october 17-19 in portland, or - data modeling · october 17-19 in portland, or. ... er/studio sig...

October 17-19 in Portland, OR

Data Modeling Zone 2016

Monday, October 17

Foundational

Data Modeling

Agile and

Communication

Big Data and

NoSQL

Hands-On and

Case Studies

Advanced

Data Modeling

DA

MA

CD

MP

Te

stin

g –

Ta

ke

an

y C

DM

P e

xa

m w

he

n y

ou

wa

nt, in

clu

din

g th

e d

ata

mo

de

ling

ex

am

! (Gre

en

Rm

)

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

8:30-11:30 Data Modeling

Fundamentals

Steve

Hoberman, Steve Hoberman

& Associates,

LLC Billiard, Pg 1

Data Quality

for Data

Modelers

Sue Geuens, President of

DAMA

International Fireside, Pg 1

An Overview of

NoSQL

Database

Joe Celko

Library, Pg 1

Hands-On CA

ERwin Data

Modeler Primer

Jeff Harris, Sandhill

Card, Pg 3

Analytic

Architecture

Modernization

– A Practical

Approach

Eddie Sayer, Teradata

Renaissance,

Pg 3

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

12:00-1:00 KEYNOTES:

Nike United

Ryan Smith and Suzanne Kimble, Nike

The Blockchain Billionaire

Steve Hoberman

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

2:15-3:15

UML Made

Easy!

Norman

Daoust, Daoust

Associates Renaissance,

Pg 5

How to talk

Data to Non-

Data People

Jill Camper, DST Systems,

Inc. Card, Pg 6

Crossing the

Unstructured

Barrier

Bill Inmon, Forest Rim

Technologies Library, Pg 6

When little

meets BIG:

Practical Data

Modeling for

Analytics

Stacie Benton, Microsoft Billiard, Pg 6

Data Modeling

for Mobile

Data

Collection

Stephen

Gonzales, Reformatix,

LLC Fireside, Pg 7

3:15-3:45 Afternoon Snacks

3:45-4:45 Case Talk -

Data Modeling

by Example

Marco Wobben, BCP Software Fireside, Pg 12

Empowering

Business Users

to Lead with

Data

Denise

McInerney, Intuit Library, Pg 8

Enrichment

Modeling:

Dimensional

Modeling for

NoSQL

Datastores

Jim Cannaliato, Leidos Billiard, Pg 13

Creating

Ontologies from

your Existing

E/R Data

Models

Nicolas

Leveque, Deutsche Bank Card, Pg 9

Advanced

Aspects of

UML Data

Modeling

Michael

Blaha, Modelsoft

Consulting Renaissance,

Pg 10

5:00-6:00 Welcome Reception in the Governor Ballroom


Tuesday, October 18

Foundational

Data Modeling

Agile and

Communication

Big Data and

NoSQL

Hands-On and

Case Studies

Advanced

Data Modeling DA

MA

CD

MP

Te

stin

g –

Ta

ke

an

y C

DM

P e

xa

m w

he

n y

ou

wa

nt, in

clu

din

g th

e d

ata

mo

de

ling

ex

am

! (Gre

en

Rm

)


7:45-8:15

SIGs

Bring Your

Model to Life

Gertjan Vlug, Attunity Library, Pg 10

“Zen with Len”

Meditation with

Len Silverston, Universal Data

Models Renaissance,

Pg 11

WhereScape –

Data

Warehouse

Automation

Douglas Barrett,

WhereScape

Billiard, Pg 30

ER/Studio SIG

Ron Huizenga, Idera Card, Pg 12

erwin

Modeling SIG

Danny

Sandwell, ERwin Fireside, Pg 31

8:30-11:30 FoCuSeD™

Business Data

Modeling Made

Easy

Gary Rush, MGR Consulting,

Inc. Billiard, Pg 13

Practical Tools

for the Human

Side of Data

Modeling


Models Renaissance,

Pg 14

Data Science

Primer

Daniel D.

Gutierrez, AMULET

Analytics Library, Pg 15

Business-

Driven Data

Architecture - A

Practical,

Model-based

Approach

(Hands On)

Ron Huizenga, Idera Card, Pg 15

Only 362

days to

DMZ 2017!!

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

12:00-1:00 KEYNOTE: How To Punch A Shark In The Face, and other stuff

Gary Hall, Jr., Olympic Gold Medalist and Data Professional


2:15-3:15

Modeling Time

Petr Olmer, GoodData Fireside, Pg 16

Beyond Data

Models: A

Metadata

Model for the

Data Curator

Dave Wells, Infocentric Renaissance,

Pg 17

Taxonomies

and Ontologies

Bill Inmon, Forest Rim

Technologies Library, Pg 17

How to compose

a fact-based

model into any

kind of schema

Clifford Heath, Infinuendo Card, Pg 27

Bridging

disparate IT

systems with

an Enterprise

Data Model

Brian Shive, Microsoft Billiard, Pg 18

3:15-3:45 Afternoon Snacks

3:45-4:45 Introduction to

Dimensional

Data Models

Petr Olmer, GoodData Fireside, Pg 19

The Importance

of Fun in Data

Modeling and

Success!


Models Renaissance,

Pg 19

Better Data

Science

conversations

through

Conceptual

Data Modeling

Asoka Diggs, Intel Corp. Card, Pg 20

Caesars

Enterprise Data

Model

Patti Lee, Caesars

Entertainment

and Michael

Blaha, Modelsoft

Consulting Billiard, Pg 21

Scales,

Measurement

and Encoding

Scheme

Joe Celko

Library, Pg 21

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


Wednesday, October 19

Foundational

Data Modeling

Agile and

Communication

Big Data and

NoSQL

Hands-On and

Case Studies

Advanced

Data Modeling

DA

MA

CD

MP

Te

stin

g –

Ta

ke

an

y C

DM

P e

xa

m w

he

n y

ou

wa

nt, in

clu

din

g th

e d

ata

mo

de

ling

ex

am

! (Gre

en

Rm

)


8:00-11:00 Introduction to

Linked Data

and the

Semantic Web

Cody Burleson, Base22. Library, Pg 29

Data

Management

Maturity – Why

We Need It and

How It Can

Propel You to

DM Leadership

Melanie Mecca, CMMI Institute Fireside, Pg 22

Modern Data

Governance:

Implications of

Agile, Big Data,

and Cloud

Dave Wells, Infocentric Renaissance,

Pg 23

Hands-On

PowerDesigner

Jeff Giles, Sandhill

Card, Pg 23

Advanced

Data Modeling

Challenges

Workshop

Steve

Hoberman, Steve Hoberman

& Associates,

LLC Billiard, Pg 24

11:15-12:15 Conceptual vs.

Logical vs.

Physical Data

Modeling - a

Contrarian

View

Gordon

Everest,

University of

Minnesota

Card, Pg 25

Even non-

relational

databases have

relationships

Pascal

Desmarets, Hackolade

Renaissance,

Pg 24

Machine

Learning

Primer

Daniel D.

Gutierrez, AMULET

Analytics Library, Pg 26

Only 362

days to

DMZ 2017!!

Essential Data

Modeling

David C. Hay,

Essential

Strategies, Inc.

Billiard, Pg 26


1:15-4:15

Business

Information

Modeling using

the fact-based

approach

Clifford Heath, Infinuendo Renaissance,

Pg 27

Agile for Data

Professionals

Larry Burns, PACCAR Card, Pg 28

Data Modeling

in the NoSQL

World

Ted Hills, LexisNexis Library, Pg 8

Only 362

days to

DMZ 2017!!

UML in Depth

Norman

Daoust, Daoust

Associates Billiard, Pg 29


Page 1

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 has trained more than 10,000

people in data modeling since 1992. 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 nine books on data modeling, including the

bestseller Data Modeling Made Simple. 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 and recipient of the

2012 Data Administration Management

Association (DAMA) International Professional

Achievement Award.

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 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 2

year contract to implement a Data Governance

program at SA’s largest mobile operator. She is

currently on a 6 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.

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.

