Wisconsin DAMA Big
Data Day – Tuesday, March 11, 2014
What is Big Data and Hadoop? How can my organization
leverage this new information capability? Is this just the latest hype
or, is there real business value to be realized by organizations that adopt Big
Data into their information ecosystem. WI DAMA Big Data is your chance to
learn more around this topic including best practices, architecture, tools, lifecycle
management, and the business use cases achieving real value from this
information capability. Learn and ask questions from Big Data real world
experts and practitioners.
8:00 – 9:00
Registration/Breakfast (anticipate 300 people being registered)
9:00 – 9:45 Chapter Business
& Vendor Presentations
9:45 - 10:45 Presentation #1: Big
Data Architecture and Technologies - The integration into conventional
Enterprise workload: Phil Shelley - President at Newton Park
10:45– 11:00 Break
11:00 – 12:00 Presentation #2: Big Data Governance:
Sunil Soares - Founder & Managing Partner at
Information Asset, LLC
12:00 – 1:00 Lunch
1:00 – 2:00 Presentation #3:
Big Data Management the Lab and the Factory: April Reeve - Advisory
2:00 – 2:15 Break
2:15 – 3:15 Big Data Panel
Presentation #1: Big Data Architecture and
Technologies - The integration into conventional Enterprise workload
Description: After starting in Google, Facebook and
other Internet companies, Big Data technologies are now successfully finding
their way into conventional enterprises. Ideal workload for these technologies
include the offloading of heavy batch processing and ETL from data warehouses
and conventional ETL tools and batch processing from mainframes or mid-range
systems or databases where performance is a bottleneck. Significant cost
reductions and performance improvements are seen by enterprises adopting these
techniques and tool. This presentation will cover an overview of the integrated
architecture and approaches for successful implementation.
Dr. Phil Shelley Bio: As former CTO of Sears
Holdings, Dr. Phil Shelley pioneered the move to near real-time digital
customer engagement, migrating people, process and systems to align with
business and collapsing systems into single points of truth. Phil led IT
Operations for one of the largest retailers in the world, through transitions
to a real-time omnichannel system architecture, design, implementation and
operations, including single view of customer, basket, inventory and loyalty
systems: The transformation centered on Open-Source and Big Data technologies.
In addition to the CTO role, in 2012 Phil was appointed CEO
of a Sears startup in Big Data technologies, along with significant recognition
for the innovations (http://consultnpp.com).
Phil left Sears to form NPP, a Big Data advisory firm, helping companies design
and implement Big Data initiatives. Dr. Shelley holds a PhD in bio-medical
engineering, having worked in the UK, Germany and USA; leading IT, R&D,
Quality and business leadership roles including CEO, CIO and Chief Technology
Phil is an industry expert speaker on Big Data, noSQL and
Cloud computing, he is also co-chair of the cloud computing chapter of the IAOP
and President of NPP, industry experts in C-Level Big Data strategies and
Presentation #2:Big Data Governance
Description: This session will review the convergence
of Big Data and Data Governance. The presentation will be divided into three
Part I: Getting Started
- Introduction to big data governance
- Big data governance framework
Part II: Big Data Governance Disciplines
- Organizing for big data governance
- Metadata for big data
- Big data privacy
- Big data quality
- Business process integration
- Master data integration
- Managing the lifecycle of big data
Part III: Governance of Big Data Types
- Web and social media
- Machine-to-machine data
- Big transaction data
- Human generated data
Sunil Soares Bio: Sunil Soares is the founder and
managing partner of Information Asset, LLC, www.information-asset.com a
consulting firm that specializes in helping organizations build out their data
governance programs. Prior to this role, Sunil was the Director of Information
Governance at IBM, and worked with clients across six continents and multiple
Sunil’s first book The IBM Data Governance Unified Process
details the fourteen steps and almost one hundred sub-steps to implement a data
governance program. The book has been used by several organizations as the
blueprint for their data governance programs, and has also been translated into
Sunil’s second book Selling Information Governance to the
Business: Best Practices by Industry and Job Function reviews the best way to
approach data governance by industry and function.
Sunil’s third book Big Data Governance reviews the
importance of data governance for different types of big data such as social
media, machine-to-machine, big transaction data, biometrics, and human
Sunil has also worked at the Financial Services Strategy
Consulting Practice of Booz Allen & Hamilton in New York. Sunil lives in
New Jersey and holds an MBA in Finance and Marketing from the University of
Chicago Booth School of Business.
