October 5-7 in Chapel Hill, NC - Technics Publications45-4:45 Mastering Master Data Communication 21st Century Andrew Kapp, Westar Energy, Inc. Chancellor, Pg Jeremy Posner, ... Teradata

October 5-7 in Chapel Hill, NC

Data Modeling Zone 2015

Page i

Monday, October 5 fundamental (for all audiences),

intermediate, advanced

7:00-9:00 Breakfast in the Hill Ballroom

8:30-11:30 Data Modeling

Fundamentals

Steve

Hoberman, Steve Hoberman

& Associates,

LLC

Old Well, Pg 1

Data Quality

for Data

Modelers

Sue Geuens, President of

DAMA

International

North Parlor,

Pg 1

Making Your

Unstructured

Data Come

Alive

Bill Inmon, Forest Rim

Technologies

Chancellor, Pg

2

About

Cassandra

Open Software

Integrators

Alumni, Pg 3

Concurrent,

Integrated,

Value

Sensitive Data

Design - A

Novel

Approach to

Data and

Data Model

Design

Richard

Ordowich, STS

Associates Inc.

South Parlor,

Pg 3

11:45-12:00 Welcome and Announcements Hill Ballroom

12:00-1:00 KEYNOTE: Data Modeler 2020 – Future of Data Modeling Panel,

Michael Blaha, Deborah Henderson, Bill Inmon, Dave Wells, Graham Witt

Hill Ballroom, Pg 4

1:00-2:15 Lunch in the Hill Ballroom

2:15-3:15

UML Made

Easy!

Norman

Daoust, Daoust

Associates

Chancellor, Pg

4

DMBOK

Overview

Deborah

Henderson, Broadstreet Data

Old Well, Pg 5

Enterprise

Conceptual

Data Modeling

Brian Shive, Microsoft

Alumni, Pg 5

Normalization -

The Achilles

Heel of Data

Modeling

Gordon

Everest, University of

Minnesota

North Parlor,

Pg 6

Case Study:

Data

Warehousing

with SCD

Yoshihiko

Hoshi and

Hiroshi

Yagishita, Future

Modeling

Technologies

South Parlor,

Pg 6

3:15-3:45 Afternoon Snacks in the Colonnade

3:45-4:45 Mastering

Master Data

Communication

Andrew Kapp, Westar Energy,

Inc.

South Parlor,

Pg 7

Ensemble

Modeling

Hans Hultgren, Genesee

Academy

Chancellor, Pg

8

Tools and

Techniques for

21st Century

Data Modeling

Jeremy Posner, Synechron

Old Well, Pg 8

Prove it!

Verifying the

worth of data

modeling with

ROI analysis

Kim Medlin, Wells Fargo

Alumni, Pg 9

Data

Integration

Tips and

Tricks

Bob Conway, Information

Engineering

Associates

North Parlor,

Pg 9

5:00-6:30 Welcome Reception on the Terrace


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Tuesday, October 6 fundamental (for all audiences),



7:45-8:15

SIGs

Protecting

Personal Data

From Hackers

Cathy Nolan,

Allstate, and

Ashley Wilson, Attorney at Law

South Parlor,

Pg 10

How to talk

Data to Non-

Data People

Jill Camper, DST Systems,

Inc.

Alumni, Pg 11

Data Modeler

to Data

Scientist - A

New Maturity

Approach

Sanjay Shirude, ACCEL BI

North Parlor,

Pg 11

Natural vs.

Surrogate

Primary Keys in

Logical Data

Models

Gary Whitney, Microsoft

Old Well, Pg 12

Session to be

announced in

July!

8:30-11:30 Facilitation

and Training

Skills for Data

Professionals

Artie Mahal, ASM Group Inc.

Old Well, Pg

12

Semantic

Structure

Analysis and

Dimensional

Modeling

Dave Wells, Infocentric

Chancellor, Pg

13

Data

Governance for

Data Modelers

Workshop

Deborah

Henderson, Broadstreet Data

North Parlor,

Pg 14

About MongoDB

Open Software

Integrators

Alumni, Pg 15

UML in Depth

Norman

Daoust, Daoust

Associates

South Parlor,

Pg 15

11:45-12:00 Welcome and Announcements Hill Ballroom

12:00-1:00 KEYNOTE: To be announced in April!!

Hill Ballroom


2:15-3:15

The Journey to

an Enterprise

Data Model

Sherri Adame, Premier Farnell

Alumni, Pg 16

Evaluating

Data Modeling

Tools? Helping

You to Decide

George

McGeachie, Metadata Matters

South Parlor,

Pg 16

Crossing the

Unstructured

Barrier

Bill Inmon, Forest Rim

Technologies

Chancellor, Pg

17

Modern Data

Architecture? Or

Fresh Messaging

for Familiar

Concepts?

Eddie Sayer, Teradata

Old Well, Pg 17

Advanced SQL

Queries

Michael

Blaha, Modelsoft

Consulting

North Parlor,

Pg 18

3:15-3:45 Afternoon Snacks in the Colonnade

3:45-4:45 Accounting:

The Essence

David Hay, Essential

Strategies, Inc.

Alumni, Pg 19

Using ISO 8000

to Measure the

Quality of

Master Data

Peter R.

Benson, Electronic

Commerce Code

Management

Association

(ECCMA)

South Parlor,

Pg 19

From

Operational to

Analytics: An

Exploration of

Data Model

Designs for

Software

Business

Applications

Ralph

Hollinshead and

Goran Stanisic, SAS Institute

North Parlor,

Pg 20

Understaffed

with data

modelers? How

to train

developers as

apprentice data

model reviewers

Sally

Greenwood, TDS

Telecom

Old Well, Pg 21

MapReduce vs.

OLAP – Do

These Two

Worlds

Collide?


Chancellor,

Pg 22

5:00-6:30 Betting on Data Modeling with Wild Bill’s Casino! (Old Well)


Page iii

Wednesday, October 7 fundamental (for all audiences),



8:30-11:30 Data Vault

Fundamentals

and Workshop

Hans Hultgren, Genesee

Academy

North Parlor,

Pg 22

Corporate

Dictionary

Workshop

Peter R.

Benson, Electronic

Commerce Code

Management

Association

(ECCMA)

South Parlor,

Pg 23

About Hadoop

Open Software

Integrators

Alumni, Pg 24

Data Modeling

for Sustainable

Systems

Graham Witt, Ajilon

Chancellor, Pg

24

Advanced

Data Modeling

Challenges

Workshop

Steve

Hoberman, Steve Hoberman

& Associates,

LLC

Old Well, Pg

25

11:45-12:45 Competency

Assessment for

the Data

Professional


Alumni, Pg

26

Six Habitual

Architecture

Mistakes and

How to Avoid

Them


Old Well, Pg

27

Conducting

Data

Modeling

Project

Meetings

Gordon

Everest, University of

Minnesota

South Parlor,

Pg 27

Case Study:

Roadmap to an

Enterprise

Logical Data

Model

Missy

Wittmann, American Family

Chancellor, Pg

28

Implementing

Data Vault in

a Columnar

Database

Petr Olmer, GoodData

North Parlor,

Pg 28


1:45-4:45

FoCuSeD Data

Modeling -

facilitated data

modeling

Gary Rush, MGR Consulting,

Inc.

Chancellor,

Pg 29

Writing effective

business rules -

a practical

method

Graham Witt, Ajilon

North Parlor,

Pg 29

The Data

Modeler’s Road

to the Certified

Data

Management

Professional

(CDMP)

Patricia Cupoli,

CCP, CDMP,

CBIP, DAMA

International

Alumni, Pg 30

Data Modeling

by Example -

Introduction

and Workshop

Marco Wobben, BCP Software

Old Well, Pg

31

Just 375 days

till DMZ

2016!!

Join us Oct

17-19 in

Portland,

OR!


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Data Modeling Fundamentals

Steve Hoberman, Steve Hoberman &

Associates, LLC

Assuming no prior knowledge of data

modeling, we start off with an exercise that

will illustrate why data models are essential to

understanding business processes and

business requirements. Next, we will explain

data modeling concepts and terminology, and

provide you with a set of questions you can ask

to quickly and precisely identify entities

(including both weak and strong entities), data

elements (including keys), and relationships

(including subtyping). We will also explore

each component on a data model and practice

reading business rules. We will discuss the

three different levels of modeling (conceptual,

logical, and physical), and for each explain

both relational and dimensional mindsets.

Steve Hoberman taught his first data modeling

class in 1992 and has trained more than

10,000 people since then, spanning every

continent except Africa and Antarctica. Steve is

known for his entertaining and interactive

teaching style (watch out for flying candy!),

and organizations around the globe have

brought Steve in to teach his Data Modeling

Master Class, which is recognized as the most

comprehensive data modeling course in the

industry. Steve is the author of seven books on

data modeling, including the bestseller Data

Modeling Made Simple. His latest book, Data

Modeling for MongoDB, presents a streamlined

approach to data modeling for NoSQL

solutions. One of Steve’s frequent data

modeling consulting assignments is to review

data models using his Data Model Scorecard®

technique. He is the founder of the Design

Challenges group, recipient of the 2012 Data

Administration Management Association

(DAMA) International Professional

Achievement Award, and highest rated

presenter at Enterprise Data World 2014.