An Overview of NoSQL Database

Joe Celko

The traditional SQL database evolved from file

systems. It assumes that all data can be expressed

in scalar values in the columns of the rows of a

table, just as file systems used fields in records of

files. But the truth is that not all data fits into a

traditional SQL model.

The storage models have changed. Columnar

databases have no rows, but keep tables as

columns which can be assembled into rows if

needed. This means that query optimizations are

very different. The MapReduce Model and Key-

Value Stores do not have tables at all. Cloud

storage is not like traditional, local file systems.

Parallel processing and brute force replace clever

indexing optimization.

Traditional SQL assumes that the data is static;

think about common business applications like

accounting and transaction processing. Changes to

the data are explicit actions done by the database.

Actions against the data are taken in other tiers of

the application. But Streaming Databases (also

known as Complex Event models) assume that

data is constantly changing on its own. This would

include sensor monitoring, stocks, commodity

trades, and so forth. These events occur so fast

that responses have to be built into the database.

Graph Databases do not have a table concept at

all! They model pure relationships, expressed as a

graph structure. SQL assumes that we know the

relationships in the data. Graph databases

discover the relationships. They are used for social

network analysis, patterns in various kinds of

flows. Think about a SQL query to find the

minimal set of “the cool kids” in a social network

whose adoption of a product makes it into the next

trend.

Textbases or document databases have no concept

of structured data. Instead of syntax, they get

meaning from semantics. For example, Nexis,

Lexis and Westlaw are used by researchers and

lawyers to locate documents. Think about trying to

write an SQL query to determine if a TV dinner

meets the contractual obligation of a company to

provide off-site employees with a meal equivalent

to a fast-food order.

One of Dr. Codd’s 12 rules for RDBMS systems

was that there must be a linear language to control


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and access the database. But Geographical Data

(GIS) deal with maps, so they are 2 or 3

dimensional by nature. Their query languages are

often graphic tools. For example, you can draw a

polygon on a map and then ask how many people

live inside it.

SQL is not always the right answer; it depends on

the question!

Joe Celko served 10 years on ANSI/ISO SQL

Standards Committee and contributed to the SQL-

89 and SQL-92 Standards. He is author of eight

books on SQL for Morgan-Kaufmann: SQL for

Smarties (1995, 1999, 2005, 2010), SQL Puzzles &

Answers (1997, 2006), Data & Databases (1999)

and Trees & Hierarchies in SQL (2004), SQL

Programming Style (2005) and Analytics & OLAP

in SQL (2005) and Thinking in Sets (2008). He has

written over 1200 columns in the computer trade

and academic press, mostly dealing with data and

databases.

Hands-On CA ERwin Data Modeler

Primer

Jeff Harris, Sandhill

This is an intensive short course on the CA ERwin

Data Modeler toolset. It is designed to help users

understand some of the more advanced features of

the tool and how to get better usage of your

existing CA ERwin Data Modeler investment. It

will assist users in being more productive and

leverage the re-usability features of ERwin Data

Modeler. This will be an instructor led course with

examples, illustrations and student exercises.

An existing knowledge of the CA ERwin Data

Modeler is advised, as this course will not be

covering the basics, though for those that are

interested in investing in the tool, we welcome you

to attend to understand the power of the tool.

What you will learn:

The benefits and workings of Domains,

The benefits and workings of Model

Naming Standards,

Also covering some dynamics of Name

Hardening,

The benefits and workings of Template

Models,

And we will explore some aspects of how to

work towards Data Modeling Standards to

achieve Data Governance.

Jeff Harris is Sandhill Consultants’ technical

services manager in North America and comes with

over 20 years of IT experience and has specialized

in Data Modeling & Data Architecture for more

than 11 years. He headed up the Sandhill

Consultants’ business in South Africa and due to

the great success he achieved there, was invited to

come to the USA to bring the same passion and

drive to replicate the same growth here. His

experience has led him to work on key projects for

some large corporations in the implementation of

ERwin, associated products, data modeling

methodologies and frameworks. These projects

involving larger geographical footprints, ranging

from South Africa, New Zealand and the USA has

brought satisfaction to several clients in all

industries, ranging from the banking industry,

government sectors and petroleum industry.


Analytic Architecture Modernization

– A Practical Approach

Eddie Sayer, Teradata

The data and analytics landscape is evolving at an

astonishing pace. The number of ‘big data’

technology alternatives is staggering. The

momentum of open-source Hadoop is undeniable.

The marketplace offers the promise of modernized

analytic architectures to increase analytic agility,

optimize TCO and drive greater business value.

Success requires your role evolve from

conventional ‘data architect’ to ‘data ecosystem

architect’. This workshop provides a structure to

help you make the transition. We will explore

practical steps for modernizing your analytic

architecture by leveraging emerging technologies

and employing data architecture patterns for

acquisition, provisioning, integration and access.

Best practices will be discussed for architecting the

modern-day analytic ecosystem by implementing

data lakes and data products, as well as

integrating heterogeneous data components, such

as relational databases, NoSQL databases and

open-source Hadoop. You will leave the session not

only better educated on analytic architecture key

themes and concepts, but armed with a practical

approach for modernizing the analytic architecture

in your own organization.

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 & Technology and Inacom Corporation.

Eddie is a founding member of the Georgia chapter

of Data Management International (DAMA) and is

a frequent speaker at industry events.

Nike United

Ryan Smith and Suzanne Kimble, Nike

No results were guaranteed, in a recent effort to

align all business functions to a single future state

vision for product data. Yet in partnership with

new business leadership and with an appreciation

for the strategic value of information, the business

and tech teams successfully prioritized and

accomplished this. Asking “what if our product

data were as good as our products,” they persisted

to arrive at broad acceptance of a substantially

improved common logical data model to describe

Nike’s innovative and iconic product lines. The

team will speak to how this future vision was

developed, elevated as a priority business

conversation, and brought to agreement and

relevance for the enterprise. Ryan Smith is an

Information Architecture Director and Suzanne

Kimble is a Data Architect, both dedicated to

Product Line Planning and Merchandising.

Ryan Smith is an Information Architecture

Director focused on Nike’s Line Planning and


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Merchandising data. With a prior background in

Healthcare and Automotive, he now seeks to

maximize the value of enterprise modeling and

well-managed product master data, to enable the

product vision to be clearly understood and

executed throughout the company.

Suzanne Kimble is a Data Architect, also at Nike,

with prior experience at Con-way and Mentor

Graphics. She has over 20 years of IT experience,

including data architecture, user experience design

and technical training.

The Blockchain Billionaire

Steve Hoberman

Expect a game changer at least once a decade. A

technology that disrupts the status quo and ideally

improves our lives in terms of efficiencies or

general well-being. Blockchain is this decade’s

game changer. We’ve past the hype cycle and now

both startups and well-established organizations

are focusing on practical uses of a replicated

general ledger beyond Bitcoin. Learn about

blockchain and its three levels of maturity. Play a

game to understand blockchain’s benefits. Identify

the opportunities available to us within data

management. Overcome the challenges that lie

ahead to become the next blockchain billionaire!

















Achievement Award.

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 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.

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 RDMBS 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 MBA

and is also a Certified Data Management

Professional.

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 database 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.

When little meets BIG: Practical Data

Modeling for Analytics

Stacie Benton, Microsoft

More organizations are trying to get value using

Big Data but questioning the return on investment

in new technologies, processes, and skills they may

not have. Understanding and defining an

organization’s “little” data can make the difference

between finding insight or drowning in data

swamps. This session will cover what makes big


Page 7

data different, define the complementary

differences between data modeler/analyst and data

scientist, and show practical examples on how data

modeling can accelerate the value from Big Data

trends, including a case study on how data

modeling on projects can help focus efforts and

help drive important business decisions and

impact.

Stacie Benton, CDMP, is currently an Information

Architect for Microsoft’s Enterprise Services

Business. She also recently spent time as the

Enterprise Information Architect for the Bill &

Melinda Gates Foundation working on building a

data culture as well as launching a Data

Governance program. She spent a tour of duty in

the consulting world as well as many years at

Microsoft, slaying dragons, both transactional and

BI, across the enterprise as well as in the Finance,

Sales, Services, HR, and Marketing spaces. She

spent years as a program manager in Microsoft IT

and has the awards and scars to prove it. She loves

talking data and process in the same sentence as

well as realizing the full potential of data through

good data management.

