Conference Sessions - June 24, 2014

Tuesday
June 24
7:30–8:30
Registration and Continental Breakfast
7:40 - 8:30 

Tuesday
June 24
7:40–8:30

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Understanding Data Governance Best Practices
Data Governance Professionals Organization Meeting


One of the many questions the Data Governance Professionals Organization (DGPO) receives from members is ‘What are some of the data governance best practices?’.  Based on this the DGPO has developed some industry DG best practices. Join us on June 24th to meet and network with DGPO members and conference attendees and learn about some of the top DG Best Practices from seasoned practitioners.  We will also discuss the benefits of membership, progress to date, and future plans for the DGPO.   Since the founding of the DGPO in 2011 the group has grown to over 3500 list members, 700 members representing over 185 companies and 15 countries.
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Tuesday
June 24
8:40–8:50

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Welcome
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Tuesday
June 24
8:50–9:40

 

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KEYNOTE: The Chief Data Officer’s Quest for Data Quality and Data Governance
Mario Faria, Head, Chief Data Officer, Inc.

Over the past few years, Chief Data Officers, a new breed of executives, have started to become a reality in corporations. All of them share the same mantra: treat data as a strategic asset. Without quality, there is no reliability and trust in data. Without a clear set of rules, responsibilities and processes, no one is accountable for success or for failures. This presentation will present lessons learned, real cases and experiences on how a CDO should put these initiatives in motion to make a difference for the business.

Level of Audience
All Levels

Speaker:
Mario Faria Mario Faria
Head
Chief Data Officer, Inc.


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Tuesday
June 24
9:40–10:10

 

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KEYNOTE: Metrics: You Are What You Measure!
Aftab Haque, Vice President, Global Data Management, SAP America, Inc

Learn about SAP’s journey to a metrics-driven data management organization. The speaker will share his personal experience, lessons learned, and best practices on how the Global Data Management organization at SAP deployed a metrics framework to improve organizational effectiveness and data quality.

Level of Audience
All Levels


Speaker:
Aftab Haque Aftab Haque
Vice President, Global Data Management
SAP America, Inc

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10:10 - 10:40 Coffee Break
10:40 - 11:30 CONCURRENT SESSIONS

Tuesday
June 24
10:40–11:30

 

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A Day in the Life - Types and Activities of Data Stewards
April Reeve, Advisory Consultant, EMC Consulting

What does the life of a Data Steward really look like? Some Data Steward activities and assignments have a beginning and an end. Other assignments are on-going. This presentation will show details and real life examples of the activities and deliverables of the various types of Data Stewards.


Different Types of Data Stewards

  • Enterprise Data Steward
  • Domain Data Steward
  • Application Data Steward
  • Project Data Steward
  • Operational Data Steward
  • Technical Data Steward

Skills
Activities and Deliverables
Tools
Training

Level of Audience
Introductory

Speaker:
April Reeve April Reeve
Advisory Consultant
EMC Consulting

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Tuesday
June 24
10:40–11:30

 

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DG as An Operations Management Process: Workstreams, Jobs and Scoping
Gregory Lampshire, Partner, K2 Solutions


Both business and IT already view many of their activities as operations management processes--back-office, middle-office or front-office. Data Governance should be viewed as an operational management process that focuses on data. Like any other operational process, there are workstreams and jobs to perform. Each workstream and job requires scoping to ensure that the work can be finished given your people, process and technology constraints. This presentation describes the core workstreams and jobs as well as the management scoping levers that can be set. This point of view will help you more easily describe what your data governance activities to others, help you choose what to work on when, give you guidance on how to achieve small or large successes and it will help you set expectations for people's behaviors and skills. This material is intended for managers who need to get the "jobs" done regardless of who owns the workstreams, owns the jobs or how they are resourced.

Topics include:

  • What are the few core workstreams and several jobs that need to be performed?
  • Ramp up and Ongoing Operation (Ramp & Run): What are the characteristics of these phases to launch and sustain DG? What do these look like?
  • Scoping the Ramp Up Phase: What are some common experience and best practice to getting your ramp sized and angled correctly?
  • Scoping for Sustained Value: What must we think about from the start to be positioned correctly for permanence?
  • Your Operational Management Process: Key thoughts on commonly encountered scenarios.

