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DAMA - Portland Metro Chapter
March, 2017 Chapter Meeting PDF Print E-mail
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Friday, 20 January 2017 17:10

The Analytical Data Mart and The Customer Analytic Record

Presented by: Dr. Bob Nisbet, Instructor, Predictive Analytics Certificate Program, University of California, Irvine

Register Here

The Analytical Data Mart and The Customer Analytic
Record to Serve Predictive Analytics
Presented by Dr. Bob Nisbet. The rise of Predictive Ana
lytics in business will generate many opportunities for
data
managers. The Business Ecosystem has arrived, and Predictiv
e Analytics models are the
windows
into
understanding its operational complexity. The Operat
ional Data Store (ODS) is an elaboration of data war
ehousing
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 predic
tive analytics. Differences between the organization o
f data
in an EDW or ODS and that needed to serve analytics ef
ficiently are discussed. The discussion will include some
specific data transforms required by analytical algorit
hms, and how the
heavy lifting
of their processing can be
committed to data mart operations,. An example of th
e blending of an ODS and an ADM to serve predictive a
nalytics
modeling operations in a Santa Barbara bank will be
presented.
The Analytical Data Mart and The Customer Analytic
Record to Serve Predictive Analytics
Presented by Dr. Bob Nisbet. The rise of Predictive Ana
lytics in business will generate many opportunities for
data
managers. The Business Ecosystem has arrived, and Predictiv
e Analytics models are the
windows
into
understanding its operational complexity. The Operat
ional Data Store (ODS) is an elaboration of data war
ehousing
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 predic
tive analytics. Differences between the organization o
f data
in an EDW or ODS and that needed to serve analytics ef
ficiently are discussed. The discussion will include some
specific data transforms required by analytical algorit
hms, and how the
heavy lifting
of their processing can be
committed to data mart operations,. An example of th
e blending of an ODS and an ADM to serve predictive a
nalytics
modeling operations in a Santa Barbara bank will be
presented.

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.

 

Speaker

Dr. Bob Nisbet’s original training was in Botany, with specialties in plant ecology and paleobotany.  He taught and conducted research in Botany and Ecology for many years in several colleges and universities, most recently as a Researcher in Forest Growth Modeling at the University of California, Santa Barbara.  He led a field investigation in central Ohio to collect plant fossils from Mississippian sediments.  He retains his interest in paleobotany to the present.  For the last 20 years of his career, Bob was active as a Data Scientist, initially for AT&T, then for NCR Corporation (after the split in 1996). He led the Yield Management analytical team at NCR Corporation which pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications, Insurance, Banking, Credit, membership organizations (e.g. AAA), and Health Care industries. He is lead author of the award-winning “Handbook of Statistical Analysis & Data Mining Applications” (Academic Press, 2009), and a co-author and general editor of the award-winning "Practical Text Mining" (Academic Press, 2012) and “Practical Predictive Analytics and Decisioning Systems in Medicine” (Academic Press, 2015).  In his retirement, he serves as an Instructor in the University of California at Irvine Predictive Analytics Certificate Program, teaching many online and on-campus courses each year in Effective Data Preparation, and co-teaching Introduction to Predictive Analytics.  He serves also as a technical advisor of the Predictive Analytics Certificate Program at UC-Irvine, as a Technical Editor of the Practical Predictive Analytics series of books by Cambridge University Press.  He serves also on the Conservation Advisory board of the Santa Barbara Botanic Garden, where he provides botanical and analytical services.

When

March 16, 2017 (Chapter Meetings, 3rd Thursday)

Schedule

8:30 - 9:00 am - Sign In 
9:00 - 10:15 am - Presentation 
10:15 - 10:30 am - Break, Chapter Announcements
10:30 - 11:30 am - Presentation continued


Standard Insurance Center in the Atrium room
900 SW 5th Avenue in Portland.

The room is located at the 5th street level past the south elevator bank (as you enter from the 5th street sidewalk to the right past the elevators and then through the double glass doors into a new window-ed space).

Cost

Free for Members!
$15 for Non-Members
$5 for Students with valid student ID
See our corporate members

Last Updated on Tuesday, 21 February 2017 23:15