Data Modeling for Mobile Data

Collection

Stephen Gonzales, Reformatix, LLC

We can easily see how today’s mobile devices and

smartphones improve our productivity in the

workplace by allowing immediate interaction with

our organizations’ information assets whether in

the office or not. Besides the basic checking of

email and calendar via mobile device, common

business desktop applications such as timekeeping,

expense reporting have gone mobile as well! You

can be assured that more business applications

will become mobile-enabled through apps or web-

based applications.

So how can the discipline of data modeling adapt

in this new world of mobile computing? This

session will provide alternative insights into the

ubiquitous entity-relationship diagram by focusing

on often-overlooked model characteristics. You will

be introduced to table categorization, comparative

strengths of relationships, and table affinity

concepts. Additionally, a set of model analysis

techniques will be introduced that leverage the

aforementioned concepts to help identify atomic

business transactions to help get into the mobile

application perspective. Finally, the topic of data

storage for mobile data collection will be covered

whether using traditional RDBMS’s or persisting

structured data in a big data environment.

You will learn:

How data modelers can participate in the

development process of mobile applications

How highly-normalized data models

provide an advantage for efficient data

collection

How to leverage often overlooked entity

and relationship characteristics to gain a

deeper understanding of the data model in

terms of natural data flow

Stephen L. Gonzales has worked in the IT field for

nearly 20 years with both large and small

businesses, government and non-profits, including

Dell, ADP/Digital Motorworks, Habitat for

Humanity RE-store, Waste Management, The State

of Texas, Oracle Retail, Advanced Micro Devices,

and General Motors.


Stephen has extensive professional experience with

software development methodologies, database

design, data analysis, data cleansing and

migration efforts. Having worked in both software

development and data warehouse environments,

Stephen has focused on designing efficient data

capture and storage solutions. He began his data

modeling career in 1996 as a staff consultant with

Information Engineering Systems Corp. (now

Visible Systems Corp.). With an active interest in

model-driven rapid application development

strategies, he founded Reformatix, LLC in 2011 to

pursue that aim.

Empowering Business Users to Lead

with Data

Denise McInerney, Intuit

The most valuable people in your organization

combine business acumen with data savviness. But

these data heroes are rare. Denise McInerney

describes how she has empowered business users

at her company to make better decisions with data

and explains how you can do the same thing in

your organization.

Businesses are only as good as the best decisions

they make. The business users who drive these

decisions are all working to make the best

decisions they can. Having access to the right data,

in the right way, can propel these business experts

in the right direction, and equip them to make

decisions that will make the business successful.

Data professionals with the technical know-how to

empower these users are true partners in any

organization’s success. Denise McInerney describes

how she has empowered business users at her

company to make better decisions with data and

explains how you can do the same thing in your

organization.

Attendees will learn how to:

Maximize their existing investments in

people and tooling

Meet their data consumers at their

starting point

Visualize their business from the

perspective of their business users

Inspire their business users to help drive

their data journey

Bridge the gap between the technologist

and business users views of the data

Create an environment of quality data that

inspires confidence in business users

Get past “tool snobbery”

Denise McInerney is a data professional with over

17 years of experience. Denise began her career as a

database administrator managing and developing

databases for online transactional systems. She

now works as a data architect at Intuit, where she

designs and implements BI and analytics solutions

with a focus on enabling the work of analysts and

business users. Denise is active in technical-

community organizations and has spoken at user

groups and conferences over two dozen times. She is

currently serving as the vice president of marketing

for PASS, an international organization of data

professionals. She is also a Microsoft Most

Valuable Professional. Denise has been a

passionate proponent of advancing the cause of

women in technology for over a decade. She has

organized events, spoken, and written on the topic.

She founded the Women in Technology chapter

of PASS and is active in the Tech Women @ Intuit

organization.

Data Modeling in the NoSQL World

Ted Hills, LexisNexis

The venerable entity-relationship (E-R) modeling

technique is very mature for SQL databases, but


Page 9

doesn't translate well to many of the NOSQL

databases. This interactive workshop will

introduce the Concept and Object Modeling

Notation (COMN, pronounced "common"), which

can represent the new structures that NOSQL

DBMSs support, while still supporting traditional

SQL DBMSs. It also has more expressive power for

modeling the real-world entities that databases are

about.

Topics to be covered include:

modeling notation for containment and

multi-valued attributes

how to model schema-less databases

how to model the problem space and the

solution space, and the mapping between

them.

Ted has been active in the Information Technology

industry since 1975, moving gradually up through

device drivers and operating systems to

communications software, applications, and finally

information architecture. Past employers include

Bank of America, AT&T Bell Laboratories, Dow

Jones, and Bloomberg. At LexisNexis, Ted co-leads

the work of establishing enterprise data

architecture standards and governance processes,

working with data models and business and data

definitions for both structured and unstructured

data. Prior to joining LexisNexis, Ted was the

Enterprise Information Architecture Executive for

Bank of America, where he led several enterprise

reference data and data warehouse projects. Ted’s

work spans the spectrum from conceptual design

through database and software implementation.

Ted has always been an active researcher, with

interests in software and data integration, data

modeling notations, and improving the

expressiveness of languages while keeping them

efficient and type-safe.

Creating Ontologies from your

Existing E/R Data Models

Nicolas Leveque, Deutsche Bank

During the last two generations, visionary people

like Codd, Chen, Kent, Blaha, Wells, Inmon,

Hoberman contributed to the development and

success of the Entity Relationship model and its

implementation by modern RDBMS vendors.

During this period, thousands of students, scholars

and professionals were taught the three data

modeling perspectives (Conceptual, Logical and

Physical), and data management has become one

of the most lucrative, job creating, and ever

changing facet of the IT industry.

The last couple of years, our industry has seen the

advent of a new modeling technique that facilitates

the integration of heterogeneous data sources by

resolving semantic heterogeneity between them:

Ontology.

This presentation will explain in detail how to

leverage all of the numerous E/R Data models

produced and turn them into an OWL ontology

model. This session will explain the links between

ontology and E/R model constructs, and will help

you with learning and creating your first ontology

from their existing E/R data models.

So, if, as with Deutsche Bank, your company has

started embracing Linked Data and ontology

modeling, come and learn how to re-use what you

did and turn your E/R Data Models into a basic

OWL ontology.


Nicolas Leveque is Head of Physical Data

Architecture, Deutsche Bank, and a Data Architect

with 20 years experience mainly within financial

institutions including UBS, Barclays, Societe

Generale, Capital Group and Deutsche Bank. He

specializes in Enterprise Data Architecture, Data

Modeling and in bridging the gap between High

Level architect and implementation teams. Still a

hands-on technician, he doesn’t believe in one-

solution-fits-all, but strives to find the right

solution depending on business and non-functional

requirements. He also looks forward and sees how

new practices and technologies could help to solve

data problems with a different and better mindset.

Advanced Aspects of UML Data

Modeling

Michael Blaha, Modelsoft Consulting

The UML class model is the UML model that is

pertinent to data modeling. It has its advantages

and disadvantages. In general, a data model

expressed in the UML notation excels at reaching

business audiences and also helps to foster deep,

abstract thinking. The primary downsides are that

the UML notation doesn’t address database design

and is less familiar to database developers.

This session will presume that the audience is

familiar with basic UML concepts. We will take a

deep dive and explore some advanced aspects. In

particular we will discuss qualified associations,

association classes, aggregation, composition, and

operations. We will explain their precise meaning,

discuss corresponding database designs, and give

compelling business examples.

Michael Blaha is a consultant, author, 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, many articles, and two video courses.

He received his doctorate from Washington

University in St. Louis and is an alumnus of GE

Global Research in Schenectady, New York. You

can find out more about him at superdataguy.com.

Bring Your Model to Life

Gertjan Vlug, Attunity

Organizations can implement their Business

Intelligence solution automatically, based on

corporate data models and business rules. No

programming or coding is needed anymore, nor is

technical modeling of optimized structures for

Data Warehouses and Data Marts Databases. This

saves time and money, and increases quality and

consistency. In general, modelers are working on

Staff-level. They define the model, business rules,

meta-data, etc. They hand it over to different IT

teams, including BI / DW teams. The BI / DW

teams start to work and, at best, use the document

from Staff as a guideline. They often lose track

with the model and start making changes without

any alignment to the model anymore.

