It is important to understand the value of capturing as much detailed root data as possible to enable the development and usage of views and other analytical perspectives when designing business intelligence solutions. There are many levels of planning and
It is important to determine expected usage patterns for business intelligence and analytical applications before design, and to evaluate their actual usage after implementation to gauge success. Building an analytical solution can be an arduous task. Many factors are required
Introduction 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 analytics. In response, business intelligence (BI) and analytics teams are shifting to a more
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 around these days. Most companies already have their first analytical models in production and are
To be agile, business intelligence and analytics systems need frameworks and metrics to enable stable evolution. Introduction Enterprise Business Intelligence and analytics solutions are complex implementation efforts because of the Develop – Support (Growth-Sustain) cycles followed concurrently. Every enterprise wide BI
Every organization’s analytics strategy should be connected to its data strategy; and every organization should have a data strategy that supports its business strategy Introduction Like the lyrics in the children’s song Dem Bones, “the hip bone’s connected to the
Organizations can find increased value in business intelligence and analytics efforts through the use of various data delivery strategies. Introduction Like many organizations, your company probably has invested significant time, effort, and money into establishing your data warehousing and business
Using a specialized data validity dimension in a data warehouse design can support data quality and analytics capabilities. Introduction While working on an Enterprise Data Warehouse for a state court system the issue of poor data quality in the source systems became
Data mining is a powerful analytical activity that can be used with data warehouses and with operational systems, yielding valuable insights. Introduction Data mining is often confused with “writing lots of reports and queries,” when in fact data mining activities