Level of Audience
Intermediate

Speaker:
Gregory Lampshire Gregory Lampshire
Partner
K2 Solutions

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Tuesday
June 24
10:40–11:30

 

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Data Analytics Improves Data Quality: or Does It?
Katherine Chalfant, Operations and Outreach Team Leader, US Environmental Protection Agency
John Warren, Senior Statistician, US Environmental Protection Agency
Most successful institutions have a formalized Quality System that describes the collection, production, and use of the data used to make decisions. The Environmental Protection Agency (EPA) has a mature Quality System that describes environmental data operations. This System relies on information contained in Quality Management Plans, Quality System Assessments, and Quality Assurance Annual Report and Workplans.

Annually 45 organizations within EPA that collect, produce, or use environmental data report the status of their quality-related activities to EPA's, Office of Environmental Information's Quality Staff. The Quality Staff in turn summarizes the information contained in these reports in an effort to describe the overall "health" of the Agency's quality-related activities and see the path forward for the upcoming year.

This paper describes the lessons learned by the Quality Staff to analyze self-reported data of the EPA's quality-related activities and make improvements for future reporting.

Focal points include sharing:

  • Past experiences to analyze self-reported data and the difficulty in creating a coherent picture.
  • Understanding of intent of reporting and the lack of consistency among reported data…Is there something sinister going on?
  • Interpretation of responses…Does the respondent truly care about what is being reported?
  • Problems associated with differing levels of granularity in reporting…Apples versus oranges?

Level of Audience
Intermediate

Speakers:
Katherine Chalfant Katherine Chalfant
Operations and Outreach
Team Leader
US Environmental Protection Agency

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  John Warren John Warren
Senior Statistician
US Environmental Protection Agency

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Tuesday
June 24
10:40–11:30

 

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Building the Corporate Vocabulary
Barbara Nichols, President, Metaview360, LLC

The Business Glossary is supposed to be the definitive source of the company data definitions from a business perspective.  It sounds relatively straightforward and there are Business Glossary tools that have made their appearance as new products in the last couple of years.  However many companies are finding it to be an unwieldy process with more questions than answers.

In a company where dozens of "data dictionaries" exist, developed by different parts of the organization, usually without collaboration, how and who should begin to bring all the variations together to develop the Holy Grail - that "single authoritative version of the truth"?  What should the relationship be among Business Terms, Data Models, Database Tables and Columns?  Who in the organization needs to do the work?  What are the first and continuing logical steps that will lead to a valid and useful Corporate Vocabulary?

This session will propose the key elements for successful creation of the Corporate Business Glossary, highlighted by the experiences at a world-wide financial institution and a US retail giant.

Level of Audience
Intermediate

Speaker:
Barbara Nichols Barbara Nichols
President
Metaview360, LLC

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Tuesday
June 24
10:40–11:30

 

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Setting Up Data Governance in a Non-Data-Centric Environment
Curt McAdams, Manager of Data Modeling, CareSource
Designing and implementing a Data Governance program is difficult enough; adding to that a low level of data maturity and high level of politics can make it even more challenging. Working through the known political culture and identifying key partners in data governance allow successful implementation of a data governance program.
In this session, Curt McAdams will discuss how CareSource's Data Governance program was implemented by embracing the political and cultural landscape of the company instead of competing against it.

In this session, Curt will discuss:

  • How and why Data Governance partners were identified
  • Key business areas that were enlisted to help with the Data Governance rollout
  • Inviting everyone to be a Data Deputy
  • How existing processes were used to "secretly" include Data Governance
  • How the culture of the company now includes Data Governance on a regular basis

Level of Audience
Introductory

Speaker:
Curt McAdams Curt McAdams
Manager of Data Modeling
CareSource

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Tuesday
June 24
10:40–11:30

 

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A Fortune 100 Financial Services Organization Achieves Data Quality
Jeff Brown, Product Manager, Infogix, Inc.

During this session, we will present the data challenges of a Fortune 100 Financial Services organization focused on delivering Retirement Services with $500B in assets under management. The organization struggled with poor quality data propagating through their systems within their Real Estate arm.  Participants will learn how this organization effectively ensured data quality consistency throughout their systems and established real-time visibility into their operational processes for continued data improvement

Level of Audience
Intermediate

Speaker:
Jeff Brown Jeff Brown
Product Manager
Infogix, Inc.

