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 agile, self-service model that enables greater user access to data and the ability to create and perform individualized reports 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 around these days. Most companies already have their first analytical models in production and are thinking about further boosting their performance. Far too often, they focus on the analytical techniques rather than on the key
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 thigh bone”, your data strategy is connected to your analytics strategy – or it should be! Further, your analytics strategy
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 intelligence environment. You’ve built the databases and the ETL programs to move, clean and integrate data from multiple sources into
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 system continuously evolves over a period with new business functionality added at regular intervals, and they need to be in
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 apparent. Referential integrity was not strictly enforced and there was very little in the way of attribute level constraints. One
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 do not involve any traditional report writing or querying at all. Data mining is performed through a specialized tool, which