Presentation #3: The Lab and the Factory –
Architecting for Big Data Management
Description: The environment needed for Big Data
Management includes more than just the various technologies storing our Big
Data. We need a “Lab” or “Sandbox” environment that is very dynamic and can be
used by the Data Scientists to throw in or throw away massive amounts of structured
and unstructured data against which to do analysis, find patterns and insights,
and develop models. Then, we need to take those models and create an
operational “Information Factory” with all the good production processes we’ve
learned around data access security and high volume efficiency to produce
insight and trigger action on an on-going operational basis.
The Data Scientist environment for predictive analytics – the Lab
Operationalizing predictions – the Factory
How does it fit with legacy data management architecture?
April Reeve Bio: April Reeve has spent the last 25
years working as an enterprise architect and program manager. Currently she is
working for EMC Consulting as an Advisory Consultant in the Enterprise
Information Management practice.
April is an expert in multiple Data Management disciplines
including Data Integration, Big Data, Data Conversion, Data Warehousing,
Business Intelligence, Master Data Management, and Data Governance.
She is the author of “Managing Data In Motion: Best Practice
Data Integration Techniques and Technologies” which is published by Morgan
November 14th - Karen Lopez
Data Modeling Contentious Issues
A highly interactive and popular session where attendees evaluate the options and best practices of common and advanced data modeling issues, such as:
* Party/party role
* Natural vs. surrogate keys
* Class Models vs. Data Models
* SOAs, Ontologies, ESBs, New TLAs and Shoe Strings
* What is Logical? What is Physical? Why Do We Care?
* Data Modeling for Cloud and NoSQL Databases
* Politics vs. Customer Satisfaction
Participants in this session will be presenting with an issue along with a range of responses or possible solutions. Participants will vote on their preferred response, and then the group as a whole will discuss the results, along with the merits of each possible response. If the specific issue has been discussed in other presentations, a summary of the responses of the other groups will be presented. The goal of this workshop is to help practitioners identify potential points of conflict in data modeling, as well as alternative approaches to resolving the issues. This presentation is targeted at experienced data modelers and assumes extensive data modeling skills.
Holiday Inn at the American Center
5109 W. Terrace Dr.
Madison, WI 53718
9:00 - 12:00 - Presentation
Current DAMA Members - Free
Non-Members - $30.00
On-line registration closes on November 17th
September 19, 2013 - Profitable Analytics
American Family Insurance - Building C
6000 American Parkway
Madison, WI 53783
Members : Free
Non Members: $30
Meeting 9:00 – 12:00
David Haertzen is the author of The Analytical Puzzle: Profitable Data Warehousing, Business Intelligence and Analytics. In addition, he contributes to industry publications and blogs. He is an acknowledged trail blazer and thinker in the fields of data warehousing, business intelligence and analytics. He has aided a diverse set of organizations from start-ups to multinationals to utilize data for their advantage.
David is known for his engaging teaching and speaking style (look out for challenging ideas!) and is a sought after presenter at industry conferences and events. He is a graduate of the University of Minnesota and holds an MBA from the University of St. Thomas.
To lean more about is training adn advisory services, visit his website www.DavidHaertzen.com and sign up for his Data and Analytic Insights Newsletter. David can be contacted at firstname.lastname@example.org .
Analytics goes beyond traditional reporting and business intelligence. Instead of showing what happened it shows what is likely to happen and when. Profitable analytics, addresses questions including:
· Why is analytics a hot topic?
· What are practical examples of analytics?
· How can analytics reduce operations cost? Increase revenues? Reduce risk?
· Who is profiting from analytics?
· What makes analytics different from reporting and business intelligence?
· What training and knowledge are needed to excel in analytics?
This session includes an effective mix of presentations and interactions. The Tricks and Traps section will show you how to avoid problems and make good choices.
Data Visualization - Wisconsin DAMA Day 2013
March 19, 2013 at 8:00 AM - 4:30 PM
Cindi Howson is the founder of BI Scorecard
Yes, You can Create An Architectural Data Model in UML
Product to Customer: A Fundamental Change through MDM
Big Data and Analytics
Agile Data Analysis
Dama Day 2011
Recognizing and Treating "Tableitis"
Introduction to ORM Modeling
Master Data Management (MDM)
Microsoft BI Overview
Workshop: Working with Techies: Better Collaboration with Developers and DBAs
Data Privacy - The Internal Threat
Enterprise Business Intelligence Solutions
Managing Data For Long Retention Periods: Requirements and Challenges