Defining the optimal organizational structure for a data warehouse, business intelligence, analytics program is a critical success factor that starts with the proper foundation. There are many ways to build IT and business organizations that support data warehousing / business
Data provides more reliable insight into organizational outcomes than intuition alone can. That’s why most managers build dashboards to monitor their sales pipeline, revenue generated, and churn rates: to get real-time updates on their team’s overall progress. While those are
Not all business intelligence / analytics efforts must be large, multi-phased initiatives. Small, focused, quickly completed projects can provide lasting value to any organization. Small, focused analytical efforts can be of great benefit to an organization. Often, they are used
Industries with broad and deep data collections, such as healthcare, require extensive business intelligence and analytical solutions to address their decision-making needs. Most healthcare providers (hospitals and insurers) are building, installing, or enhancing data capture systems. Many are focused on
Successful project managers present realistic expectations for completing any effort. Managing expectations for delivery is crucial to the perception of success, especially with business intelligence and analytics projects. Many data warehouse, business intelligence and analytics projects would have been labeled
Is your business intelligence environment ready for safe, reliable, scalable accessibility via the Internet? How are you addressing this challenge? Typically, business intelligence (BI) environments are constructed and deployed initially to internal clients across the corporate local area network or
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
Every data warehouse and business intelligence initiative has risks. Learn to identify them at the start and select the optimal remedy or mitigation technique to address each risk. It is essential to identify the potential risks within every project and
Explaining data warehouse automation can be challenging, but all data warehouse / business intelligence professionals should understand the concepts and practices. Data warehouse automation is not some sort of wizardry. It does not automagically build, model, and load a data
