Karen is a senior project manager and architect with an extensive background in development processes and information management. She specializes in taking practical approaches to systems development. She has 20+ years of public speaking (keynotes, speeches, and demonstrations). She wants attendees to have fun, gain insights and take away inspiration for working with new technologies and methods.
Tag: datawarehouse
March 2023: Data Governance with Dataplex
(RESCHEDULED!) This session will include a data governance overview, review of components of data governance, and data governance through Dataplex on GCP with use-cases and demos. Please join us for this IN PERSON event!
January 2023: The Data Lakehouse with Bill Inmon
Certain vendors have suggested that the organization throw data into a data lake and then let end users analyze the data in the data lake. The data lake quickly turns into a data swamp or sewer. No one gets any value out of the data lake. In order to turn your data into something useful you need to turn your data lake into a Data Lakehouse. This presentation is all about the evolution of architecture and how to start to get value out of your data lake.
January 2019: Benefits and Challenges of Migrating to the Cloud
DAMA PDX chapter meeting
March 2017: The Analytical Data Mart and The Customer Analytic Record
The rise of Predictive Analytics in business will generate many opportunities for data managers. The Business Ecosystem has arrived, and Predictive Analytics models are the “windows” into understanding its operational complexity. The Operational Data Store (ODS) is an elaboration of data warehousing design to serve the needs of business data users better (cf. Kent Graziano’s talk). The Analytical Data Mart (ADM) serves an analogous purpose to serve the needs of predictive analytics. Differences between the organization of data in an EDW or ODS and that needed to serve analytics efficiently are discussed. The discussion will include some specific data transforms required by analytical algorithms, and how the “heavy lifting” of their processing can be committed to data mart operations,. An example of the blending of an ODS and an ADM to serve predictive analytics modeling operations in a Santa Barbara bank will be presented.