Data Quality for Data Modelers

Sue Geuens, President of DAMA

International

Data Quality is not generally a priority when

you start data modeling. The focus is on

defining your conceptual model or

understanding of the business requirements;

parlaying that into a decent logical model and

then handing it over to the DBAs or physical

DB modelers.

Unfortunately, that focus is ignoring the fact

that Data Quality is a primary driver in being

able to use the data the business has captured,

created and stored to provide meaningful

business intelligence that drives accurate and

timely business decisions.

Sue’s almost 20 years in data stands her in

good stead. She has been involved in many

projects of data modeling, designing and

understand very large databases and systems;

has run a couple of data quality projects and

most recently finished a two year contract to

implement a Data Governance program at

SA’s largest mobile operator. She is currently

on a six months project at the same company

designing and implementing a KPI Metric

model for the Commercial Operations division

(including Online and Self Service) and this

project has managed to unearth many data

quality anomalies.

This workshop will help data modelers

understand how to consider data quality

actually MUST fit into any data model – be it

at the conceptual level or right down in the

nuts and bolts of the physical model.


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Typical discussions will be around the

dimensions of data quality, how to keep strict

controls on the data as you start to develop

your models, understanding how to get the

business to specify their data at a “Fit for

Purpose” level – enabling data modelers to

manage their models and to build them

keeping the quality of the data as a key

priority. Further discussions will include why

Sue believes that primary keys, foreign keys,

clearly defined relationships and data

attributes all contribute to appropriate data

quality. Finally, a discussion on measuring

how your data models stand up against good

data quality and governance.

You should leave this workshop with a clear

understanding of what changes you may need

to bring to your future data modeling efforts to

improve the quality of the data your business

requires to make solid and innovative business

decisions for the future.

Sue is a Senior Data Management Specialist

who has been customer facing for the past 18

years. During this time she has focused

specifically in the financial (banking,

insurance, pensions) and telecommunications

sectors, gaining immense knowledge and

expertise in both. Each year she attends a

number of Data Management conferences

giving presentations both locally and overseas.

Her initial step into the world of data came

about in the form of designing and

implementing the first registration system for

the NHBRC. Since then she has moved on to

various businesses and enterprising, always

working toward Data Quality and Integrity,

which is her passion. Sue was elected President

of DAMA SA during January 2009 and was

the driving force behind the Inaugural Meeting

which was held on 18th February 2009 at

Vodaworld in Midrand. Just completed

implementing Data Governance at a large SA

Telco, Sue has moved her focus to responding

to the many challenges facing SA companies

with their data. Sue has just been voted in as

the DAMA I President for the 2014/2015 term.

Making Your Unstructured Data

Come Alive

Bill Inmon, Forest Rim Technologies

80% of the data in the corporation is

unstructured. Yet nearly all of the corporate

decisions are made on the basis of structured

data. This half day presentation addresses

how you can start to incorporate unstructured

data in the corporate decision making process.

This presentation entails the various aspects

of textual disambiguation and explores how

textual disambiguation is used to transform

textual data into structured data that can then

be analyzed by standard analytical tools.

Bill Inmon – the “father of data warehouse” –

has written 53 books published in nine

languages. Bill’s latest adventure is the

building of technology known as textual

disambiguation – technology that reads raw

text in a narrative format and allows the text to

be placed in a conventional data base so that it

can be analyzed by standard analytical

technology, thereby creating unique business

value for Big Data/unstructured data. Bill


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was named by ComputerWorld as one of the ten

most influential people in the history of the

computer profession. For more information

about textual disambiguation refer to

www.forestrimtech.com.

About Cassandra

Open Software Integrators

This is a basic introduction to Cassandra

including how it works, ideal use cases,

counter-indicated use cases and best practices.

We'll show you:

how to install and operate Cassandra

how to operate the Cassandra APIs

(Java, REST)

how to monitor Cassandra.

The training will be conducted by Open

Software Integrators, a Big Data consulting

and services company specializing in Hadoop,

Cassandra, MongoDB and other NoSQL

technologies. OSI focuses on executive strategy,

initial install, design and implementation;

helping companies transition from legacy

systems into a data-driven organization.

Concurrent, Integrated, Value

Sensitive Data Design - A Novel

Approach to Data and Data Model

Design

Richard Ordowich, STS Associates Inc.

One of the significant challenges in designing

a data model is the human factor. Current

data modeling best practices concentrate on

the technical challenges. Addressing the

human factors requires techniques mostly

unfamiliar to designers. Human factors that

affect data model design include:

Enterprise level participation

Common interests

Autonomy

Consensus

Cooperation

Accountability

Ownership

Politics

We refer to these human factors as Values.

These Values impact a data model design in

various ways:

Acceptance of the model

Ownership of the model

The suitability of the model to satisfy

all needs and expectations

The extensibility of the model

The adaptability of the model

Many describe the human factors as the gap

between the business users and IT. It is also

frequently referred to as the politics affecting

the design or the “elephant in the room”. Few

of the current data model design best practices

adequately address these human factors or

Values.

We researched the history of data modeling to

understand the successes and failures of data

models. We studied other domains where

design principles are similar to data model

design. We identified human factor best

practices used in these domains and developed

a series of human factors design practices that

should be included in data modeling.

In the 1970’s, Charles Bachman introduced

the Three Schema Approach for data models.

The three schemas consisted of an external

model, a conceptual model and an internal or

physical model. The external model represents

the business user viewpoints.

In our work in human factors design we focus

on the external model.

In this session we will share with you the best

practices we have adapted from other domains

that will help to improve data and data model

designs. These best practices will help you

identity the Values of your users and factor in

those Values into your design. These best


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practices will also help you bridge the

business/IT gap and help you work with the

business users as collaborators and owners of

the data model design. We will provide you

with a roadmap that shows you how to adopt

these best practices in your organization. We

call this the Concurrent, Integrated, Value

Sensitive Data Design.

Richard Ordowich is an independent

consultant with more than 30 years of

experience in IT including data governance,

data standards, data warehouse and data

management. Richard has an in-depth suite of

skills including project management, quality

assurance, architectural design, software

development, hardware development, business

management, business analysis, market

research, operational workflow design, product

development and IT Management. In his career

Richard has designed innovative hardware

solutions, large scale trading systems and

managed new startup companies and software

development teams. Richard has designed data

models including a common data warehouse

model used in government.

Richard has designed and implemented data

governance and data quality programs and

provides guidance and mentoring to business

managers and IT in the areas of data

governance, data quality and data warehouse.

Richard’s expertise includes strategic vision

along with a hands-on approach to problem

solving. Richard has broad industry experience

in utilities, financial services, government,

manufacturing, media, and

telecommunications third party services.

Richard is also an experienced helicopter and

fixed wing pilot.

Data Modeler 2020 – Future of

Data Modeling Panel

Michael Blaha, Deborah Henderson, Bill

Inmon, Dave Wells, Graham Witt

The processes, roles, and tools involved in

building applications are changing rapidly,

due primarily to big data, NoSQL, and very

shortly the Internet of Things. As our

environment changes, data modelers may need

to refine skills and techniques. Get a glimpse

into the future from five experts and ask your

questions!

UML Made Easy!

Norman Daoust, Daoust Associates

An introduction to the thirteen UML diagram

types and their relationship to data modeling.

We’ll focus on those most relevant to data

professionals. The presentation includes

examples of each of the thirteen diagram types

from a case study.

Attendees will learn:

which UML diagram type is closest to

a data model

which UML diagram type includes

entity names from your data model

which UML diagram type visually

illustrates the allowable state changes

of an entity from your data model

when to use each of the diagram types

Norman Daoust founded his consulting

company Daoust Associates,

www.DaoustAssociates.com in 2001. His

clients have included the Centers for Disease

Control and Prevention (CDC), the Veteran’s

Health Administration, the Canadian Institute

for Health Information, a Fortune 500 software


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company, and several start-ups. He has been

an active contributor to the healthcare industry

standard data model, the Health Level Seven

(HL7) Reference Information Model (RIM)

since its inception. He enjoys introducing data

and process modeling concepts to the business

analysis community and conducting business

analysis training courses. Norman’s book,

“UML Requirements Modeling for Business

Analysts” explains how to adapt UML for

analysis purposes.

Data Management Body of

Knowledge (DMBOK) Overview

Deborah Henderson, Broadstreet Data

The quick 101 course through the DMBOK for

those who need to consider how they might or

should use it as an operating framework for

data management.

An overview will be followed by techniques on

using the DMBOK as a guide for evaluating

current state and priorities, and where your

gaps are with staff skills and management

ownership.

DMBOK2 the new revision due in 2015 will

also be cited.

Deborah Henderson, B.Sc., MLS, PMP, CDMP

is the Data Governance Practice Manager for

Broadstreet Data in Toronto and teaches data

governance fundamentals classes publicly and

privately. She is Program Director for the

DAMA-DMBOK (Data Management Body of

Knowledge), a global effort going on since 2005.

With over 25 years in data management, she

consults in data governance in the energy,

capital markets, heath and automotive sectors.

Enterprise Conceptual Data

Modeling

Brian Shive, Microsoft

I will present a conceptual Enterprise Data

Model that is a union of models from IBM,

Boeing, AT&T and Microsoft. I will

demonstrate how views of the EDM can be

used for IT planning, scoping projects, defining

data requirements for IT packages, defining

data integration and mapping to conceptual

value chain models.