Model-Driven Data Warehousing solves these

issues. Based on the model, the complete

implementation (Design of Data Warehouse (3NF

and/or Data Vault) and Data Marts (Star-

schemas), plus the generation and execution of

ETL-code) is automated. And when changes need

to apply, a simple “update” of the model adjusts

the Data Warehouse automatically.

Creating a logical data model gives you a

Data Warehouse in just a few clicks. With

consistent management information available,

technical complexity and challenges are no longer

delaying your business advantage. Better

alignment between the business and IT can also be

achieved through this model-driven approach.

From a Corporate perspective this will bring more


Page 11

control and consistency at the implementation of

business rules, meta-data standards and correct

definitions of KPIs. From a developer’s perspective

the backlog of work is decreased, unsatisfied and

impatient customers are no longer frustrated, and

you don’t have to redo your own work over and

over for each change. The technical, time-

consuming and error-prone activities are

automated for you.

You finally have achieved an agile Data

Warehouse: Flexible, Incremental, Fast, Cost

effective. With shorter time to market due to the

efficiency and productivity improvements, you gain

a significant competitive advantage. Data

modeling not only ensures that your business

intelligence solution will be built more quickly and

effectively, but that the underlying information

that drives them is correct and fully documented.

Usher in a new era in Data Warehouse automation

by automatically designing and generating the

Enterprise Data Warehouse code for Third Normal

Form and/or Data Vault as well as Star Schema

data marts and staged data for Business

Intelligence projects.

Gertjan Vlug is the director of Data Warehouse

Automation at Attunity where he heads the

corporate development of next generation of ETL

tools that are fundamentally changing how

analytics platforms are built. Gertjan has a long

history of driving innovations in Business

Intelligence (BI) dating back to 1980’s. His

involvement in BI industry includes executive

management roles at Cognos (now part of IBM),

Business Objects (now part of SAP) and several

European consulting agencies. As an entrepreneur

he built 3 different companies whose focus were to

solve BI challenges. His latest, BIReady, was

recently acquired by Attunity. Prior to starting

BIReady, Gertjan was the founder of

QuickIntelligence and VisionProof.

“Zen with Len”

Meditation, Qigong (moving meditation),

and Talking Meditation (The what,

why and how of meditation)

Len Silverston, Universal Data Models

Prepare yourself and your mind for the day so you

can make the most of it! Come invigorate yourself,

reduce stress, develop your mind, and learn about

and practice meditation.

Len Silverston, who is not only a data modeling

thought leader, but is also a fully ordained Zen

priest and spiritual teacher, will provide this brief

overview of what meditation is, why it is

important, how to meditate, and lead a sitting

meditation and moving meditation (Qigong)

session. This will be an enlightening, wonderful

session to start your day in a relaxed and receptive

state of mind!

Len Silverston is a best-selling author, consultant,

and a fun and top rated speaker in the field of data

modeling, data governance, as well as human

behavior in the data management industry, where

he has pioneered new approaches to effectively

tackle enterprise data management. He has helped

many of the largest organizations world-wide as

well as small organizations, to integrate their data,


systems and even their people.He is well known for

his work on “Universal Data Models”, that are

described in his The Data Model Resource Book

series (Volumes 1, 2, and 3), (Volume 1 was rated

#12 on the Computer Literacy Best Seller List) and

these books have been translated into multiple

languages. He is the winner of the DAMA (Data

Administration Management Association)

International Professional Achievement Award for

2004 and the DAMA International Community

Award for 2006. He has received the highest

speaker ratings at many international conferences

and is dedicated to being of the greatest service to

his audiences.

Enrichment Modeling:

Dimensional Modeling for

NoSQL Datastores

Jim Cannaliato, Leidos

Many have rejoiced at the arrival of "schema free"

NoSQL data stores, appearing to bring joy to

developers by relieving them of the burden of doing

data modeling. However, reports on the death of

data modeling have been greatly exaggerated.

Data modeling approaches such as dimensional

data modeling and star schemas were incredibly

useful in relational stores, and are now with

Enrichment Modeling, are even more powerful

than ever in NoSQL data stores. Learn how to go

beyond traditional dimensional modeling for

BigData stores, such as Hadoop, MongoDb or

ElasticSearch, and see firsthand examples of this

in action with DigitalEdge.

Jim Cannaliato is a Technical Fellow at Leidos,

and previously a Vice President at SAIC. He has

over 30 years of software development and

architecture experience, building complex

intelligence systems for our nation’s defense. For

the past ten years, he has been building large scale

database systems using a dimensional data

modeling approach. He received his Bachelor of

Science in Applied Science and Technology in

Computer Science from Thomas Edison State

college, and is a certified SCRUM Master.

ER/Studio SIG

Ron Huizenga, Idera

Hear the latest features available in ER/Studio

and ask the Idera team any questions you have on

ER/Studio!

Ron Huizenga is the Senior Product Manager for

the Idera ER/Studio product family. Ron has over

30 years of experience as an IT executive and

consultant in Enterprise Data Architecture,

Governance, Business Process Reengineering and

Improvement, Program/Project Management,

Software Development and Business Management.

CaseTalk - Data Modeling by Example

Marco Wobben, BCP Software

Q: Data modeling is described as a craft and once

completed the results may even seem artful. Yet

outsiders may see data modeling as abstract, time

consuming or even unnecessary. In many cases the

data modeler interviews business experts, studies

piles of requirements, talks some more, and then,

hocus pocus, presents a diagram with boxes, crows

feet, arrows, etc… Then the slow process begins to


Page 13

keep the diagrams up to date, explain what the

diagrams behold, and sometimes even data

modelers themselves may get lost while

maintaining a growing set of data models and

requirements.

A: Fact based information modeling is the very

opposite of abstract. Fact based information

modeling uses natural language which expresses

facts that are intelligible for both business and

technical people. It does not require people to

understand the modeler’s magical language of

boxes and arrows. Although models can be

presented in several diagramming notations, they

can be validated in natural language at all times.

This gives both data modelers, technically skilled

people, and business people the benefit of having a

well documented and grounded data model.

Therefore the method of Fact Oriented Modeling, is

also known as "Data Modeling by Example".

Presentation Highlights:

key elements of fact oriented modeling;

data modeling with facts;

visualizing the model;

validating and verbalizing;

transforming and generating output (E.g.:

SQL, Relational, UML, XSD,

PowerDesigner, etc.).

Marco Wobben is director of BCP Software and has

been developing software well over 30 years. He has

developed a wide range of applications from

financial expert software, software to remotely

operate bridges, automating DWH generating and

loading, and many back- and front office and web

applications. For the past 10 years, he is product

manager and lead developer of CaseTalk, the CASE

tool for fact based information modeling, which is

widely used in universities in the Netherlands and

across the globe.

FoCuSeD™ Business Data Modeling

Made Easy

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.

Practical Tools for the Human Side of

Data Modeling


Success in data modeling requires skills in

communications, gaining sponsorship and buy-in,

negotiation, understanding needs, and leadership.

Yet many data modeling classes do not focus on

these essential aspects of effective data modeling.

This interactive, informative and fun class focuses

on the human elements of data modeling. Len

Silverston, best-selling data modeling author,

leads this seminar and provides tools, techniques,

principles, interactive exercises and case studies

that illustrate the importance of the human side to

data modeling.

This seminar will address the behavioral side of

data modeling in many different contexts such as

data modeling for big data, dimensional modeling,

traditional data modeling and more.

Participants of this seminar will learn about:

Inevitable human scenarios that data

modelers are bound to face and how to

address them

Personal, political and cultural factors that

are critical to successful data modeling

Practical tools, techniques, and principles

to enable successful data modeling such as

keys in gaining and sustaining funding,

realizing a common vision, how to get to

common semantics, developing trust,

communications, involvement, leadership,

and managing conflict

Stories of how using these principles were

the key to success for some organizations

and how other organizations had huge

challenges stemming from human

behavioral issues

This class will share fundamental and practical

tools while being an informative and fun

experience – come join it!









systems and even their people. He is well known for













his audiences.


