data analytic systems often lack the ability to adjust quickly to a changing business environment
Internally built solutions can provide meaningful business information; yet are unable to enlighten understanding, knowledge, or wisdom; a critical goal of Business Intelligence
Excessive IT Infrastructure overhead and technical expertise requirements can severely impede successful implementations by those companies in need of data analytic solutions