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11:30 - 11:45 Room Change
arrow11:45 - 12:15 DG AND IQ SOLUTIONS

Tuesday
June 24
11:45–12:15

 

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Centralized Approach to Governance & Master Data Management
Robert Rich, Global Program Director MDM, Teradata Corporation

In this session Robert Rich will discuss how to establish a governance framework for master data that is tightly integrated with the Enterprise Data Warehouse.  Teradata Master Data Manager supports a centralized approach to govern master data  including party, product, location or reference data.  Capabilities include data validation with alerts, business rule management, workflow and approval management, role and column based access control and complete management of a “golden” logical record.  A single solution supports multiple domains and a variety of integration patterns including analytical and operational MDM.  Teradata MDM is a powerful and flexible solution that lets our customers approach master data and data governance projects in the order and level of complexity that drives the most business value.

Audience Level:
All Levels

Speaker:
Robert Rich Robert Rich
Global Program Director MDM
Teradata Corporation

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Tuesday
June 24
11:45–12:15

 

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Agile, Efficient, and High Quality Data Integration & Governance via Smart Enterprise Data Management
Marty Loughlin, Vice President, Financial Services, Cambridge Semantics Inc.
Smart Enterprise Data Management (EDM) is a new, more sensible paradigm for managing enterprise data. Smart EDM revolves around managing enterprise data at the semantic level--its essential meaning as understood by the business people--regardless of how fragmented, incomprehensible, or inconsistently the data is actually stored across enterprise systems. In addition, data governance, traceability, and usage tracking is a natural by-product of this semantic approach. Smart EDM simplifies complex data environments and puts business people in control of their data.

In this talk you will learn:

  • the six defining characteristics of Smart EDM
  • the value of a common, conceptual business model as the “data glue” that ties together your EDM activities
  • about Anzo Smart Data Integration, a Smart EDM solution that dramatically decreases the cost and improves the governance of traditional ETL projects

Audience Level:
All Levels

Speaker:
Marty Loughlin Marty Loughlin
Vice President, Financial Services
Cambridge Semantics Inc.

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Tuesday
June 24
11:45–12:15

 

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The Celebrity of Data – Changing the Way Data is Viewed, Treated and Managed
Lisa Dodson, Data Management Practice Leader, SAS
DAMA defines data management as “…the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets.”  In this session Lisa Dodson will discuss and walk attendees through a scenario that illustrates how SAS Data Management encompasses all the aspects of managing data including data integration, data quality, data federation, data governance and master data management. Through a proven modernization strategy enabled by a phased approach, attendees will understand how they can experience success at any stage of their data maturity.

Audience Level:
All Levels

Speaker:
Lisa Dodson Lisa Dodson
Data Management Practice Leader
SAS

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Tuesday
June 24
11:45–12:15

 

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Data Governance at Hoag Hospital: The Synergy of Data Lineage, Business Glossary and Data Quality
Jessica Pham, Data Governance Analyst, Hoag Hospital
Dawid Duda, Director of Product Development, Compact Solutions
Hoag ’s Data Governance program focused initial efforts on improving data quality and data understanding. One of the key elements of this initiative is end to end data lineage, allow to track data flows all the way from a BI report to the warehouse to the ETL to the source system of records.

What attendees learn from this session:

  • The approach used to deploy an Enterprise Data Governance program and technology based on deep understanding of the computer systems and data flows to truly understand how does data flow and how it has been derived from the sources. We will showcase end to end lineage from QlikView, Informatica, Pl/SQLs, SQL scripts etc.
  • Exposure to a thriving Data Governance program in a complex environment where Data Lineage, Business Glossary and Data Quality all come into play together

Audience Level:
All Levels

Speakers:
Jessica Pham Jessica Pham
Data Governance Analyst
Hoag Hospital

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  Dawid Duda Dawid Duda
Director of Product Development
Compact Solutions

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12:15 - 12:30 Room Change
arrow12:30 - 1:00 DG AND IQ SOLUTIONS

Tuesday
June 24
12:30–1:00

 

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Let’s Change the Way we Look at MDM!
Gauthier Vasseur, Chief Operations Officer and co-founder, Semarchy

Come and hear the stories about Iulia (Marketing Analytics), Joris (Investment Banking), Kadima (Consumer Goods), and Serge (Automotive) who took on an MDM journey to take their business to the next step.

