Michael Anderson

Michael has more than twenty years of Data Warehousing experience on multiple hardware platforms for various industries. Enterprise Architect for Data Warehouses and Marts at Fortune 500 Companies. He has extensive experience with ETL, Business Intelligence\DSS, SOA, and Data Quality related issues.

Blogs from this Author

Creating the Canonical Modeling Environment – Part 2

Now that we’ve established what a canonical data model is, let’s talk about our objectives for what we want to achieve with our Canonical Data Model and what toolsets can be applied. In my Canonical Data Modeling environment, I want to store and manage my entities and their relationships. I want support for the Conceptual, […]

Canonical Data Modeling-Marriage of SOA & Enterprise Data Model

A Canonical Model is the marriage of your data’s business semantics and the related business rules governing your enterprise asset. Your data assets can be represented by structure (Relational Data) or non- structure (Big Data) in multiple ontological frameworks. Your business semantic is composed of the natural business language used for conducting its affairs stored […]

Is Your IT BI Strategy Aligned to the Business Strategy?

In my February blog, I wrote about basic BI categories so we could assess our BI environment. If you recall our BI categories were as follows: Type I – Reporting and Query tool sets Type II – Analytic tool sets Type III – Predictive Modeling tool sets Now, we can gather some facts to measure […]

Intelligent Taxonomies

In my past, I’ve had to perform Data Warehouse (DW) Assessments where we need to assess an organization’s DW maturity. In doing so, it’s important to categorize the things that you’re measuring to give it more context. Here is a two part blog where I provide a simple definition of BI and what categories are […]

Data Profiling: The First Step in Data Quality

When I think of data quality, I think of three primary components: data profiling, data correction, and data monitoring. Data profiling is the act of analyzing your data contents. Data correction is the act of correcting your data content when it falls below your standards. And data monitoring is the ongoing act of establishing data […]