Learn how to communicate the data model to

business users and IT staff.

We will walk through the EDM in detail with

scenarios to demonstrate the value of the

EDM.

Brian started his data modeling career in the

late 1970s while working as a consultant to the

relational database gurus at IBM. Brian

learned from John Zachman at IBM how to

use the discipline of engineering when

designing data. Brian works at Microsoft

where during his 18 years he has served as

Microsoft Corporate Data Administrator,

Enterprise Architecture Lead Information

Architect, Principal Architect, Development

Manager and most-fun-one Developer. He spent

16 years with Boeing IT. Brian also worked as

Solar Energy Designer, Executive of Boy Scouts

of America, musician, comedian and poet and


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janitor. Brian and his wife and two children

live in the Seattle area. He teaches Aramaic in

his Methodist church and can be seen on

YouTube sounding at times like Jimi Hendrix.

He is working on a book of poetry and loves

teaching data modeling, database design and

data integration. The human brain and the

behavior it elicits have provided Brian with

years of study in neurology, psychology,

sociology and history. He is the author of the

novel, “Data Engineering”.

Normalization - The Achilles Heel

of Data Modeling

Gordon Everest, University of Minnesota

Many database professionals think they know

all about normalization, but when it gets down

to doing it, or explaining it to others in their

organization, they often stumble. This

workshop will test your knowledge, work on

some exercises, and learn (or reinforce) some

principles about record-based (ER/relational)

database design. This is an opportunity to

strengthen your own level of knowledge.

Unfortunately, there are also several myths

and misunderstandings surrounding the

concept that confuse and confound both

novices and experts. This session explores:

What is normalization, its history and

evolution, why it is important

The basic principles behind

normalization

What we need to know to perform

normalization

Learn and apply a practical, effective

method for recognizing and correcting

violations of normal forms

The consequences of not identifying

and correcting violations of the normal

forms

Why a DBMS or data modeling tool

cannot help data modelers produce a

normalized design

Since good design requires

normalization, what is

denormalization, and why do we

consider it?

Dr. Everest is Professor Emeritus of MIS and

Database in the Carlson School of

Management at the University of Minnesota.

With early “retirement”, he continues to teach

as an adjunct. His Ph.D. dissertation at the

Univ of Pennsylvania Wharton School entitled

“Managing Corporate Data Resources” became

the text from McGraw-Hill, “Database

Management: Objectives, System Functions,

and Administration” in 1986 and remained in

print until 2002!

Gordon has been teaching all about databases,

data modeling, database management systems,

database administration, and data

warehousing since he joined the University in

1970. Students learn the theory of databases,

gain practical experience with real data

modeling projects, and with hands-on use of

data modeling tools and DBMSs. Besides

teaching about databases, he has helped many

organizations and government agencies design

their databases. His approach transfers

expertise to professional data architects within

those organizations by having them participate

in and observe the conduct of database design

project meetings with the subject matter

experts. He is a frequent speaker at

professional organizations such as DAMA.


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Case Study: Data Warehousing

with SCD

Yoshihiko Hoshi and Hiroshi Yagishita,

Future Modeling Technologies

We will show you a case study of

implementing theoretical data warehouse

models into the real business field, including

how Slowly Changing Dimensions (SCDs) are

implemented in Japan. Also we will introduce

our case study of segmenting customers

(Customer Tags) in a women’s cosmetics

company.

Yoshihiko Hoshi has over 30 years experience

in data modeling, and is currently working for

a Japanese Major Bank. In Japan, he designed

the de-fact standard data model for financial

derivatives, Risk Management and Market

Data.

Hiroshi Yagishita is a system modeler and

consultant with over 30 years of application

development experience. Initially developed a

mainframe’s operating system, and currently a

database application specialist, especially

focusing on modeling technologies. Now

working for “Future Modeling Technologies”,

an independent solution provider in Japan.

Also a DAMA-Japan Member.

Mastering Master Data

Communication

Andrew Kapp, Westar Energy, Inc.

During software development and as

developers come and go, data modelers often

lack the time, software and methods to

adequately document and communicate

critical model details such as master data’s

lineage and metadata. In addition, as an

application or database matures, good

documentation can become increasingly

difficult to maintain and, without good and

readily accessible documentation a database

can become a mix of confusion and spaghetti.

This session will discuss methods to make

data (particularly master data) more intuitive

to manage and track for all of IT.

Andrew Kapp is the Enterprise Information

Architect for Westar Energy, Inc., a Kansas-

based power generation and delivery utility. He

has 14 years of experience in data management

and architecture on a diverse variety of

industries and data. Andrew specializes in

modeling techniques to improve information

awareness and accessibility among the IT and

data user communities.


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Ensemble Modeling

Hans Hultgren, Genesee Academy

Ensemble Modeling represents a family of

modeling approaches that share a common

purpose and a common modeling paradigm.

Ensemble forms address our need for data

integration, historization, auditability and

modeling agility. This session will cover the

need, the approach, the underlying premise

and the current flavors of Ensemble Modeling.

Attendees can expect to understand why

organizations should consider Ensemble

Modeling for their DWBI program.

President at Genesee Academy and a Principal

at Top Of Minds AB. Data Warehousing and

Business Intelligence educator, author,

speaker, and advisor.

Currently working on Business Intelligence

and Enterprise Data Warehousing (EDW) with

a focus on Ensemble Modeling and Data Vault.

Primarily in Stockholm, Amsterdam, Denver,

Sydney and NYC.

Published data modeling book “Modeling the

Agile Data Warehouse with Data Vault” which

is available on Amazon websites in both print

and Kindle e-reader versions.

Specialties: Information Management and

Modeling, Ensemble Modeling, Data Vault

Modeling, Agile Data Warehousing, Education,

e-Learning, Entrepreneurship and Business

Development.

Tools and Techniques for 21st

Century Data Modeling

Jeremy Posner, Synechron

For many years data modeling, which grew up

in the last century, has been bogged down by

the tools that modelers use and slow release

cycles due to highly manual effort at each

stage.

In the meantime, the software development

arena has moved ahead greatly and reaped the

benefits of tooling to deliver faster and more

iteratively. We call it “Agile” but you don’t

have to “be” Agile in order to reap these

rewards.

We will present some innovative techniques

that allow your data model to deliver faster,

with higher quality and lower cost. We call

this “21st Century Data Modeling”, and

describe some tools and techniques that

actually allow us to achieve these goals.

You will learn:

why reducing the release cycle is so

vital for the success of an Enterprise

Data Model

how employing continuous integration

/ delivery techniques from software

development benefits the data model

development

how to reduce re-work by

implementing model checks and test

harnesses

output artifacts you can deliver from

the modeling cycle

testing your model for backwards

compatibility in a programmatic way

techniques to deal with breaking

changes, when they absolutely need to

happen.


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Jeremy Posner is Senior Director – Data

Strategy and Services at Synechron, and a

Data Architect with 20 years experience mainly

within global financial institutions including

Morgan Stanley, Deutsche Bank, Merrill Lynch

and JP Morgan. He specializes in enterprise

data architecture, modeling, tools, standards

and metadata. Still a hands-on technician, he

strives to apply new practices and technologies

to common data problems with a “different and

better” mindset.

Prove it! Verifying the worth of

data modeling with ROI analysis

Kim Medlin, Wells Fargo

In software development organizations where

the culture of data modeling isn’t ingrained,

upper management will probably need to be

convinced that the additional effort required to

model the data is worthwhile to the enterprise.

In these cases, being able to determine the

return on investment (ROI) of data modeling

will be imperative. To do so, you must

understand the general value proposition of

data modeling. In this session, learn how to

justify the time and effort required to

implement data modeling. Appreciate the

benefits of data modeling, such as reduced

maintenance, improved data quality, enhanced

requirements definitions, and a value-added

communications channel. Learn to calculate

the data modeling ROI using approaches such

as cost-benefit, percentage of project savings,

percent of maintenance savings, and percent of

development.

A Data Architect for over 20 years, Kim Medlin

has worked for Fortune 100 companies as well

as high-powered consulting firms. His data

modeling expertise runs the gamut from

healthcare to banking to warehouse

automation. Having held a variety of both

technical and managerial roles, Kim is a

stalwart of data modeling and Data

Architecture evangelism. Kim has worked

directly with Wells Fargo, Keane Consulting,

Xerox, BB&T, and Data General. Kim received

his Mathematics degree (with a concentration

in Computer Science) from Appalachian State

University.

Data Integration Tips and Tricks

Bob Conway, Information Engineering

Associates

Data integration is the cornerstone of data

warehousing (DW) and master data

management (MDM) but has proven to be one

of the more difficult technical challenges. This

presentation provides helpful techniques for

merging data from disparate functional areas

of organizations into a unified semantic data

model. The approach leverages a modular

design and metadata-driven method to provide

a durable, extensible data integration tool kit.


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Bob Conway has 30+ years expertise in data

modeling, data architecture and data

warehouse design. As an internal resource and

as a consultant Bob has implemented dozens of

successful DW/BI projects in industries as

diverse as telecommunications, financial

services, health care, manufacturing, retail,

and Oil and Gas. He developed the RAPID®

Architecture and Methodology and teaches

classes on data modeling, architecture, and

agile development. He was an adjunct faculty

member in graduate programs at University of

Denver and University of Colorado. He brings

his rich business and technical experience to

the classroom as meaningful examples. His

presentation style is informative and

entertaining.