Learn how:

  • Compliance could be ensured,
  • Big Data Analytics achieved,
  • E-commerce efficiency delivered
  • and Customer Service improved.
  • Real case, real people, real results that show the true value of MDM.

Audience Level:
All Levels

Speaker:
Gauthier Vasseur Gauthier Vasseur
Chief Operations Officer and co-founder
Semarchy

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Tuesday
June 24
12:30–1:00

 

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Business Data Authority: a Data Organization for Strategic Advantage
Stan Christiaens, Co-founder and Operational Director, Collibra
To avoid drowning in today’s data deluge requires new processes and methodologies that establish business data authority over these valuable data assets. In simple terms, this translates into placing the appropriate ownership and control of data that affords greater agility and utility of business data. In doing so, businesses can start to gain control and establish scalable, systematic processes that avoid the pitfalls of reactive practices that limit the scope and purpose of various kinds of data.

That objective is all the more urgent today because business data is growing in scale, complexity and velocity as companies strive to gain access to more diverse data sets that span cloud, services, social technologies and mobile devices. And the insights and perspectives derived from these data sets are often nuanced in shades of grey, not black or white. But once the data is embraced, it becomes clear that it’s not a simple proposition to provide access to it because the data lacks contextual pointers, which helps in two ways: trust and identifying the right data assets.

In this session we will show how you can set up the Business Data Authority: Data Governance & Stewardship provide the right level of control and trust in data. By managing the business glossary, curating reference data, handling classifications, taxonomies and hierarchies, setting up policies and rules, measuring compliance, monitoring quality and resolving issues, facilitating data sharing, ... Data Stewards enable the process of data management.

We will make use of Collibra's Data Governance Center to show how the Business Data Authority works in practice, and how a configurable operating model (roles, workflow, organization, ...) drives the right level of adoption and business engagement to establish a sound approach to data maturity, and turn information into a competitive differentiator.

Audience Level:
All Levels

Speaker:
Stan Christiaens Stan Christiaens
Co-founder and Operational Director
Collibra

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Tuesday
June 24
12:30–1:00

 

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The Right MDM Matching Methodology for Building a True Customer Golden Record
Michael Ott, Sr. VP, Innovative Systems, Inc.
The core objective of implementing Customer Master Data Management is to build an accurate and complete representation for each individual or organizational customer. The ability to deliver this customer “Golden Record” depends primarily on the matching methodology that is used. If the right methodology is not used, it can jeopardize the large investment in an MDM project.
Attendees will learn about:
  • The range of customer matching methodologies available today
  • Their specific characteristics and impact on customer MDM data quality and required review
  • The origins of the cognitive-based approach and how it is changing the MDM matching paradigm
  • How to audit your existing matching quality in relation to best practice standards

Audience Level:
All Levels

Speaker:
Michael Ott Michael Ott
Sr. VP
Innovative Systems, Inc.

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1:00 - 2:00 Lunch
2:00 - 2:50 CONCURRENT SESSIONS

Tuesday
June 24
2:00–2:50

 

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Data Stewardship Staffing Model: Talent, Techniques, & Training
Maureen Spence, Sr. Manager, Data Management & Governance, Capital One

All successful programs lead back to people - a company’s most important asset.  Hiring the right people for the job can change the outcome for any company.  Since Data Stewardship is a culmination of skills, it leads to a more difficult path for hiring.  Hiring the right person for the job and Growing your team of Data Stewards is one of the key values to successful Data Governance in any company. 

In this session, Maureen Spence, winner of the 2013 Data Steward Award, will provide an adaptable hiring practice that can assist companies in the hiring of Data Stewardship roles.

Attendees will learn about:

  • Job requirements to job fit
  • Aligning job responsibilities to business value
  • Looking for talent beyond functional fit
  • Shaping job responsibilities to retain talent
  • Grow your talent
  • Survey your talent for future retention

Level of Audience
Introductory

Speaker:
Maureen Spence Maureen Spence
Sr. Manager, Data Management & Governance
Capital One

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Tuesday
June 24
2:00–2:50

 

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Launching a Data Governance Initiative at Railinc - The First 150 Days
Jose Solera, Director IT Enterprise Services & PMO, Railinc
David Weinberg, Data Architect, Railinc

Railinc is the data steward for the North American railroad industry. We provide data, information, data services, and software for our thousands of customers. Though a small company by most standards, our data stores are outsized--we handle more than 10 million transactions per day generated by freight Railroads such as Union Pacific and Canadian National. Some of this data is vital to the industry--incorrect information can stop trains. New initiatives will dramatically grow our data stores. Following the success of our MDM initiative and with corporate and industry support we launched a Data Governance Center of Excellence with a primary focus on data quality. Our presentation will recount the drivers for our program, the challenges we faced in remediating the data quality issues, and our successes to date. Our success is not simply important to Railinc, but to the American economy as a whole.

