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Meeting Details - 2015



November 11, 2015

Title: Why do Enterprise Data Warehouses take so long, cost so much and too often cannot answer my questions?


An Enterprise Data Warehouse (EDW) is a critical factor in getting a leg up on the competition. Organizations invest millions of dollars and thousands of person hours designing, implementing and maintaining their EDW. Far too often, those responsible for the EDW are disappointed that the EDW does not provide a competitive advantage and costs to maintain it keep climbing.

This presentation will explore the reasons why so many EDW implementations don't live up to expectations. We will also discuss some ways to have a high probability of success in implementing an EDW at a low initial cost and a low total cost of ownership (TCO) through Data Warehouse Automation (DWA) technology. We will discuss

· How DWA significantly reduces time and cost while at the same time increasing quality and user satisfaction.

· Identify the industry leaders

· Discuss how DWA by itself does not necessarily guarantee success

· Discuss how Enterprise Database Design Patterns coupled with DWA help ensure success

We will close with a demo of how DWA technology works by building a data mart for Sales with slowly changing dimensions and incremental loading, generating the data warehouse relational tables as well as cubes, populating the data warehouse and cubes and running some reports.


Joe Oates, Author/Consultant

Joe Oates, is an internationally known speaker, author, thought-leader and consultant on data warehousing, database design and object-oriented (OO) development. He has more than 30 years of experience in the successful management and technical development of business, real-time, OO and data warehouse applications for industry and government clients. His successful data warehouses include the following industries: banking, health care, credit card, telephone, distribution and life insurance.

Tobias Eld, Vice President, TimeXtender
oversees the delivery of the company's data warehouse solutions for customers throughout the United States and Canada. He manages all efforts to assist partners and clients, and helps customers achieve a well-structured data warehouse, impactful analytics and accurate reporting.


August 12, 2015

Title: Data Management at the Crossroad of Governance and Quality – Metadata Intelligence


The emergence of the role of the Chief Data Officer has resulted in greater visibility of Data Governance issues that have confronted data management professionals for years. Enterprises are now coming to grips with the increasing importance of the reliability of data that is provided to internal and external consumers. The creation of a data quality industry segment can be seen as a response to these issues.

Don will be speaking about the Data Management ecosystem that talks about traceability of the business glossary to databases and from operational data collection to data distribution systems.


Don Soulsby, VP Architecture Strategies, Sandhill Consultants

Mr. Soulsby is Sandhill Consultants Vice President Architecture Strategies. His practice areas include strategic and technical architectures for data management, metadata management, and business intelligence. Mr. Soulsby has held senior professional services and product management positions with large multi-national corporations and software development organizations.

He has an extensive background in enterprise architecture and data modeling methodologies. He has over 30 years of experience in the development of operational and decision support applications.

He is completing his qualification as an Enterprise Data Management Expert (EDME) with the CMMI Institute. Mr. Soulsby is an excellent communicator and has taught metadata, data modeling and data warehouse courses through public offerings and onsite engagements to corporate clients.

Mr. Soulsby is a recognized thought leader who speaks regularly at international industry events, MIT CDO Conferences and DAMA functions.


June 10, 2015

Title: Data Requirements Modeling


Continuing from last month’s webinar: It will be a quick review of what was presented last month, then a short presentation on ramifications to DRM related to the Zachman Framework, Three-Tier Architecture, data reverse engineering, OOD (Object Oriented Design), followed by an interactive demo of going through the steps of SDM, CDM, DRM, LDM, and DR-LDM.  The plan is to use ER/Studio DA as the modeling tool, as was requested in the last meeting.

Yet, the data modeling of data requirements is not done independently nowadays, if ever, of other types of data modeling. Trying to include it in conceptual or logical modeling forces the combination of designing a solution at the same time or even before the problem space has been fully defined. The systematic reconciliation the data design with the data requirements, element by element, is a manual exercise that is most often not done and will cause the physical data design to go through cycles of readjustments.

Why aren't we building data models that graphically represent and specifically address data requirements? Why can't we automatically reconcile a data model representing the results of the analysis of data requirements with the LDM that represents the proposed logical data design solution? Because the data modeling tools we have now do not facilitate these activities.

François will share the evolution of his thinking on DRM, what he realized and what he proposes. He will share how he sees it being planned and done, and by whom, what changes the modeling tools must incorporate to allow the tool users to benefit fully of the DRM activities, the characteristics he sees in the central model object of DRM: the Logical View, and how different this model object from the Physical View.

François will spend some time in showing how, using specific workarounds, one may be able to create data models that look like DRMs in two different modeling tools.

Presenter:  Francois Cartier, Senior Consultant, E-Modelers

François Cartier has more than forty years of diversified experience in Information Technology in a wide variety of commercial sectors, including telecommunications, transportation, manufacturing, wholesale, government agencies, insurance, and financial institutions. He has designed systems marrying relational with object oriented technologies, built and contributed to corporate data models, designed operational and decision support databases under a variety of DBMS’s.

He has managed data analysis, system development, application support and IT change control teams. He has been using various modeling tools in the last 25 years. He has given classes at Golden Gate University, and made technical and management level presentations at various forums in the USA and Japan.

He is a DAMA SF chapter member since 1985, a past president and the treasurer for the last 12 years. He has been working for e-Modelers since 2002 on various consulting and teaching assignments with clients.