Protecting Personal Data From

Hackers

Cathy Nolan, Allstate, and Ashley Wilson,

Attorney at Law

Data Analysts and Data Modelers have a

unique opportunity to preview the amount of

data their company is collecting and to

consider if it is all necessary information to

run the business or just “nice to have”.

Questions to be asked should include: Is this

data secure from both internal and external

hackers? What data should be tagged as

“sensitive” data? Is the company breaking any

privacy laws by storing personal data? Who

within the company has access to personal

data? What happens if there is an external

data breech?

At a personal level, your identity and personal

data is being amassed by data brokers every

time you log on to your laptop, use your cell

phone, access an app, or use your GPS.

Companies are collecting a variety of data

about you, combining it with location

information, and using it to both personalize

their own services and to sell to advertisers for

behavioral marketing. Law enforcement

agencies are tracking your car and insurance

companies are installing devices to monitor

your driving. Clerks are making copies of your

credit cards. And if that wasn’t enough, the

FBI has reported that hackers have been

discovered embedding malicious software in

company computers, opening a virtual door for

criminals to rifle through an organization’s

valuable personal and financial information.

In additional to warning you about the ways

your data can be stolen, this presentation will

offer suggestions for limiting the amount of

personal data that is available to be seized and

divulged.

Cathy Nolan has an MBA in Business

Administration and 30 years’ experience as an

Information Analyst. When she became a

victim of identity fraud through the hacking of

her credit card information, she began

extensive investigation into credit card and

identity theft along with the many ways

personal information is being compromised.


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Ashley Wilson is an Attorney at Law practicing

in Illinois and Wisconsin. She is a graduate of

the University of Illinois and received her law

degree at Marquette University. As an attorney

she became interested in the growing threat to

privacy and the lack of legal protection

afforded to individuals by the government and

our court system.

How to talk Data to Non-Data

People

Jill Camper, DST Systems, Inc.

Do you ever find it frustrating to talk about

data to those who just don’t seem to know how

to “talk data”? This session will give specific

tips on how to talk to data to non-data people

so that you can both be on the same page and

get your projects and ideas approved.

Jill has over 16 years of data management

experience in the financial services industry,

including high profile conversions. For the past

7 years she has been focused on data design in

both the mainframe and open systems world

including traditional RDMS design as well as

Data Warehouse, Star Schemas, and BI

oriented designs. She loves educating people on

things data and the importance of data in our

everyday lives.

Jill has her bachelor’s degree in Psychology

and her M.B.A and is also a Certified Data

Management Professional.

Data Modeler to Data Scientist - A

New Maturity Approach

Sanjay Shirude, ACCEL BI

We are experiencing the need for advanced

analytics pertaining to Big Data, Machine

Learning and the Internet of Things, which

carry large volumes of collected data. The role

of the Data Modeler is growing beyond its

traditional relationship with data to

constructing predictive models and

incorporating decision analysis in what closely

resembles the role of Data Scientist.

This session will provide you the framework to

grow from the traditional data modeler to

incorporating a combination of truly disruptive

drivers of business and social value. From this,

you will walk away with a new relationship to

the data ecosystem as a data professional.

Dr. Sanjay Shirude is the founder and CEO of

ACCEL BI. He has 25+ years experience as a

practitioner, consultant, and educator in the

field of Business Information Technology

Integration. He brings a simplified and


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balanced perspective to the integration of

business and technology, encompassing both

business leadership and technical roles,

focusing on areas such as Data Management,

Data Governance, Enterprise Data

Warehousing, Master Data Management

(MDM), and applied Business Intelligence

architecture/analytics. His experience covers

Agile, scrum, and SDLC waterfall

methodologies; with roles as a program

manager, scrum master, product owner,

analyst, trainer, and mentor Sanjay serves as

VP of Education and Research for DAMA-I,

holds a Ph.D. in Information Management;

Masters in Statistics, Masters in Management

and PMP, CDMP, and CBIP Certifications. He

is a contributor and co-author to DMBOKII

and Enterprise Data Management Expert.

Natural vs. Surrogate

Primary Keys in Logical Data

Models

Gary Whitney, Microsoft

Learn why identifying the Primary Key of an

Entity by finding its Natural key(s) makes for

a much better Logical Data Model than using

a Surrogate Key.

Actual examples will be used to illustrate how

data modeling errors can be discovered when

using natural keys, and how business data

rules may not be able to be modeled if a

surrogate key in used.

Gary has over 40 years of experience designing

and building software systems for major

corporations like Lockheed, Hydraulic

Research Textron, Howard S. Wright

Construction, Paccar, Met Life, and currently

Microsoft. He is a Principal Information

Architect creating data models for a multi-year

project to support the business operations for

the largest division of Microsoft.

Facilitation and Training Skills

for Data Professionals


A Business Process describes How Work Gets

Done; Data describes the Facts needed to

execute that Process. One without the other

has little value in organizations. If Process is

the body then Data is the nervous system

which makes the body function. In the fast

pace of business change and frequent

reorganizations, the Data Analysts and the

Business and Process Analysts should expand

their value to the organizations by cross-

pollinating their understanding of how to

facilitate Data and Process requirements more

effectively.

The art and craft of enabling individuals and

groups to discuss issues and opportunities

around a shared objective; and develop agreed

strategies for a common direction is generally

referred to as Facilitation. Facilitation also

includes enabling people to learn through

transfer of knowledge and training in specific

skills by a subject matter expert. The person

or persons skilled in Facilitation are called

Facilitators. The approach for creating

agendas, conducting research, and facilitating

sessions to deliver planned outputs is referred

to as the Facilitation Process.

Using a case study of process improvement

and data design, this workshop will provide

hands-on experience in how Data

Professionals can leverage facilitation

techniques and tools in their craft to be more


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effective in gathering requirements and

transferring knowledge to users, and other

data professionals.

What you will learn:

Adult Learning Theory and the

Learning Process

Multiple Intelligences Framework for

effective design and delivery of work

sessions

Session Leader qualities and

competencies; Session environment

setup

Methods and tools including the use of

engagers and energizers

Designing Agendas; Facilitation

Framework and how to self-develop for

success

For two decades Artie Mahal successfully led

mission-critical management support

programs as Effective Business Change

Regional Manager for North America and

Latin America at Mars International. While at

Mars International he developed and delivered

programs on Information Resource

Management, Business Change/Process

Management and Learning and Leadership

Development. His last role at the company was

to manage Training and Development

including the formation of Mars University in

North America. Artie has provided services on

four continents and has been a speaker at

national and international professional forums

including Seton Hall University’s MBA

program and Rutgers University Business

College. Artie Mahal is a Senior Consultant

with BPTrends Associates since 2006. He is

also the founder of ASM Group and is a

Business Process Management (BPM)

consultant and trainer, developing and

delivering BPM professional services privately

to corporations and publicly through Boston

University’s Corporate Education Center.

Artie is the author of two books: 1) How Work

Gets Done, Business Process Management,

Basics and Beyond, and 2) Facilitator’s and

Trainer’s Toolkit. Artie is an accomplished

facilitator and has facilitated workshops

internationally in North America, Europe and

Asia Pacific regions. His workshops are highly

interactive and use state of the art methods

such as a “brain compatible learning method.”

He has facilitated workshops for Strategic

Planning, Business Process Improvement,

Ideation, After Action Reviews and Project

Management. Artie is a certified trainer in

Business Process Management (BPM), Human

Change Management, Diversity and Project

Management.

Semantic Structure Analysis and

Dimensional Modeling


Well-designed dimensional data provides

business capability for iterative and

interactive analysis – getting quick answers to

business questions as they arise. Most

dimensional modelers begin with a set of

known business questions, and then develop

star-schema designs to answer those

questions. This approach, while theoretically

sound, has some deficiencies in practice.

Answering today’s known questions is a good

beginning. But the best and most sustainable

dimensional data structures are designed to

answer unanticipated questions well into the

future.

The challenge, of course, is how to design data

structures to answer unknown questions that

may occur at some future time. Direct

translation of questions into schema doesn’t


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get the job done. This tutorial describes an

alternative approach that enriches

dimensional models by examining the

semantic structure of a representative set of

questions. Translating semantics instead of

specific questions produces more robust

dimensional data models.


Techniques to parse business

questions and discover the underlying

semantic structure of those questions.

The skills needed to map business

question semantics as facts and

qualifiers.

How to translate facts and qualifiers

into logical dimensional models.

Tips and techniques to extend and

enrich dimensional models, thus

increasing their long-term value and

usefulness.

Dave Wells is actively involved in information

management, business management, and the

intersection of the two. As a consultant he

provides strategic guidance and mentoring for

Business Intelligence, Performance

Management, and Business Analytics

programs - the areas where business

effectiveness, efficiency, and agility are driven.

As an educator he plans curriculum, develops

courses, and teaches for organizations such as

TDWI and eLearningCurve. On a personal

level, Dave is a continuous learner, currently

fascinated with understanding how we think,

both individually and organizationally. He

studies and practices systems thinking, critical

thinking, lateral thinking, and divergent

thinking, and he now aspires to develop deep

understanding and appreciation for the art

and science of innovation.