Topics:

  • Railinc and its role in the North America railroad industry
  • Challenges, costs, and risks of poor data quality to the railroads
  • Recapping the MDM experience
  • Building on the MDM experience to form and drive a Data Governance Center of Excellence
  • Successes to date

Level of Audience
Introductory

Speakers:
Jose Solera Jose Solera
Director IT Enterprise Services & PMO
Railinc

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  David Weinberg David Weinberg
Data Architect
Railinc

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Tuesday
June 24
2:00–2:50

 

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IQScoring: Establishing a Simple Information Quality Metric, A Useful Technique to Baseline, Monitor, and Raise Information Quality in Your Organization
Rodney Schackmann, Sr Staff Information Architect, Intel
Information Quality is core to effective business analysis and decision making. But if you can’t consistently measure the Information Quality, nor communicate IQ problems clearly and succinctly to executives or program sponsors, you may lose the opportunity to effectively assess and work the problem. The IQScoring Method helps you establish IQ baselines and IQScores can facilitate quicker communication – to get more and better discussions started, and ideally, more IQ projects worked.

A simple approach that helps work this common problem is amazingly absent, so we’ve devised the IQScoring method that can help. It is transforming the way we execute projects, and ultimately do business.

Level of Audience
Intermediate

Speaker:
Rodney Schackmann Rodney Schackmann
Sr Staff Information Architect
Intel

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Tuesday
June 24
2:00–2:50

 

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Cirque du soleil: When Data Value Drives the Show
Mario Cantin, Chief Data Strategist, Prodago
When your data is about Artists, Shows, Runs, Castings and Performances; When your business success factors are creativity, agility and innovation; When policies, structures and boundaries might be perceived as restrictive and non-productive, how do you govern data? How do you bring coherence in data management without impacting organizational performance?

The answer is value-driven data governance.

Topics discussed:
  • A pragmatic, quick payback, value-based framework to data governance
  • Key milestones that paved the successful implementation path at Cirque du soleil
  • What's needed to make IT projects and key stakeholders accountable of data quality
  • Outline how the data governance business case can be self-sustained when driven by data quality requirements
  • Share key success factors, key takeaways ... and clowny moments

Level of Audience
Intermediate

Speaker:
Mario Cantin Mario Cantin
Chief Data Strategist
Prodago

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Tuesday
June 24
2:00–2:50

 

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Integrating Data Governance through a Data Management Assessment: A Case Study with Fannie Mae
Funmi Balogun, Director, Enterprise Data Standards and Governance, Fannie Mae
Melanie Mecca, Product Manager, Data Management Maturity Model, CMMI Institute

This case study illustrates how Fannie Mae leveraged a Data Management Maturity (DMM) evaluation to support the business case for continued corporate investment in the data program, develop a data management capability improvement plan, and strengthen data governance integration and data quality initiatives. The DMM provides a common language for data management practices and a graduated path to maturity integrating the proven approach of Capability Maturity Model Integration (CMMI). The assessment highlights existing best practices for broader application; facilitates dialogue across the organization; and fosters a shared vision for the collaborative culture needed for sustained success.

Level of Audience
Intermediate

Speakers:
Funmi Balogun Funmi Balogun
Director, Enterprise Data Standards and Governance
Fannie Mae

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  Melanie Mecca Melanie Mecca
Product Manager, Data Management Maturity Model
CMMI Institute

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Tuesday
June 24
2:00–2:50

 

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Data Governance for Enterprise Reference Data
Lowell W. Fryman, Sr. Principal, Aspen Information Solutions