Page 15

Data Science Primer

Daniel D. Gutierrez, AMULET Analytics

Data science involves understanding and

preparing the data, defining the statistical

learning model, and following the Data Science

Process. Statistical learning models can assume

many shapes and sizes, depending on their

complexity and the application for which they are

designed. The first step is to understand what

questions you are trying to answer for your

organization. The level of detail and complexity of

your questions will increase as you become more

comfortable with the data science process.

In this session, I will cover the most important

steps in the data science process – a general

formula followed by data scientists in striving to

achieve best practices with a data science project:

understanding the goal of the project, data access,

data munging, exploratory data analysis, feature

engineering, model selection, model validation,

data visualization, communicate the results and

deploy the solution to production.

Daniel D. Gutierrez is a practicing data scientist

through his Santa Monica, Calif. consulting firm

AMULET Analytics. Daniel also serves as

Managing Editor for insideBIGDATA.com where

he keeps a pulse on this dynamic industry. He is

also an educator and teaches classes in data

science, machine learning and R for universities

and large enterprises. Daniel holds a BS degree in

mathematics and computer science from UCLA.

Business-Driven Data Architecture -

A Practical, Model-based Approach

(Hands-On)

Ron Huizenga, Idera

We are in the midst of a re-awakening, in which

the business value of data is once again being

recognized. This is occurring against a backdrop of

continuously evolving technology, an explosion of

diverse data sources, and exponential growth in

volume. To truly enable our organizations, we

require a practical, business-driven approach to

effectively understand, define and utilize data

effectively. We will discuss a practical approach to

business-driven data architecture and modeling

with several concepts including Business Data

Objects, data and business process model

structure, change management, and business

collaboration. This will be a hands-on session

utilizing examples and demos with ER/Studio

Enterprise Team Edition.

Ron Huizenga is the Senior Product Manager for

the Idera ER/Studio product family. Ron has over

30 years of experience as an IT executive and

consultant in Enterprise Data Architecture,

Governance, Business Process Reengineering and

Improvement, Program/Project Management,

Software Development and Business Management.

KEYNOTE: How To Punch A Shark In

The Face, and other stuff

Gary Hall, Jr., Olympic Gold Medalist and

Data Professional

Gary Hall, Jr. is a washed up swimmer, inducted

into the Olympic Hall of Fame. He has ten


tarnished Olympic medals. As an Advisory Group

member to the Aspen Institute’s Sports & Society

Project Play program, Gary leads an initiative that

aims to extend health insurance incentives for

youth sport enrollment. He is the Member and

Alliance Manager for T1D Exchange, an innovative

model accelerating clinical research through the

use of data. He oversees a partnership with the

American College of Sports Medicine addressing

the management of chronic conditions in sport and

physical activity. He was recently published in the

British Journal of Sports Medicine as a co-author

of the International Olympic Committee consensus

statement on Youth Athlete Development. He

serves on the Leadership Board for the National

Youth Sports Health & Safety Institute, the

Advisory Board to the University of Arizona

Department of Surgery and the International

Children’s Board of Sanford Health.

Gary is a world recognized patient advocate,

having twice testified before Congress on health

care related issues. In April, he addressed a select

group of 300 scientists, ethicists and researchers at

the Vatican conference on cellular therapy,

immediately following the Pope’s address. Also,

while spearfishing in the Florida Keys, Gary

punched the shark that attacked his sister.

Gary will talk about the Olympic experience, as

Team USA returns from Rio. He’ll share how a

right brained athlete with extensive chlorine

exposure came to appreciate the value of research

and data. And he’s agreed to tell the shark story.

Modeling Time

Petr Olmer, GoodData

I will provide an introduction to the challenges of

modeling, managing, and querying temporal data

in relational and document-oriented databases.

Current, uni-temporal and bi-temporal data will be

covered, as well as working with valid time and

transaction time, temporal relations, logic, TSQL2,

and SQL:2011. See examples of temporal features

of SQL Server, PostgreSQL, and MongoDB.

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.


Page 17

Beyond Data Models: A Metadata

Model for the Data Curator

Dave Wells, Infocentric

As the world of data management grows and

changes, the role of data curator becomes

increasingly visible and significant. A curator is a

person responsible for management and

custodianship of a collection of things. Just as a

museum curator oversees a museum’s asset

collection, or a gallery curator is responsible for a

collection of art objects, a data curator has similar

responsibility for a collection of data assets.

Curation – whether art, artifacts, or data – implies

active and ongoing management of assets through

their entire lifecycle of interest and usefulness.

Acquisition, description, preservation, location,

appropriate use, presentation, and sharing are all

curation responsibilities.

Curating is an asset management activity, and

asset management requires data about the assets

under management. For data asset management,

that data is metadata. Current metadata

approaches, however, offer more support for data

creators and consumers than for data curators. To

support the increasingly important role of data

curator we must extend current metadata models

to include information such as data origins,

interest communities, sharing constraints,

presentation guidelines and practices, preservation

considerations, and more.

In this presentation you will learn:

The what and why of curating data

The role of the data curator

The relationships of curation and governance

The kinds of metadata that are needed for data

curation

The positioning of curator metadata into a

comprehensive metadata model.

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.

Taxonomies and Ontologies

Bill Inmon, Forest Rim Technologies

Professionals have long recognized the need for

abstraction of data. And the world of unstructured

data is no different. But abstraction of data in the

unstructured world takes a different form -

taxonomies and ontologies.

This session is an introduction to taxonomies and

ontologies and how they apply to the world of

unstructured data.


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 database 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.

Bridging disparate IT systems with

an Enterprise Data Model

Brian Shive, Microsoft

Most businesses store data in multiple places.

They may have a SQL Server store for their

General Ledger, a NoSQL store for customer

relationship management, a DB2 store for

enterprise resource management, a SharePoint file

store for contracts and an Oracle store for orders,

deliveries and billing. To present all this data in a

single integrated view typically involves large

development time and costs for extracting,

transforming and loading a business intelligence

warehouse. Then the data must be transformed

into star and snowflake schemas for reporting.

This adds significant latency to the data in the

reports. This commonly used approach is

reasonable for business intelligence questions that

look across broad sections of the business and over

broad periods of time. Stale data is not a problem

when queries look across years of history.

Unfortunately this approach is not good for

business queries that must present near real-time

data across multiple data stores.

This presentation will examine a hybrid solution to

near real-time cross data store reporting that

combines data virtualization tools and graphs.

Data virtualization tools such as Composite

Software and Informatica Data Services are

industry standard solutions to near real-time

reporting. The new design element presented is the

use of graphs to stitch together multiple data

stores into a single web of linked data keys. This

new element allows for the data virtualization

tools to access data in parallel, allowing for near

real-time high performance across an almost

unlimited set of data stores.

This new design element is not easy to design and

implement. Graphs can represent unlimited

complexity and can be difficult to design,

implement and test. In this presentation we will

examine how to design a key-only graph that

unites separate data stores into a single web of

related data keys. We will look at the various

flavors of graphs such as: directed, non-directed,

cyclic and acyclic graphs and the challenges

involved in each flavor combination. We will

examine the use of graph relationships where each

relationship is “typed” allowing for dynamic

extension of the graph to new business uses with

minimum changes to the graph code. Further the

use of “typed” relationships allows for more control

over graph traversal when exploding a graph.

The design approach presented will include

enterprise data models and value chain models as

the basis for graph design. This enterprise view of

data and process is necessary to stitch together

multiple systems and data stores in a way that

meets user needs for near real-time reporting. This

design approach to near real-time reporting has

been implemented at Boeing, the Federal Aviation

Agency and Microsoft.


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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 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”.

Introduction to Dimensional Data

Models

Petr Olmer, GoodData

Description of basic techniques used in

dimensional data modeling including star schemas

and grain. Learn how to model business processes

in transaction, snapshot, and accumulating fact

tables. We will discuss these aspects of dimension

design: conformed dimensions and performance

optimization.