Data Governance for Data

Modelers Workshop

Deborah Henderson, Broadstreet Data

Data Governance may seem like a faraway

topic to data modelers: too strategic to have

much direct impact on a modeler’s work. This

seminar will connect the strategy directly to

the modeler’s work and show the benefits of

data governance in a systemic way as

modelers always knew systems development

really works.

In this workshop we will look at:

Data Governance as a framework

What modelers are already doing - and

the connection to data governance

Modeling in context - architecture,

design, operations

What’s important and when -

principles based modeling

We will complete ‘hands on’ exercises:

Estimating templates – from model

discovery to model creation

Reporting on modeling activity and

governance scorecards

Organizing for Quality, and modelers

place in this


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Deborah Henderson, B.Sc., MLS, PMP, CDMP

is the Data Governance Practice Manager for

Broadstreet Data in Toronto and teaches data

governance fundamentals classes publicly and

privately. She is Program Director for the

DAMA-DMBOK (Data Management Body of

Knowledge), a global effort going on since 2005.

With over 25 years in data management, she

consults in data governance in the energy,

capital markets, heath and automotive sectors.

About MongoDB


This is an introductory training on MongoDB.

We'll explain what it is, when you should use a

document database like MongoDB.

You'll learn:

how to install MongoDB

how to insert data into MongoDB

how to query data from MongoDB

basic schema design in MongoDB

clustering and network topologies

monitoring with Mongo Management

Service









UML in Depth

Norman Daoust, Daoust Associates

An in-depth look at those UML diagram types

of most importance to data professionals: use

case, activity, class, object, state machine,

timing, sequence, communication and package.

The presentation includes best practice

guidelines and tips for each of these diagram

types. They will be illustrated with examples

from a case study. We will briefly illustrate

how to model services and their operations for

Service Oriented Architecture (SOA).

Note: This is not an introductory

session. Attendees should be familiar

at least with use case and class

diagrams.


for each of the listed diagram types:

modeling tips, diagram layout tips,

naming guidelines

the relationships between the different

diagram types

how these diagram types can assist

data professionals in their work

Norman Daoust founded his consulting

company Daoust Associates,

www.DaoustAssociates.com in 2001. His

clients have included the Centers for Disease

Control and Prevention (CDC), the Veteran’s

Health Administration, the Canadian Institute

for Health Information, a Fortune 500 software

company, and several start-ups. He has been

an active contributor to the healthcare industry

standard data model, the Health Level Seven

(HL7) Reference Information Model (RIM)

since its inception. He enjoys introducing data

and process modeling concepts to the business

analysis community and conducting business

analysis training courses. Norman’s book,

“UML Requirements Modeling for Business

Analysts” explains how to adapt UML for

analysis purposes.


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The Journey to an Enterprise

Data Model

Sherri Adame, Premier Farnell

Metadata management is a reasonable place

to begin if your organization is not practicing

data or information governance formally. If

data requirements are always a last thought

or rarely considered, Metadata can start your

organization’s steps to developing a data

modeling practice. This presentation is about

Premier Farnell’s Metadata management

journey to maintaining an enterprise data

model.

Sherri Adame is passionate and evangelizes all

things data. She is well respected and speaks

frequently on data governance and various

data management topics. One of the Top 25

information managers in 2011 by Information

Week, she constantly challenges status quo. She

has an engaging personality and style that

makes the dullest of topics engaging and

exciting for all levels of an organization.

Evaluating Data Modeling Tools?

Helping You to Decide

George McGeachie, Metadata Matters

The evaluation and selection of a data

modeling tool for your organization can be a

daunting task. Not only are there numerous

technical criteria and requirements, but there

are often political, organizational and cultural

challenges as well. The place of data modeling

in the organization, the types of models to be

created (Enterprise, Conceptual, Logical,

Physical), and not forgetting any historical

considerations e.g., a database administrator

may have a “favorite” tool that he has used in

the past. The corporate standard might dictate

yet another technology, which may not align

technically with your particular project. There

may be a push to use technical checklists or

formal RFPs that may not apply to your

individual needs. Information from vendors

may be flavored with their own particular

strengths, which may not be relevant to your

requirements. So how do you sort through all

of these conflicting messages to choose the tool

that is right for you and your culture?

It is imperative that your organizations’

requirements be fully understood, documented

and prioritized, and that the team responsible

in the decision process clearly highlights the

implications of requirements before the

evaluation process gets too far along, and that

the team be well versed in diplomacy and

stakeholder management.

This presentation will describe the factors to

consider – technical, organizational and

cultural when evaluating a data modeling tool

and share a simple 10 step process that

anyone can adopt.

To avoid frustrations and streamline the

decision making process, leverage this 10-step

guide to evaluate each data modeling solution

best suited for your unique business needs. It

will enable the evaluation team to make a

strategic and sound decision, and maybe even

make you the data modeling evaluation hero.


Page 17

George McGeachie has spent his working life

creating, managing and linking data models,

process models, and others. He encourages

organizations to connect and utilize their

metadata islands, to recognize the wealth of

information contained in their data models, to

recognize that the creation of data models must

form part of an integrated approach to

improving their business, and therefore

recognize the importance of avoiding the

creation of islands of metadata in the first

place.

Crossing the Unstructured Barrier

Bill Inmon, Forest Rim Technologies

The most exciting advances in technology have

been made in the arena of incorporating

textual data into the corporate decision

making process. This presentation addresses

the reality of textual exploitation of medical

records, call center information, restaurant

and hotel feedback analysis, and other arenas

where text is found.

Bill Inmon – the “father of data warehouse” –

has written 53 books published in nine

languages. Bill’s latest adventure is the

building of technology known as textual

disambiguation – technology that reads raw

text in a narrative format and allows the text to

be placed in a conventional data base so that it

can be analyzed by standard analytical

technology, thereby creating unique business

value for Big Data/unstructured data. Bill

was named by ComputerWorld as one of the ten

most influential people in the history of the

computer profession. For more information

about textual disambiguation refer to

www.forestrimtech.com.

Modern Data Architecture? Or

Fresh Messaging for Familiar

Concepts?


The data and analytics market is vastly

different than what existed only two to three

years ago. The number of ‘big data’ technology

alternatives is staggering. The momentum of

open-source Hadoop is undeniable. Numerous

organizations are confounded by how to

proceed.

Throughout this evolution, however, many

fundamental data architecture requirements

have remained remarkably stable.

Organizations still must ingest and process

new sources of data. Organizations still

perform cross-functional analysis to glean

insights. Organizations still optimize data for

access and carry out rapid experiments. Many

data architecture constructs are as applicable

today as they were 10-15 years ago. In fact,

some have been extended and rebranded using

terms such as ‘Modern Data Architecture’ and

‘Data Lake’.

This presentation examines the emergence of

reference information architectures gleaned


Page 18

from numerous client projects to help make

sense of the confusing marketplace, and in the

process, paint a vision for future data and

analytic solutions.

For over two decades, Eddie has been helping

large organizations gain sustainable

competitive advantage with data. He has

worked at length in various roles including

enterprise data management, enterprise

architecture, data modeling and data

warehousing. Eddie joined Teradata in 2008

and has since conducted numerous

engagements with clients, helping to set

direction for data management. Prior to

joining Teradata, Eddie was a Data Architect

at CheckFree Corporation, the largest provider

of e-billing and electronic bill payment in the

US. Previously, Eddie held similar positions at

Macys Systems and Technology and Inacom

Corporation. Eddie currently serves as the VP

of Programs for the Georgia chapter of Data

Management International (DAMA) and is a

frequent speaker at industry events.

Advanced SQL Queries

Michael Blaha, Modelsoft Consulting

SQL is underutilized in software development.

Way too many developers think there is

nothing to SQL but store and retrieve. For

example, many programmers use a layer to

hide a database with the layer storing and

retrieving data a record at a time.

This session will explain the motivations for

using advanced SQL.

We will include multiple examples of advanced

SQL queries. The queries will illustrate some

of the possibilities. We expect the queries to be

helpful templates for attendees to use in their

own work.

The session will present business situations

where we have used advanced SQL queries.

For example, we often write meta-SQL, SQL

code that generates SQL code. For one project

we read an ERwin data dictionary and with

meta-SQL generated database comment

commands to load comments for each table

and column. As another example, we wrote

SQL queries to look for time gaps and overlaps

in staged data for a data warehouse.

Advanced SQL queries fit well into application

development. One technique is to encapsulate

advanced SQL logic within stored procedures

that provide an API to application

programmers.

Michael Blaha is a consultant and trainer who

specializes in conceiving, architecting,

modeling, designing, and tuning databases. He

has worked with dozens of organizations

around the world. Blaha has authored seven

U.S. patents, seven books, and many articles.

His most recent book is “UML Database

Modeling Workbook”. He received his doctorate

from Washington University in St. Louis and is

an alumnus of GE Global Research in

Schenectady, New York.


Page 19

Accounting: The Essence

David Hay, Essential Strategies, Inc.

Data modelers typically are not big fans of

accounting. When they took the introductory

class in college, they found it to be about lots of

arithmetic procedures for dealing with

seemingly obscure topics─but somehow the

underlying structure and nature of accounting

never quite got across. (OK, it’s true. “They” in

this case is Dave Hay, the author of this

presentation.)