Many organizations continue to deal with reference data as an application-level issue. After all, every application requires some reference data. Multiple business units may use similar reference data and may even be purchasing similar data from multiple diverse data service providers at significant cost to the organization. Experience shows that upwards of  80% of reference data may be used by multiple applications. Clearly, common reference data should be considered “enterprise data” and governed through the Data Governance group. Common reference data often crosses subject area lines and therefore difficult to identify which Data Steward may be responsible for managing it. Moreover, reference data can often be seen as an unwanted stepchild and left behind for applications to manage. However, many of the organization’s metrics and regulatory reporting may now depend upon common reference data. This session discusses common symptoms for the lack of reference data management and how to instantiate governance – either formally or informally – by covering these key issues:
  • Identifying what is "common reference data" & which Data Stewards/SMEs are the trustees when reference data crosses Lines of Business.
  • Dealing with regulatory bodies & leveraging governmental agencies as sources of reference data
  • Creating new ways for informal governance of reference data while leveraging "natural entry points" to take advantage of business events in the business

Audience Level
Introductory

Speaker:
Lowell W. Fryman Lowell W. Fryman
Sr. Principal
Aspen Information Solutions

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2:50 - 3:10 Room Change
arrow3:10 - 4:00 CONCURRENT SESSIONS

Tuesday
June 24
3:10–4:00

 

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Tribbles and Borg: Seeding the Starship Governance 
George Yuhasz, Director, Data Process & Governance, HealthNow New York Inc.

HealthNow initiated a Data Governance program in 2012 aimed at rationalizing the complex and siloed current-day processes with a lean and agile approach to governance and stewardship. The initial round of metrics that described each subject area has been based on Data Completeness, Accuracy & Timeliness. These KPIs are aligned specifically to organizational cost, risk and revenue opportunities.

This session will look at how Data Governance at HealthNow has grown from an academic approach based on best practices into a parallel series of initiatives driving transformation in people, process, technology and culture. Using the Star Trek motif of seeding the Enterprise with Tribbles and evolving the organization into an assimilated instance of Borg, the audience will leave with an appreciation of the importance of marketing, relationship building, stakeholder satisfaction AND successful delivery in any data governance implementation.

Level of Audience
Intermediate

Speaker:
George Yuhasz George Yuhasz
Director, Data Process & Governance
HealthNow New York Inc.

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Tuesday
June 24
3:10–4:00

 

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What the DG *?@!
Kimberly Nevala, Director Business Strategy, SAS Best Practices

In an era where data is a competitive asset and product in its own right, data governance is crucial.  Especially as big data, mobile applications and other emerging practices challenge existing paradigms for the use, access, and sharing of data. 

But I’ll say it: data governance is hard. And once data governance gets a bad rap, it’s hard to recover. 

In fact, the very mention of “governance” can evoke the specter of management overhead and bureaucracy.  Paired with unclear priorities or slow and ineffective processes (to name a few) this perception can topple even the most well intentioned governance program.  

Using lessons learned from the early adopters and emerging leaders we’ll uncover the common causes for data governance failures.  The discussion will touch on organizational, cultural and operational gotchas and provide programmatic solutions to avoid – or address – these issues. 

Level of Audience
Intermediate

Speaker:
Kimberly Nevala Kimberly Nevala
Director Business Strategy
SAS Best Practices

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Tuesday
June 24
3:10–4:00

 

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A Balanced Scorecard Approach to Data Governance
Catherine Folkes, President, Data Best Practices

Defining the goals of a Data Governance program assures a manageable road map, measurable goals and a higher rate of executive support. Using a Balanced Scorecard approach to Data Governance ensures the identification of tangible and intangible requirements and provides a platform for a shared dialogue across organizations. This session will outline the framework for a Balanced Scorecard approach to Data Governance and understand how this approach brings both the technical and the nontechnical users together ensuring an efficient, high impact outcome

Level of Audience
Intermediate

Speaker:
Catherine Folkes Catherine Folkes
President
Data Best Practices

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Tuesday
June 24
3:10–4:00

 

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Extending Asset Management Practices to Information Assets – A Foundation for Successful Information Governance
Duncan Munro, IT Program Manager, Seattle Public Utilities

Asset Management lies at the core of service delivery for Seattle Public Utilities (SPU). Via a 'triple-bottom-line' evaluation, graded in proportion to the cost and complexity of an investment, SPU is able to apply consistent and transparent decision making that ensures we deliver the highest economic, environmental and social benefits to our customers. Recognizing that information is a fundamental underpinning of our Asset Management practice, SPU has developed a model of governance and stewardship for the information lifecycle that aligns with our asset management practice. The model has been applied to the information that is used in the processes that manage the drainage and wastewater systems that serve SPU customers. As our approach to governing and managing information as an asset matures we plan to extend the model to each information subject area used in service delivery.