Introductory session–no previous dimensional

modeling knowledge required.

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.

The Importance of Fun in Data

Modeling and Success!


A key principle in success is that when we enjoy

what we do, there is much more likelihood of

success.

This session will illustrate this principle in the

context of fun during data modeling and how this

can lead to success. The presentation will show

this in many different ways. For example, you will

learn about the ‘science of fun’ and what actually

happens where there is a lot of fun, real life data

modeling stories comparing very serious data

modeling efforts with much more fun efforts and

what the outcomes were, principles of fun, and

most importantly, we will demonstrate this

principle in this class, and have a load of fun in

the presentation!

Participants of this session will gain:

Information and tools regarding the

‘science of fun’, what happens when fun is

involved, and what we can do to increase

fun and success

Principles of fun

Stories regarding data modeling efforts

that involved fun and what happened


A REALLY FUN experience in this interactive and

fun presentation!









systems and even their people. He is well known for













his audiences.

Better Data Science conversations

through Conceptual Data

Modeling

Asoka Diggs, Intel Corp.

As a data management professional, I’ve long been

impressed at how quickly and efficiently we can

articulate the scope of the data for a project using a

conceptual data model. The modeling technique

and the graphical representations used to

articulate agreed understanding of what the data

of interest is for the problem at hand, and the

relationships among that data, enable teams of

people with a range of skills and points of view to

contribute effectively and efficiently to what the

data of interest are, what things mean, and

thereby articulate the scope for a business

problem.

As a developing data scientist, I’ve been frustrated

with how chaotic discussions about business

problems can be. What specifically is the problem

we think needs solving? What are the data of

interest? What do we know, what do we think is

true, and what are we investigating? What are the

business problems and how are we framing them

as data mining problems? I know from experience

that without a well framed problem, analysis is

unable to proceed. I also believe that framing a

business problem as a data mining problem is as

much as 50% of the work involved in advanced

analytics.

Can we apply the skill of ER modeling to the data

science process, and not only enhance our

understanding of the data of interest, but even

help with framing business problems as data

mining problems? I believe that we can.

In this session we will discuss an extension to

conceptual data modeling that can be used

immediately by ER modelers to contribute early in

the advanced analytics lifecycle, to more quickly

articulate the business problem being tackled, the

data of interest that apply to the business problem,

and graphically represent the specific focus of the

analytics effort. This may also be a good reason for

the data scientists to learn a little bit about ER

modeling.

In particular, we will cover:

The advanced analytics lifecycle and where

this technique fits

Framing a business problem as a data

mining problem

Changes in the meaning of graphical

symbols in this view of conceptual data

modeling


Page 21

And we’ll work at least one generic

example to see the idea in practice

After 15+ years of experience in a variety of data

management disciplines, including database

administration, ER modeling, ETL development,

and data architecture, I am transitioning into the

world of analytics / data science. It turns out that

there IS something even more fun than ER

modeling, and I’ve found it. Today, my primary

interest is in the organizational transformation

involved in adopting analytics as a source of

competitive advantage. How does an organization

get there? What needs to be done? What

organization design and leadership changes are

needed to get the most benefit from analytics? I now

spend my work life practicing these new analytic

modeling skills, and teaching others how to

participate and contribute to predictive analytic

projects.

Caesars Enterprise Data Model

Patti Lee, Caesars Entertainment, and

Michael Blaha, Modelsoft Consulting

In 2014 Caesars constructed an enterprise data

model for the entire company. This was a major

undertaking with one person year of effort. The

model was built with the support of top IT and

business management. The authors will present an

overview of the EDM and dive deep into several

areas. We will discuss objectives, EDM inputs,

EDM deliverables, the construction process, and

governance. This session will appeal to attendees

who are interested in advanced data models and

their commercial use. It will provide guidance for

those constructing their own EDMs. Attendees will

obtain insight into the industry of hospitality and

gaming.

Patricia Lee is the Director of Data Architecture for

Caesars Entertainment and an evangelist for data

governance. Patti has a unique breadth of business

knowledge obtained from years of designing and

integrating a broad spectrum of systems, including

the award winning Total Rewards loyalty program.

Michael Blaha is a consultant, author, 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, many articles, and two video courses.

He received his doctorate from Washington

University in St. Louis and is an alumnus of GE

Global Research in Schenectady, New York. You

can find out more about him at superdataguy.com.

Scales, Measurement and Encoding

Scheme

Joe Celko

Most DBAs are fair on normalization and good on

the mechanics of their SQL product. But they do

not know anything about how to actually design


the data going into that database and its encoding!

Would you really like to use Roman Numerals

today? On a scale from 1 to 10, what color is your

favorite letter of the alphabet? Did you know that

measurement theory was invented in 1947?

First we will talk about measurement theory, then

classify types of scales. But how do we encode such

things for a database? How do we add verification

(check digits and regular expressions) and

validation?

Finally we will discuss examples of actual

encoding for the same problem. For example,

compare the Canadian, US, and UK postal codes.

There is a set of ISO Standards that every DBA

should know.

Joe Celko served 10 years on ANSI/ISO SQL

Standards Committee and contributed to the SQL-

89 and SQL-92 Standards. He is author of eight

books on SQL for Morgan-Kaufmann: SQL for

Smarties (1995, 1999, 2005, 2010), SQL Puzzles &

Answers (1997, 2006), Data & Databases (1999)

and Trees & Hierarchies in SQL (2004), SQL

Programming Style (2005) and Analytics & OLAP

in SQL (2005) and Thinking in Sets (2008). He has

written over 1200 columns in the computer trade

and academic press, mostly dealing with data and

databases.

Data Management Maturity – Why We

Need It and How It Can Propel You to

DM Leadership

Melanie Mecca, CMMI Institute

Our industry is continually building capabilities

based on its considerable accomplishments over

the past decades. Some of the (roughly) sequential

milestone markers that most organization share

include: data design, data administration, data

architecture / warehousing, data quality and

governance, MDM, and predictive analytics using

both structured and unstructured data.

So why haven’t organizations attained DM

perfection? As we know, the data layer in the vast

majority of organizations grew project by project,

typically to meet specific needs of a line of

business. Best practices were not usually shared,

useful work products languished in project

repositories, etc. – and above all, there was no

universal mandate to manage data as a critical

corporate asset.

The Data Management Maturity (DMM)SM Model’s

primary goals are to accelerate organization-wide

DM programs by: providing a sound reference

model to quickly evaluate capabilities, strengths

and gaps; accelerating business engagement;

launching a collaborative vision / strategy; and

identifying key initiatives to extend existing

capabilities while building new ones – leading to

efficiency, cost savings, creativity, and improved

data quality.

In this seminar, we’ll address:

Data Management Capabilities and

Maturity Evaluation

The DMM in action – interactive exercises

Case study examples – how organizations

accelerate their progress

How to leverage Data Management

Maturity to empower your career.

Ms. Mecca, CMMI Institute’s Director of Data

Management Products and Services, was the

managing author of the Data Management

Maturity (DMM) SM Model. Her team created a

business-centric method for assessing an

organization’s capabilities via the DMM, and she

leads Assessments for organizations in multiple

industries.


Page 23

She directed development of three successive

courses leading to the Enterprise Data

Management Expert certification, web-based

learning, and DM topic seminars. In 30+ years

solving enterprise data challenges, Ms. Mecca

advocates measuring data management

capabilities as the quickest path to empowering

data management and to achieve business value,

and is devoted to advancing our industry through

data management education.

Modern Data Governance:

Implications of Agile, Big Data, and

Cloud

Dave Wells, Infocentric

Traditional data governance practices need to

adapt to the realities of today’s data management

practices. It is as simple as ABC — Agile, Big data,

and Cloud. Each brings new challenges for

governance. Do agile and governance conflict or

can they coexist? The answer, of course, is that

they must coexist. The challenge is in adapting

both agile and governance processes to be

compatible. Big Data certainly brings new

governance challenges: governing data from

external and open sources, questions of data

quality and traceability, data ethics, and more.

Cloud implementations obviously amplify

governance concerns for data privacy and security.