In preparing his first book, Data Model

Patterns: Conventions of Thought, Dave Hay

finally figured it out: Accounting is in fact a

business modeling language itself--one that

predates data modeling by some 400 years. It

has a rigorous structure, which is, as modeling

languages go, remarkably clever.

What this means is that, to model accounting

is not like modeling any other subject area.

This is in fact a meta-model. It has links to the

entire model of an enterprise’s data─but it is

fundamentally at right angles to it.

This presentation will describe Dave Hay’s

data model of accounting (well, double-entry

bookkeeping), and will include some recent

insights into how computed fields can enforce

the underlying rules that must be followed.

People with an accounting background may

get new insights into the nature of their field,

while those who’ve never been able to

understand accounting will have the

opportunity, for the first time, to get a clear

understanding of exactly what it is about.

In the Information Technology Industry since it

was called “Data Processing”, Dave Hay has

been producing data models to support

strategic and requirements planning for nearly

thirty years. He has worked in a variety of

industries, including, among others, banking,

clinical pharmaceutical research, and all

aspects of oil production and processing.

For over 22 years he has been President of

Essential Strategies, Inc., a consulting firm

dedicated to helping clients define corporate

information architecture, identify

requirements, and plan strategies for the

implementation of new systems. Dave is the

author of the original data model patterns

book, Data Model Patterns: Conventions of

Thought, as well as books on requirements

analysis and metadata after that.

In 2011, he published the successor book,

Enterprise Model Patterns: Describing the

World. This is a comprehensive set of patterns

addressing enterprise models from several

levels of abstraction. Since he took the unusual

step of producing model patterns in UML, a

follow-on book, UML and Data Modeling: A

Reconciliation was published recently. He

has spoken at numerous international and

local DAMA, user group, and other conferences.


Page 20

Using ISO 8000 to Measure the

Quality of Master Data

Peter R. Benson, Electronic Commerce Code

Management Association (ECCMA)

This one hour presentation focuses on ISO

8000, the international standard for quality

data, and how the standard is used in

measuring the quality of master data. By the

end of this presentation you will be able to use

ISO 8000 to objectively measure the quality of

master data.

Mr. Peter Richard Benson is the Founding and

Executive Director of the Electronic Commerce

Code Management Association (ECCMA). The

international association was founded in 1999

to develop and promote the implementation of

co-operative solutions for the unambiguous

exchange of information.

Peter has enjoyed a long career in data driven

systems starting with early work on debugging

Vulcan the precursor of what became dBase,

one of the early relational database

applications designed for the personal

computer market. Peter went on to design

WordStar Messenger, one of the very first

commercial electronic mail software

applications which included automated high to

low bit conversion to allow eight bit word-

processing formatting codes to pass through the

early seven bit UNIX email systems. Peter

received a British patent in 1992 covering the

use of automated email to update distributed

databases. From 1994 to 1998 Peter chaired

the ANSI committee responsible for the

development of EDI standards for product data

(ASC X12E). Peter was responsible for the

design; development and global promotion of

the UNSPSC as an international commodity

classification for spend analysis and

procurement. Most recently, in pursuit of a

faster, better and lower cost method for

obtaining and validating master data, Peter

designed and oversaw the development of the

eOTD, eDRR and eGOR as open registries of

terminology, data requirements and

organizations mirrored on the NATO

cataloging system. Peter is also the project

leader for ISO 8000 (data quality) and ISO

22745 (open technical dictionaries).

From Operational to Analytics: An

Exploration of Data Model Designs

for Software Business

Applications

Ralph Hollinshead and Goran Stanisic,

SAS Institute

Physical database design decisions are largely

dependent on the expected workload but can

also vary widely depending on the database

technology being used. In this session, we will

cover some real world examples of SAS

Industry solution data models deployed and

designed for different database targets. These

data models include areas such as customer

intelligence, sensor data for machine

maintenance, and banking risk data. In

addition to exploring the general design

decisions and tradeoffs made, we will cover

some of advanced techniques such as the use

of JSON datatypes and partitioning strategies.

We will also look in to design decisions for

Hadoop Hive tables as well as NoSQL

databases such as Hbase. Attendees of this

session will gain a good real world overview of

advanced physical data model design

techniques that they may consider useful for

their own data model work.


Page 21

Ralph Hollinshead is an experienced leader in

data modeling database design and

development in Financial Services,

Government, Life Sciences, Retail, and

Communications. He has domain knowledge as

well as database expertise in SAS, Oracle,

Teradata, Hadoop and other major relational

databases.

His proven experience includes team

development and management in database

design for both operational and business

intelligence usage as well as developing for Big

Data implementations in Hadoop

environments. He is the development manager

for SAS Banking industry reference data model

as well as oversight responsibility for the data

architecture for SAS industry solutions. He is a

leader in setting and implementing corporate

standards for data architecture.

I have over 25 years of professional IT

experience in the healthcare, life sciences,

insurance, and financial industries. I possess

hands-on experience across the full data

warehouse development lifecycle, and have

managed and lead teams of data architects.

In last 10 years I have held roles ranging from

a data warehouse Solution Architect, Principal

Data Integration Architect to an Enterprise

Data Architect. I offer both breadth and depth

of knowledge in the areas of data warehouse

architecture, as well as development and

adoption of data modeling standards and best

practices.

Understaffed with data modelers?

How to train developers as

apprentice data model reviewers

Sally Greenwood, TDS Telecom

Are you envious of IT shops with a large team

of expert data modelers? Are you struggling to

“make do” with one or two, and agonizing over

the projects you have to let go into production

with problem-laden data designs?

Learn to leverage the small number of expert

data modelers in your IT shop by creating a

Logical Data Model Review program staffed by

apprentice data modelers from your

development teams.

This session provides a practical, step-by-step

plan to start a successful logical data model

review program that leverages your scarce

expert data modeling resources.

Learn how to:

Select a group of apprentice data

modelers from the development staff

who can review the less-complicated

data models for projects

Train and mentor the apprentices

Set up a simple process for project

teams to request a reviewer

Define guidelines for assigning

reviewers, and which reviews should

be reserved for one of the Data

Architects

Document the results of the review


Page 22

Partner with the DBA staff to ensure

only reviewed designs are

implemented.

Sally Greenwood, CBIP, is a Data Architect

with TDS Telecom in Madison, WI. She has 25

years of experience designing and reviewing

logical data models for operational and data

warehouse systems for a wide variety of

businesses and organizations, including retail,

manufacturing, health care, and

telecommunications. She has been writing and

teaching Logical Data Modeling, Requirements

Analysis, Complex Decision Making and

Professional Facilitation courses since 1985.

Her current Logical Data Model Review

Program is entering its sixth year and has

reviewed over 300 projects. Some of her greatest

professional satisfaction comes from

identifying and growing talent in others.

MapReduce vs. OLAP – Do These Two

Worlds Collide?


The emergence and popularity of MapReduce

brings a new debate to the world of data

management. Are MapReduce and OLAP

compatible, competitive, or conflicting? A

simple web search of “MapReduce and OLAP”

yields interesting results:

OLAP is Becoming Obsolete

Can OLAP be done in BigTable?

MapReduce or Data Warehouse?

When the noise quiets and the dust settles I

believe we’ll find that both OLAP and

MapReduce are alive and well. Each is

designed for a different purpose – OLAP for

interactive analysis of data and MapReduce

for making sense of big data. This presentation

explores how big data – specifically key-value

pair data from MapReduce – works to enrich

and extend dimensions and to increase the

value that we can deliver with OLAP

databases.

Dave Wells is actively involved in information

management, business management, and the

intersection of the two. As a consultant he

provides strategic guidance and mentoring for

Business Intelligence, Performance

Management, and Business Analytics

programs - the areas where business

effectiveness, efficiency, and agility are driven.

As an educator he plans curriculum, develops

courses, and teaches for organizations such as

TDWI and eLearningCurve. On a personal

level, Dave is a continuous learner, currently

fascinated with understanding how we think,

both individually and organizationally. He

studies and practices systems thinking, critical

thinking, lateral thinking, and divergent

thinking, and he now aspires to develop deep

understanding and appreciation for the art

and science of innovation.

Data Vault Fundamentals and

Workshop

Hans Hultgren, Genesee Academy


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This Data Vault Workshop is a highly

interactive session. Attendees will first receive

a data vault introduction (and modeling

primer) and then will participate in an active

modeling exercise.

The presentation will cover the drivers for

choosing data vault modeling, the core

fundamentals of the data vault modeling

approach, and several practical insights for

applying data vault modeling in your

organization. There will be time for questions

and discussion concerning topics of interest

from the audience. The modeling exercise is a

small group interactive modeling lab where

attendees will review a business case and work

together to create an effective data vault

model. Groups will then present their models

to the larger group and field questions and

comments from fellow attendees.

President at Genesee Academy and a Principal

at Top Of Minds AB. Data Warehousing and

Business Intelligence educator, author,

speaker, and advisor.

Currently working on Business Intelligence

and Enterprise Data Warehousing (EDW) with

a focus on Ensemble Modeling and Data Vault.

Primarily in Stockholm, Amsterdam, Denver,

Sydney and NYC.

