The BI Fulfillment Gap — A WORK IN PROGRESS

There is an inconvenient truth about the discovery-driven BI world — ideas always outpace their fulfillment.  We never seem to catch up to the intelligence that we know is locked inside ever changing data.  This BI fulfillment gap is actually a good thing.  It drives us into new frontiers of understanding of our customers and businesses.  Our job as BI professionals is to manage both sides of the fulfillment gap — enabling new insights that expand the gap and move it forward, and providing the BI solutions that narrow the gap.

This page follows three demonstration projects I am working on to tackle the BI fulfillment gap.  I will post ideas and progress in the Demonstration Projects category in the sidebar menu.  Your feedback and suggestions are welcomed to help me address common concerns from multiple perspectives.

• Flexible Hierarchies — Business hierarchies are the “crown jewels” of an analytic-driven organization.  They are the analytic framework for marketing, product and sales management, and competitive benchmarking.  This project creates a high-performing EDW model for dynamic flexible hierarchies.  The project will provide technical artifacts for a Netezza DW appliance solution, but the modeling concepts are applicable to other EDW platforms.
• Netezza Dynamic Data Lineage — Data lineage traces business analytics through each stage of data integration and transformation back to the the EDW source.  Data lineage metadata supports business analytics auditing, EDW data flow design, QA test planning, and change impact analysis. This project defines a data lineage solution using Brightlight’s Netezza data integration framework.
• Business Analytics Data Modeling — Business analytics are the distillation of “big data” that is actually used to make decisions.  Surprisingly, business analytics are rarely included in the data models used to build the EDW.  More often, BI designers model analytics in the BI tool metadata, assuming that the EDW data model and physical structure will support those analytics.  Surprises occur at this point!  This project suggests best practices and techniques for integrating business analytics into the EDW data modeling process to create better BI solutions.