March 11, 2015

Title: Implementing a Data-Centric Strategy & Roadmap – Focus on what Really Matters


Data is the lifeblood of just about every organization and functional area today. As businesses struggle to come to grips with the data tsunami, it is even more critical to focus on data as an asset that directly supports business imperatives as other organizational assets do. Organizations across most industries attempt to address data opportunities (e.g. Big Data) and data challenges (e.g. data quality) to enhance business unit performance. Unfortunately however, the results of these efforts frequently fall far below expectations due to haphazard approaches. Overall, poor organizational data management (OM) capabilities are the root cause of many of these failures. This workshop will cover three lessons as illustrated in examples, which will help you to establish realistic OM plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers.

Among others, you'll walk away with three takeaways:
1. That organizational thinking must change: Value-added data management practices must be considered and included as a vital part of your business 's strategy.

2. Walk before you run with data focused initiatives: Understand and implement necessary data management prerequisites as a foundation, then build upon that foundation.

3. That there are no silver bullets: Tools alone are not the answer. Specifying business requirements, business practices and data governance are almost always more important.



Peter Aiken, Founding Director, Data Blueprint

Peter Aiken, Ph.D., is widely acclaimed as one of the top ten data management authorities worldwide. As a practicing data consultant, author and researcher, he has been actively performing in and studying data management for more than 30 years. Throughout his career, he has held leadership positions and consulted with more than 50 organizations in 20 countries across numerous industries, including defense, banking, healthcare, telecommunications and manufacturing.

He is a highly sought-after keynote speaker and author of multiple publications, including his latest book “Monetizing Data Management”.

Peter is the Founding Director of Data Blueprint, a data management consulting firm that puts organizations on the right path to leverage data for competitive advantage and operational efficiency.

He is also past President of the International Data Management Association (DAMA-I)

Lewis Broome, CEO, Data Blueprint
An innovative and practiced thought-leader in data management, Lewis Broome has more than 20 years of experience successfully designing, managing, implementing and leading global data management and information technology solutions. His successful track record is marked by strong leadership coupled with a passion for driving data and technology solutions from a clear business-value proposition.

As an executive in the global financial industry, Lewis led the development of globally integrated data solutions for two of the largest banks in the world. He designed and delivered data solutions (conceptual, logical and physical) and was able to drive standards and deliver timely, cost-effective solutions that were aligned to business objectives.

In his current role as CEO, Lewis, in partnership with Peter Aiken, Ph.D., has developed a tier-1 consulting organization that effectively combines data management, management consulting and technology into a unique professional services offering.

February 10, 2015

Title: Hadoop Data Lake Controversy: Can You Have Your Lake And Use It Too?


Hadoop provides an ideal platform for storing many types of data that business users - data engineers, data scientists, data analysts, and business analysts - can leverage for data science and analytics. But Hadoop is a file system that lacks the automation to catalog what data it contains, and has no native way for users to find and understand the data they need for their data science and analytics projects. The lack of automation is overlooked when a team conducts a pilot since the data set is known; however, it becomes debilitating as projects grow beyond a proof point or two. The end result is data anarchy where the business has to scavenge for data and hoard what it can find, while IT is desperately trying to manage the data to meet the needs of the business.

Using data in Hadoop is like scavenging at a flea market.  It is impossible to know upfront what data is there and it would take too much time to browse through the entire market. In the case of Hadoop, it is not practical to browse through all the files in the cluster to find the right ones to wrangle or visualize.

The opposite of shopping at a flea market is Amazon.com. From a user perspective, it is easy to search and find the right product very quickly. A user doesn’t need to write code or browse through endless list of items. Amazon.com provides a catalog of products with detailed information that anyone can use.

Waterline Data solves the challenges of finding, understanding, and governing data in Hadoop. Waterline Data is like Amazon.com for Hadoop data. Waterline helps anyone find and understand data in Hadoop without writing code or wasting time browsing through unintelligible files.  In addition to providing the self-service experience to find and understand the right data, Waterline Data also automates building and maintaining a data inventory, securely provisions data to users, and enables data governance throughout.


Presenter:  Alex Gorelik, Founder and CEO, Waterline Data

Alex created Waterline Data to accelerate the adoption of Big Data and data driven decision-making at enterprises.

Prior to Waterline Data, Alex served as general manager of Informatica’s Data Quality Business Unit, driving marketing, product management and R&D.  Also for Informatica, Alex managed a team of 400 engineers and product managers as SVP of R&D for Core Technology, developing Informatica’s platform and data integration technology.  

Alex joined Informatica from IBM, where he was an IBM Distinguished Engineer for the Information Integration team. IBM acquired Alex's second startup, Exeros that specialized in enterprise data discovery.

Previously, Alex was co-founder, CTO and VP of Engineering at Acta Technology (acquired by Business Objects and now marketed as SAP Business Objects Data Services).

Prior to founding Acta, Alex managed development of Replication Server at Sybase and worked on Sybase’s strategy for enterprise application integration (EAI). Earlier, he developed the database kernel for Amdahl’s Design Automation group.

Alex holds a B.S. in Computer Science from Columbia University School of Engineering and a M.S. in Computer Science from Stanford University.




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