Published data modeling book “Modeling the

Agile Data Warehouse with Data Vault” which

is available on Amazon websites in both print

and Kindle e-reader versions.

Specialties: Information Management and

Modeling, Ensemble Modeling, Data Vault

Modeling, Agile Data Warehousing, Education,

e-Learning, Entrepreneurship and Business

Development.

Corporate Dictionary Workshop

Peter R. Benson, Electronic Commerce Code

Management Association (ECCMA)

This is a workshop on the creation,

management and use of a corporate dictionary.

A dictionary includes metadata but also

classes and controlled values, basically all the

concepts used in identifying and describing

individuals, organizations, goods or services.

The workshop explores ISO 22545 Part 10:

Dictionary representation and Part 11:

Guidelines for the formulation of terminology.

The workshop looks at how the corporate

dictionary is used in stating requirements for

master data. By the end of this session you

will be able create an ISO 22745 corporate

dictionary and you will be able to use a

corporate dictionary to state ISO 22745

requirements for master data.

Mr. Peter Richard Benson is the Founding and

Executive Director of the Electronic Commerce

Code Management Association (ECCMA). The

international association was founded in 1999

to develop and promote the implementation of

co-operative solutions for the unambiguous

exchange of information.


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Peter has enjoyed a long career in data driven

systems starting with early work on debugging

Vulcan the precursor of what became dBase,

one of the early relational database

applications designed for the personal

computer market. Peter went on to design

WordStar Messenger, one of the very first

commercial electronic mail software

applications which included automated high to

low bit conversion to allow eight bit word-

processing formatting codes to pass through the

early seven bit UNIX email systems. Peter

received a British patent in 1992 covering the

use of automated email to update distributed

databases. From 1994 to 1998 Peter chaired

the ANSI committee responsible for the

development of EDI standards for product data

(ASC X12E). Peter was responsible for the

design; development and global promotion of

the UNSPSC as an international commodity

classification for spend analysis and

procurement. Most recently, in pursuit of a

faster, better and lower cost method for

obtaining and validating master data, Peter

designed and oversaw the development of the

eOTD, eDRR and eGOR as open registries of

terminology, data requirements and

organizations mirrored on the NATO

cataloging system. Peter is also the project

leader for ISO 8000 (data quality) and ISO

22745 (open technical dictionaries).

About Hadoop


We give you a basic introduction to Hadoop,

What is it, how can you use it and why.

This includes an introduction to:

The Hadoop Ecosystem

HDFS: Hadoop Distributed Filesystem

YARN

Name Node

Map Reduce

Hive

Basic Installation

Basic operation

Reading/Writing/Navigating HDFS

Getting Data into Hive

Getting Data out of Hive









Data Modeling for Sustainable

Systems

Graham Witt, Ajilon

Systems, like any other high-value

acquisitions, should continue to work after

the warranty period, but far too often fail

to work completely as intended. Data

modelers can enhance the quality and

lifespan of a system if they take a broader

view than one which simply converts

information storage and retrieval

requirements into a data model. This

workshop looks at how to add value to the

data modeler’s contribution to system

development or package customization,

including:

identifying real-world complexity and

its implications for the choice of data

structures

the role of generalization in managing

such complexity

development of a common vocabulary

for real-world concepts, attributes,

relationships and processes, and use of

that vocabulary in system artifacts

and user interfaces

recognizing how the data is to be used

in Business Intelligence and the

implications of that use for choice of

data structures

analysis of how changes in the real

world are to be recorded in the chosen


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data structures, with implications for

process and user interface design

ensuring test plans cover data update

side-effects adequately

managing data model change

effectively.

The workshop includes a series of case

studies drawn from the speaker’s

experience dealing with these issues.

Graham has over 30 years of experience in

delivering effective data solutions to the

government, transportation, finance and utility

sectors. He has specialist expertise in business

requirements, architectures, information

management, user interface design, data

modeling, database design, data quality and

business rules. He has spoken at conferences in

Australia, the US and Europe and delivered

data modeling and business rules training in

Australia, Canada and the US. He has written

two textbooks published by Morgan Kaufmann:

“Data Modeling Essentials” (with Graeme

Simsion) and “Writing Effective Business

Rules”, and writes monthly articles for the

Business Rule Community

(www.brcommunity.com).

Advanced Data Modeling

Challenges Workshop

Steve Hoberman, Steve Hoberman &

Associates, LLC

After you are comfortable with data modeling

terminology and have built a number of data

models, often the way to continuously sharpen

your skills is to take on more challenging

assignments. Join us for a half day of tackling

real world data modeling scenarios. We will

complete at least ten challenges covering these

four areas:

NoSQL data modeling

Agile and data modeling

Abstraction

Advanced relational and dimensional

modeling

Join us as in groups as we solve and discuss a

set of model scenarios.

Steve Hoberman taught his first data modeling

class in 1992 and has trained more than

10,000 people since then, spanning every

continent except Africa and Antarctica. Steve is

known for his entertaining and interactive

teaching style (watch out for flying candy!),

and organizations around the globe have

brought Steve in to teach his Data Modeling

Master Class, which is recognized as the most

comprehensive data modeling course in the

industry. Steve is the author of seven books on

data modeling, including the bestseller Data

Modeling Made Simple. His latest book, Data

Modeling for MongoDB, presents a streamlined

approach to data modeling for NoSQL

solutions. One of Steve’s frequent data

modeling consulting assignments is to review

data models using his Data Model Scorecard®

technique. He is the founder of the Design

Challenges group, recipient of the 2012 Data

Administration Management Association

(DAMA) International Professional

Achievement Award, and highest rated

presenter at Enterprise Data World 2014.


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Competency Assessment for the

Data Professional


Professional Development does not happen by

accident. It requires an awareness of where

you are today in terms of your skills and

competencies; where you are headed and how

you would get there through proven methods

of development.

In the fast pace of the dynamic and global

work environment, one must be prepared to

embrace any change at any time: in what they

do, how they do it, and where they do it.

Employees are expected to continuously

demonstrate their value to the organization

through measurable performance. Being

stagnant in a role or in a given set of skills

therefore, is not an option. Review your

transferable and versatile skills: technical,

softer or organizational skills that can be

applied in a wide variety of positions across

the organization. They are your asset

inventory. Plan to enhance this asset through

a methodical approach to your professional

development.

This presentation will provide you with the

basic concepts of assessing your current skills,

principles of development, and planning tools

for enhancing your developmental journey.

What you will learn:

Understanding of Competency and

Skills, and their role in jour jobs

Self-Assessment tool: VADI (Variety,

Adversity, Diversity and Intensity)

Development Learning Framework:

70/20/10

Self-Development Mantra: How to

Write. Speak, Learn, Think, Present

and Network

Creating and maintaining an ongoing

developmental plan

For two decades Artie Mahal successfully led

mission-critical management support

programs as Effective Business Change

Regional Manager for North America and

Latin America at Mars International. While at

Mars International he developed and delivered

programs on Information Resource

Management, Business Change/Process

Management and Learning and Leadership

Development. His last role at the company was

to manage Training and Development

including the formation of Mars University in

North America. Artie has provided services on

four continents and has been a speaker at

national and international professional forums

including Seton Hall University’s MBA

program and Rutgers University Business

College. Artie Mahal is a Senior Consultant

with BPTrends Associates since 2006. He is

also the founder of ASM Group and is a

Business Process Management (BPM)

consultant and trainer, developing and

delivering BPM professional services privately

to corporations and publicly through Boston

University’s Corporate Education Center.

Artie is the author of two books: 1) How Work

Gets Done, Business Process Management,

Basics and Beyond, and 2) Facilitator’s and

Trainer’s Toolkit. Artie is an accomplished

facilitator and has facilitated workshops

internationally in North America, Europe and

Asia Pacific regions. His workshops are highly

interactive and use state of the art methods

such as a “brain compatible learning method.”

He has facilitated workshops for Strategic

Planning, Business Process Improvement,

Ideation, After Action Reviews and Project

Management. Artie is a certified trainer in


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Business Process Management (BPM), Human

Change Management, Diversity and Project

Management.

Six Habitual Architecture

Mistakes and How to Avoid Them


Is your architecture characterized by excessive

costs, supportability issues and business

dissatisfaction?

This session examines six habitual

architecture mistakes observed on numerous

client projects: employing a technology-driven

approach; allowing the architecture to

accidentally evolve; ignoring organizational

constraints; deviating from fundamental

principles; reinventing the wheel for common

design problems, straying from engineering

discipline. The presenter will explore four

architecture components that are essential for

avoiding the mistakes: a structured

architecture framework; architecture

principles and advocated positions; design

patterns and implementation alternatives;

reference architectures.

The session concludes with proven

recommendations for maturing architecture

capabilities. You will leave the session not only

better educated on architecture, but armed

with ideas for architecting high-quality

solutions in your own environment.

For over two decades, Eddie has been helping

large organizations gain sustainable

competitive advantage with data. He has

worked at length in various roles including

enterprise data management, enterprise

architecture, data modeling and data

warehousing. Eddie joined Teradata in 2008

and has since conducted numerous

engagements with clients, helping to set

direction for data management. Prior to

joining Teradata, Eddie was a Data Architect

at CheckFree Corporation, the largest provider

of e-billing and electronic bill payment in the

US. Previously, Eddie held similar positions at

Macys Systems and Technology and Inacom

Corporation. Eddie currently serves as the VP

of Programs for the Georgia chapter of Data

Management International (DAMA) and is a

frequent speaker at industry events.