Beyond the obvious, cloud also brings new

governance questions related to services

provisioning, service level agreements, quality

management, integration, availability, and much

more.

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.

Hands-On PowerDesigner

Jeff Giles, Sandhill

After a quick overview to PowerDesigner, we will

exploit the more powerful features in the tool,

including model generation. In this half-day

workshop you will learn how to maximize your

investment in PowerDesigner by leveraging this

often-misunderstood capability. Build information

architectures, enterprise data models, and many

more data connections.

What you will learn:

What is Link n’ Synch

Top Down Generations

Bottom Up Generations

Establishing the correct level of model

abstraction

Understand the ERD to UML connection

Linking Process to Data


Lineage and Impact Analysis

Jeff Giles is Principal Architect at Sandhill

Consultants. Prior to that he has worked for Sybase

Inc and SAP. He has over 15 years experience in

Information Technology and is recognized as a

Certified PowerDesigner Professional. Jeffrey has

been a guest lecturer on modeling Enterprise

Architecture at the Boston University School of

Management. He has been involved in modeling

various perspectives of process, data, systems, and

technology.

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.

















Achievement Award

Even non-relational databases have

relationships

Pascal Desmarets, Hackolade

The JSON-based dynamic-schema nature (aka

schemaless nature) of NoSQL document data

stores is a fantastic opportunity for application

developers: flexibility, fast and easy evolution,

ability to start storing and accessing data with

minimal effort and setup.

With increased scale and complexity of the data, is

it still good enough to find data structures

described tacitly in the application code? With a

bigger team -- including analysts, architects,

designers, developers, and DBAs – don’t the

different stakeholders want to engage in a fruitful

dialog about the evolution of the application and

the data?

A database model (or map) helps evaluate design

options beforehand, think through the implications

of different alternatives, and recognize potential

hurdles before committing sizable amounts of

development effort. A database model helps plan

ahead, in order to minimize later rework. In the

end, the modeling process accelerates development,

increases quality of the application, and reduces

execution risks.


Page 25

But the traditional methods and tools of the old

‘relational’ world are no longer valid for NoSQL. As

a matter of fact, this argument has sometimes

been used as a justification against the adoption

and implementation of NoSQL solutions.

In this session, after a review of the different

modeling alternatives, you will learn how to

reverse-engineer an existing instance so you can

enrich the documentation and constraints. You

will also learn how to develop a model from scratch

to produce a Mongoose schema or MongoDB 3.2

validator script.

Pascal Desmarets has been designing applications

and databases for a couple of decades, but has

embraced MongoDB and its dynamic schema

approach. In the course of designing a social

network built on MongoDB, he realized that he was

missing some of the tools previously at his disposal

in the old relational world. After a thorough

inventory of the different solutions available on the

market, he embarked on the design and

development of a new application targeted at

analysts, solution designers, architects, developers,

and database administrators. Users can visually

design, model, define, and create documentation for

Mongo databases. More information is available at

http://hackolade.com.

Conceptual vs. Logical vs. Physical

Data Modeling - a Contrarian View

Gordon Everest, University of Minnesota

Here we challenge the traditional view of these

being levels of data models. First, by looking at the

dictionary meaning of these three terms. Then

presenting an alternative view. We conclude that

a conceptual model, as commonly viewed, is not a

different type of model, but rather an abstracted

view of a more detailed underlying model. Thus, it

is a matter of presentation not of modeling. We

also conclude that all data models are logical

models since they are developed according to some

logical data modeling scheme. There is also

confusion about what elements of a data model are

moving into physical storage and

implementation. If these three terms do not help

us distinguish data models, what do we call a data

model which includes all of the semantic detail of

the user domain being modeled, but without

including any constructs or elements which are

really in the physical implementation realm. Is a

relational data model logical or physical?

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

http://hackolade.com/


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.

Machine Learning Primer

Daniel D. Gutierrez, AMULET Analytics

Machine learning can be thought of as a set of tools

and methods that attempt to infer patterns and

extract insight from enterprise data assets. The

subject of machine learning is one that has

matured considerably over the past several years.

Machine learning has grown to be the facilitator of

the field of data science, which is, in turn, the

facilitator of big data. In this session, I will provide

a high-level overview of the field by examining the

two primary types of statistical learning:

supervised learning and unsupervised learning.

Supervised learning is the most common type,

often associated with predictive analytics. We’ll

discuss two classes of supervised algorithms to

make predictions: regression and classification.

Next, we’ll discuss the most common type of

unsupervised algorithm: clustering to discover

previously unknown patterns within the data.

Daniel D. Gutierrez is a practicing data scientist

through his Santa Monica, Calif. consulting firm

AMULET Analytics. Daniel also serves as

Managing Editor for insideBIGDATA.com where

he keeps a pulse on this dynamic industry. He is

also an educator and teaches classes in data

science, machine learning and R for universities

and large enterprises. Daniel holds a BS degree in

mathematics and computer science from UCLA.

Essential Data Modeling

David C. Hay, Essential Strategies, Inc.

The history of the Information Technology

Industry has been marked by the effort to

apply new technologies to the world they are

to serve. The technologies have introduced

new structures to the way problems are

solved, but it is not always easy to connect

those structures to that world.

Among the problems is that of making sure

the “real-world” problem is properly

understood in the first place—in terms that

could be addressed.

The attempt to understand the “essence” of a

domain being addressed in fact pre-dates the

IT industry—by about 2500 years. (Ontology

was the branch of Greek philosophy concerned

with identifying what exists.) This

presentation will show how the effort has been

undertaken through the last five decades.

This includes a timeline of the essential

aspects of each technology (relational data

bases, essential data flow diagrams, the

semantic web, etc.), plus an example of an

“essential” enterprise data model.

A veteran of the Information Industry since the

days of punched cards, paper tape, and teletype


Page 27

machines, Dave Hay has been producing data

models to support strategic information planning

and requirements planning for over thirty years.

He has worked in a variety of industries, including,

among others, power generation, clinical

pharmaceutical research, oil refining, banking, and

broadcast. He is President of Essential Strategies

International, 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 book, Data Model

Patterns: Conventions of Thought, published by

Dorset House, Requirements Analysis: From

Business Views to Architecture, from Prentice Hall,

and Data Model Patterns: A Metadata Map, from

Morgan Kaufmann.

Most recently, he wrote Enterprise Model Patterns:

Describing the World, and UML and Data

Modeling: A Reconciliation, both from Technics

Publications.

He has spoken numerous times to annual DAMA

International Conferences (both in the United

States and Europe), annual conferences for various

Oracle user groups conferences, and numerous local

chapters of both data administration and database

management system groups.

He may be reached at [email protected]

or (713) 464-8316. Many of his works can be found

at http://articles.essentialstrategies.com.

How to compose a fact-based model

into any kind of schema

Clifford Heath, Infinuendo

Fact-based modeling is not just a powerful

business communication tool, it is the one true ring

of power for the data modeler. By working with a

fully decomposed schema in “elementary form”, we

avoid making a commitment to any particular kind

of composite schema. This presentation will show

how different composition rules applied to a fact-

based schema will generate an entity-relational,

star, snowflake, hierarchical, object-oriented, XML

or graph schema.

Clifford Heath is a computer research scientist who

has long experience in the design and

implementation of enterprise-scale software

products, and in the use of fact-based modeling. He

is a Certified Data Management Professional

(CDMP) at Masters level, has published a number

of papers in peer-reviewed scientific journals, holds

several patents, is the creator of the Constellation

Query Language and is a participant in the Fact

Based Modeling Working Group. Clifford has

frequently presented at chapter meetings of the

Data Management Association as well as at the

NATO CAX Forum and the European Space

Agency, and is CTO and founder at Infinuendo.

Business Information Modeling using

the fact-based approach

Clifford Heath, Infinuendo

Most modeling languages are effective for

communicating only with people who have been

trained to read and interpret the details. However,

fact-based languages are deeply rooted in natural

speech. They build a complete vocabulary for the

business – not just a glossary of terms – and can be

used directly with untrained business personnel.

This radically improves the depth and breadth of

the business dialogue.