Presentation of the model will allow audiences to understand how the approach:

  • Incorporates quality dimensions for each phase of the lifecycle as a part of the asset value proposition
  • Provides a performance-based reporting process that measures for each quality dimension if we are delivering to expectations
  • Leverages the governance and stewardship processes as new domains of information are encompassed
  • Creates a basis for organizational change management as our governance practice matures
  • Aligns with approaches to establishing an enterprise architecture for the agency

Level of Audience
Intermediate

Speaker:
Duncan Munro Duncan Munro
IT Program Manager
Seattle Public Utilities

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Tuesday
June 24
3:10–4:00

 

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How to Enhance Data Stewardship with a Governance Workflow & Asset Approval Process  
Denis Kosar, Data Governance Consultant, Knowledge Masters LLC
Kevin Shannon, VP Data Strategy Operations, Standard and Poor’s

This presentation will provide an overview of Standard and Poor's Data Stewardship program and how the introduction of a formal workflow, Governance Approval Processes and the shared management of Business Assets has enhanced data governance and data stewardship.

We will also explore the introduction of a pilot project which established a formal approval workflow, a formal definition of data stewardship and the creation of a Procedural User Guide using a Data Governance Management tool. This User Guide was developed to assist the Data Stewards in providing them a training aid and reference source.
 
The presentation consists of the following:

  • Overview of S&P and Data Strategy & Operations (DSO) Organization
  • Current state of Data Stewardship
  • Target state of Data Stewardship
  • Workflow improvement and tool use
  • Stewardship training, certification and stewardship user guide

Level of Audience
Intermediate

Speakers:
Denis Kosar Denis Kosar
Data Governance Consultant
Knowledge Masters LLC

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  Kevin Shannon Kevin Shannon
VP Data Strategy Operations
Standard and Poor’s

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Tuesday
June 24
3:10–4:00

 

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How to Develop a Complete Enterprise Data Governance Policy  
Robert S. Seiner, President/Publisher, KIK Consulting / TDAN.com

An Enterprise Data Governance Policy defines the organization's (or Business Unit's) core principles of what it means to "govern" data and the dimensions of how to measure governance effectiveness.

Whether or not a policy is required by your organization, this artifact becomes a valuable communications and awareness tool, and can become the backbone of a successful Data Governance program.

In this session, Bob Seiner will define a structure for policy development and walk the attendees through the components step by step resulting in a complete Enterprise Data Governance Policy. Attendees can expect to walk away with an end product ready for consideration in their organization.

Level of Audience
Introductory

Speaker:
Robert S. Seiner Robert S. Seiner
President/Publisher
KIK Consulting / TDAN.com

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4:00 - 4:40 Coffee Break & Exhibits Open
arrow4:40 - 5:30 CONCURRENT SESSIONS

Tuesday
June 24
4:40–5:30

 

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Successful Global Data Governance-One Small Win at a Time 
Mark Nadeau, Director, Global Marketing Technology and Data Governance Office, Schneider Electric
Mario Rosso, Data Governance Process Manager, Schneider Electric

Using a small-win approach, the Schneider Electric Global Marketing and Customer Data Governance program found success by chipping away at persistent data management concerns in the face of evaporating resources, global culture-clashes, sponsorship turnover, and competing demands.

The Schneider team discovered that a just-in-time, iterative approach to governance combined with the guiding principles of data quality improvement, process enablement, and promotion of process efficiency was a far more effective strategy to trying to “boil the ocean”. The cumulative impact of each tiny victory, while not headline worthy on a case-by-case basis, resulted in added value to the business and establishment of a sustained commitment to improved data management.