Conducting Data

Modeling Project Meetings

Gordon Everest, University of Minnesota

We will cover best practices in working with

business subject matter experts to gather

information requirements which can lead to

the design of databases to support their

applications. Learn different methods

(extended series of meetings, accelerated JAD

session, interviews), when and how best to use

them, and advanced preparations. This session

is based on actual experiences and a

comparative research project.


Page 28

Dr. Everest is Professor Emeritus of MIS and

Database in the Carlson School of

Management at the University of Minnesota.

With early “retirement”, he continues to teach

as an adjunct. His Ph.D. dissertation at the

Univ of Pennsylvania Wharton School entitled

“Managing Corporate Data Resources” became

the text from McGraw-Hill, “Database

Management: Objectives, System Functions,

and Administration” in 1986 and remained in

print until 2002!

Gordon has been teaching all about databases,

data modeling, database management systems,

database administration, and data

warehousing since he joined the University in

1970. Students learn the theory of databases,

gain practical experience with real data

modeling projects, and with hands-on use of

data modeling tools and DBMSs. Besides

teaching about databases, he has helped many

organizations and government agencies design

their databases. His approach transfers

expertise to professional data architects within

those organizations by having them participate

in and observe the conduct of database design

project meetings with the subject matter

experts. He is a frequent speaker at

professional organizations such as DAMA.

Case Study: Roadmap to an

Enterprise Logical Data Model

Missy Wittmann, American Family

How many times have you attempted to create

an Enterprise Logical Data Model and had to

put the work aside to work on a project

model? Let’s get together to discuss an

approach that is a win – win for everyone.

Where do we start?

Who needs to be involved?

Socializing the efforts

Get everyone on the same page

Process/Process/Process

.

Missy Wittmann is an Information Modeling

Engineer Specialist at American Family

Insurance. Missy has worked in the data

modeling field for over fifteen years in various

roles. She started out as a business partner on

a project that was doing some data modeling

and enjoyed the process so much that she

changed career paths. Missy has facilitated

projects for Business Modeling, Logical and

Physical Data Modeling. Data Modeling is an

exciting place to be in the world of technology.

No matter what technology is being used to get

the end result, we always need our data!

Implementing Data Vault in a

Columnar Database

Petr Olmer, GoodData

This session presents some insights into what

it takes to implement a Data Vault data

warehousing solution in a columnar database,

Vertica specifically. Columnar storage

strategies and available analytical functions

imply different reasoning behind the design of

satellites and links.

You will learn the differences of Data Vault

implementation in classical row-oriented and

columnar databases. Lessons learned from

real implementations will be shared. Basic

knowledge of Data Vault architecture is

expected.


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Petr Olmer studied multi agent systems,

artificial intelligence, and declarative

programming. He saw big data for the first

time while working at Computer Centre at

CERN, The European Laboratory for Particle

Physics. Today he works at GoodData,

building tools and defining architecture and

methodology for BI implementations.

FoCuSeD Data Modeling -

facilitated data modeling

Gary Rush, MGR Consulting, Inc.

This interactive session is geared to enable

data analysts to facilitate Data Modeling

sessions with business clients. Gary will show

you how to facilitate, step-by-step, a data

modeling workshop and what skills or tools

are needed at each step. It is a brief summary

of Gary’s book, FoCuSeD Data Modeling Made

Easy. Attendees will learn:

How to build a Data Model with

business clients who have never seen a

data model.

How to use the modeling session to

clarify and re-engineer the business.

How Active Listening affects the model

and how to effectively listen to your

clients.

How to harness the collective

knowledge of your clients to build a

data model that they embrace. How to

make the model truly their model.

Gary Rush, IAF CPF, Founder and President

of MGR Consulting, Inc., attended the U.S.

Naval Academy and is a former Chair of the

International Association of Facilitators (IAF).

He is a recognized leader in the field of

Facilitation and Facilitator training,

managing projects since 1980, facilitating since

1983, and providing Facilitator training since

1985; and continues to be the leading edge in

the industry by continuing as a practicing

Facilitator.

As a Facilitator Trainer, he teaches FoCuSeD.

He teaches specific “how to” with an

understanding of the “why” to perform as an

effective Facilitator; he provides detailed

Facilitator and process tools, enhances his

training through effective learning activities,

and, as an IAF CPF Assessor, he covers the

IAF Core Facilitator Competencies and what

students need to do to achieve them. As a

Facilitator, he improves client business

performance through effective application of

exceptional facilitation processes and he is

highly skilled at engaging participants and

guiding them to consensus.

Gary has written numerous “how to” books,

including the FoCuSeD Facilitator Guide – a

comprehensive reference manual sharing his

step-by-step process so that students can

replicate his practices. His alumni often tell us

how much Gary has changed their lives.


Page 30

Writing effective business rules - a

practical method

Graham Witt, Ajilon

This workshop teaches modelers how to

collaborate with business stakeholders to

develop well-formed and consistent statements

of an organization’s business rules to enhance

business effectiveness, prepare for system

development, and obtain meaningful data from

customers and/or other organizations. This

workshop covers the following topics:

What is a business rule?

Why do organizations have rules?

What is the best way to document a

rule?

Why do we need to document rules?

Types of rule

An end-to-end rule development

process

Producing well-formed rule statements

Rule statement quality assurance.

Graham has over 30 years of experience in

delivering effective data solutions to the

government, transportation, finance and utility

sectors. He has specialist expertise in business

requirements, architectures, information

management, user interface design, data

modeling, database design, data quality and

business rules. He has spoken at conferences in

Australia, the US and Europe and delivered

data modeling and business rules training in

Australia, Canada and the US. He has written

two textbooks published by Morgan Kaufmann:

“Data Modeling Essentials” (with Graeme

Simsion) and “Writing Effective Business

Rules”, and writes monthly articles for the

Business Rule Community

(www.brcommunity.com).

The Data Modeler’s Road to the

Certified Data Management

Professional (CDMP)

Patricia Cupoli, CCP, CDMP, CBIP,

DAMA International

For the data modeler, the CDMP is a

designation identifying they have

demonstrated a standard level of knowledge

and experience within Data Management and

specifically Data Modeling. The CDMP is

offered through DAMA International and the

ICCP. During the first half of this workshop,

we will be discussing:

CDMP certification process

Topics, concepts and terms of the Data

Modeling exam

Preview of the IS Core and Data

Management Core exams

Workshop attendees will have the ability to

take the Data Modeling exam during the

second half of this workshop. The exam cost is

$285. As a special DMZ feature, you pay only

if you pass (passing is 50% or better).

Bring your own Windows-based laptops – the

USB drive has to be unencrypted as the exam

runs off this drive. Your exam results and

unofficial performance profile can be viewed

immediately.


Page 31

Patricia Cupoli, CCP, CDMP, CBIP,

TOGAF®9 Certified, is a course developer and

teaches online Data Management at Edmonds

Community College and ICCP CDMP courses.

She has an extensive background in the areas

of Data Governance, Data Warehousing,

Metadata Solutions and Repositories,

Enterprise Modeling (business process and

data) for Business Re-engineering, Project

Management, IT Strategic Planning, and

Librarianship / Information Science. She has

presented at many DAMA, TDWI and Data

Modeling Zone conferences, and has published

professionally. Pat is the 2006 winner of the

DAMA International Professional Award.

Pat is the DAMA International ICCP Director,

Project Manager for Data Exam Development,

DAMA Education Committee member, past

ICCP Board President, and a past president of

DAMA International, DAMA Chicago, and

DAMA Philadelphia / Delaware Valley. She is

the DMBOK2 Editor and was an author

contributor for two DMBOK chapters:

Documents and Content Management, and

Professional Development.

Data Modeling by Example -

Introduction and Workshop

Marco Wobben, BCP Software

In many DMZ presentations, data modeling is

described as both a craft and an art. For most

outsiders, data modeling is some kind of

magic: the data modeler interviews business

experts, studies piles of requirements, talks

some more, and then, hocus pocus, presents a

diagram with boxes, crow’s feet, arrows, etc.

Fact based information modeling is the very

opposite of such magic. It does not require

people to understand the modeler’s magical

language of boxes and arrows. Instead, fact

based information modeling uses natural

language to describe sample facts that are

intelligible for business people. Therefore, it is

also known as “Data Modeling by Example”.

Part 1 – Introduction

The presentation highlights:

the origins and key elements of fact

based modeling;

its place in the requirements

engineering process;

usage in large scale information

management;

some forward engineering capabilities

(ER, UML);

Part 2 – Workshop

In the workshop you will install a free

modeling tool on your own windows computer

and practice with:

verbalizing elementary facts;

modeling with fact expressions;

visualizing and validating your model;

generating output for business experts

and software engineers.

Marco Wobben is partner of BCP Software and

has been developing software for more than 30

years. He has developed applications for


Page 32

financial experts, software to remotely operate

bridges and a wide range of web applications.

For the past 10 years, he has been the main

developer of CaseTalk, the CASE tool for fact

based information modeling, which is widely

used in universities in the Netherlands and

beyond.