As each new fact is encountered, the modeler adds

appropriate elements to an Object Role Model

diagram, which makes it easy to verbalize to

natural language. Alternatively, the new fact can

be expressed directly in the structured natural

language form of the Constellation Query

mailto:[email protected]


Language, which is immediately readable to

business users but can also be understood by the

computer. This makes it possible to describe any

factual situation precisely, to ask or answer any

factual question, and to elaborate detailed business

rules about possible or allowable situations.

This presentation will introduce you to the

approach and to these two languages, peeking at

the respective software tools which generate

physical schemas. A group exercise will challenge

you to apply it to model a sample problem. The

attendee will learn to use verbalization and fact-

based analysis to understand any subject material,

and to communicate it at a deep conceptual level

with those less expert in the field.

Clifford Heath is a computer research scientist who

has long experience in the design and

implementation of enterprise-scale software

products, and in the use of fact-based modeling. He

is a Certified Data Management Professional

(CDMP) at Masters level, has published a number

of papers in peer-reviewed scientific journals, holds

several patents, is the creator of the Constellation

Query Language and is a participant in the Fact

Based Modeling Working Group. Clifford has

frequently presented at chapter meetings of the

Data Management Association as well as at the

NATO CAX Forum and the European Space

Agency, and is CTO and founder at Infinuendo.

Agile for Data Professionals

Larry Burns, PACCAR

In recent years, there’s been intense debate about

how (or whether) the principles of Agile

development can/should be applied to data

management work (including data modeling and

database development). Now the Agile debate has

shifted to BI development, raising questions of

whether an incremental approach can be applied to

enterprise-wide data work.

Larry Burns, author of Building the Agile

Database, has been in the vanguard of Agile Data

for over a decade. In his current role as Data and

BI Architect for a global Fortune 500 company, he

is also applying Agile principles to the

development of his company’s BI architecture. In

this workshop, Larry will be providing answers to

the questions that all Data and BI professionals

have about Agile, including:

What is Agile (hint: you might think you know,

but you’re probably wrong), and why is it

important?

What are the implications of Agile to IT

projects, including Data and BI development?

What is the relationship of Agile to Enterprise

Architecture (e.g., the Zachman Framework)?

What is the impact of Agile on architecture and

design? On data quality?

How can Agile principles be applied to Data

and BI initiatives?

How can data modeling and database design be

done in an Agile manner?

How can patterns be used in an Agile approach

to Data and BI development?

How can data virtualization help achieve data

agility?

What is the importance of an “Agile Attitude”?

Larry Burns is the domain architect for Data and

BI at a global Fortune 500 company, where he is

involved in enterprise-level data modeling and BI

development. He also teaches data management

and database development to the company’s

application developers. He was a contributor to

DAMA International’s Data Management Body of

Knowledge (DAMA-DMBOK), and a featured

columnist for TDAN.com. He was formerly an


Page 29

instructor and advisor in the certificate program

for Data Resource Management at the University of

Washington in Seattle. He is the author of Building

the Agile Database, published by Technics

Publications.

Introduction to Linked Data and the

Semantic Web

Cody Burleson, Base22

In his 2009 TED talk, Tim Berners-Lee (a.k.a. The

Father of the Web) urged us onward toward a

compelling vision for the next Web - a "Semantic

Web".

"It's called Linked Data," he said. "I want you to

make it. I want you to demand it."

But what is Linked Data? Why is it valuable? How

does it work? And how can you use it to advance

your organization with the work you do?

Briefly described, Linked Data, is a method of

modeling and publishing structured data so that it

can be interlinked and become more useful

through semantic queries. Linked Data proposes to

turn the existing Web of hyperlinked documents,

which are mostly made for people to read, into a

Web of linked data that better enables computers

and people to work in cooperation. It simplifies the

publishing and integration of data and it can solve

a host of classic I.T. problems within your

organization and on a global scale. Linked Data

can change the way you think about how to model,

create, share, and integrate data.

In this gentle, step-wise, and comprehensive

presentation, you'll learn essential concepts - the

most important things you need to know. Those

new to this topic will walk away with a fresh new

perspective and a whole new toolbox for solving

problems. Those familiar to the topic are sure to

glean new insights, best-practices, and lessons-

learned. In addition to learning about the "what,

why and how", you will learn about:

Essential W3C standards such as the

Resource Description Framework (RDF)

The SPARQL Query Language (SQL for

the Semantic Web)

OWL - The Web Ontology Language and

Ontology Modeling

“Triple-store” Graph Databases

Pre-existing (reusable) Vocabularies

Linked Open Data sources that you can

find and leverage on the Web

Free, Open Source, and Commercial Tools

Available for Use.

Cody Burleson is a founder, Enterprise Web

Architect, and Director of Product Innovations at

Base22, a consulting and systems integration firm

that builds enterprise-class web solutions for some

of the world’s most recognized brands. Their motto

is “enterprise web evolution”. Cody has been an

avid practitioner and advocate for Linked Data

and semantic computing for over sixteen years -

contributing several important works to the field.

He is a member of the World Wide Web Consortium

and an active participant in the W3C's Linked

Data Platform Working Group. When not chasing

his xenophobic dog through miles of cactus covered

Texas country, he designs web solutions that

leverage Linked Data and Semantic Web

technologies for better outcomes.

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.

Attendees will learn:

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.

WhereScape – Data Warehouse

Automation

Douglas Barrett, WhereScape

This session will discuss what data warehouse

automation is and show how it can be applied

using WhereScape tools to automate data

warehouse design, development and operations.

Data Warehouse Automation is both an alternative

to and in support of self service BI tools.

WhereScape 3D is a data warehouse design

automation tool. 3D stands for data driven design.

WhereScape 3D be used to discover source systems

and then derive target models such as star

schemas and data vaults. Once a target design

looks good it can then be exported to WhereScape

RED.

WhereScape RED is a data warehouse

development automation tool. RED is used to

accelerate project delivery and drive agile

development of your data warehouse. Building /

maintaining the data warehouse is often the

lengthy, fraught part of BI projects. WhereScape

RED is an IDE for building and managing data

warehouses, marts, stores and vaults. Together

with 3D, RED provides data warehouse

automation from design to delivery.

This session will discuss how WhereScape tools

can be used to drive data warehouse projects using

an agile approach to get the best outcome - using

collaboration with the domain experts (eg BAs) and

iterations of a working / populated subject area.

Douglas has worked for WhereScape in the US for

the last 4 years, and globally for 12. He has worked

in data warehousing on SQLServer since SQL 7.

Douglas has given talks t SQL Saturdays, TDWI

events and Tech Eds.

WhereScape is the leading provider of automation

software for planning, building and extending

Microsoft SQL Server data marts, data warehouses

and analytic systems. More than 400 SQL Server

customers use WhereScape to accelerate and

automate development while delivering a fully

documented SQL Server solution. To learn more

about how hundreds of SQL Server customers have

developed solutions with WhereScape in weeks

instead of months, click here.

http://www.wherescape.com/


Page 31

erwin Modeling SIG

Danny Sandwell, erwin

Come and meet the erwin team and join a

discussion on all things erwin. In April 2016 erwin

Modeling became the standalone entity, erwin

Inc.. It has been an been an exciting 6 months

establishing this new entity, defining our vision

and strategy, expanding our solution set in the

first major step of realizing that vision, all the

while continuing to deliver the market leading

data modeling solution for our customers. Hear

about:

erwin Inc Vision and Strategy

erwin Modeling latest releases

erwin Inc expansion into the Enterprise

Architecture space with acquisition of

Corso Agile EA

NEW erwin Cloud Core Bundle

Plans for the Future

Additionally we are excited to facilitate an open

conversation, answers all your questions and

provide meeting place for you to meet and connect

with your fellow data management professionals.

With more than 25 years’ experience in the IT

industry, Danny has been with erwin for over 16

years. His industry experience includes various

roles in data administration, database design,

business intelligence, metadata management and

application development. Danny’s roles with erwin

include technical presales consulting, business

development, product management and business

strategy. Each role has given him a deep insight

into a broad range of issues facing organizations as

they plan, develop and manage their data

architecture and strategic information delivery

infrastructure. Danny is responsible for the

strategy, messaging, and strategic alliances for

erwin Modeling.

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