Topics covered include:

  • Origins – the establishment of a global customer master data governance program from recognition of a need for improved data management, through loss of expected resources and sponsorship, and ultimate recovery and success
  • Obstacles - a look at familiar and not-so familiar roadblocks to governance success
  • Approach - an outline of how well-thought-out strategy mixed with seat-of-the pants execution helped the team in overcoming challenges and embracing opportunities
  • Results - a review of lessons-learned: what worked, what didn't, and what still needs to be done

Level of Audience
Introductory

Speakers:
Mark Nadeau Mark Nadeau
Director, Global Marketing Technology
and Data Governance Office
Schneider Electric

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  Mario Rosso Mario Rosso
Data Governance Process Manager
Schneider Electric

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Tuesday
June 24
4:40–5:30

 

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Introduction to Entity Resolution and Master Data Life Cycle Management
John R. Talburt, Professor, UALR Information Science

The inability to properly integrate the same information coming from multiple sources is one of the leading causes of poor data quality in an organization. Whether it is the failure to recognize the same customer making transactions through different sales channels or to aggregate sales of the same product, the negative impact on business can be significant. Effective master data management requires that both IT and Business understand and address the complete life cycle of master data and the fundamental principles of entity resolution (ER).  This presentation provides an introduction to current practice in data matching, record linking, and entity information life cycle management that are foundational to building an effective strategy to improve data integration and master data management (MDM).

Major topics include

  • What is entity resolution and why is it important
  • An overview of the four major entity resolution architectures
  • An overview of the CSRUD entity information life cycle
  • The advantages and disadvantages of deterministic and probabilistic record matching
  • Strengths and weaknesses of commonly used approximate match algorithms
  • Methods for assessing entity identity integrity

This talk provides an introductory-level overview of entity resolution and the master data life cycle that underpin MDM and entity-based data integration.  It is appropriate for both business and IT professionals attending the conference.

Level of Audience
Introductory

Speaker:
John R. Talburt John R. Talburt
Professor
UALR Information Science

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Tuesday
June 24
4:40–5:30

 

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Strategic Data Governance – Leveraging a Federated Data Governance Model
Kira Chuchom, Data Governance, Microsoft

This case study is from a data governance practitioner’s perspective within Microsoft's internal Data Governance efforts, and builds upon how to establish local data governance programs by creating a Data Governance Community from the “outside” in.

Topics covered in this session include:
  •  What problem(s) are we trying to solve?
  • Incorporating history, data ecosystem and culture
  • Why choose a Federated model?
  • What is a local Data Governance Office?
  • Who are the players?
  • Where are the connection points?
  • How to deploy Worldwide? And sustain?
  • Examples of Active Listening in action
  • How to measure success

Level of Audience
Intermediate

Speaker:
Kira Chuchom Kira Chuchom
Data Governance
Microsoft

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Tuesday
June 24
4:40–5:30

 

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Healthy Data Governance at Spectrum Health
Timothy Zeddies, AVP, Analytics & Improvement, Spectrum Health

This session shows Spectrum Health's approach and lessons learnt in implementing, practicing and maturing Data Governance.

Topics:

  • Why is data important in a healthcare organization
  • How did we get started
  • How we've create a culture of data governance
  • How we’ve used data governance tool(Collibra) to operationalize data governance
  • How we're making consensus-driven data standards and policies available across the organization.
  • How we've Integrate data governance within the project life cycle
  • How we're promoting data standardization and consistency
  • Best practices for DG implementation  and lessons learned

Level of Audience
Introductory

Speaker:
Timothy Zeddies Timothy Zeddies
AVP, Analytics & Improvement
Spectrum Health

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Tuesday
June 24
4:40–5:30

 

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Rising Business Complexity and Impact on DQ and Governance
Michael Scofield, Assistant Professor, Loma Linda University

Expensive business software often fails over time because the business evolves into greater complexity. This discussion shows how to anticipate such complexity.

Nearly all enterprises are becoming more complex, and for most, application architecture cannot morph to support it. So business leaders who thought they would get a long payback on their investment in business software (purchased, or home-grown) will be disappointed. We will review the seven major causes of business complexity. These are useful for those authorizing major business application projects, as well as data architects to anticipate business morphing which may render the investment obsolete. Also, when negotiating data exchanges and purchases, architecture and evolution must be considered. What is the consequence of this on DQ and data governance?

Each of the seven causes has leading indicators. A major cause is government regulation, and the ACA is a great example of mind-boggling complexity--for all involved.

In this session, you will learn:

  • The seven causes of enterprise complexity
  • What external forces may be driving that complexity
  • Techniques for protecting your data against external pressures
  • Questions to ask of business experts to anticipate increasing complexity

Level of Audience
Intermediate

Speaker:
Michael Scofield Michael Scofield
Assistant Professor
Loma Linda University

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Tuesday
June 24
4:40–5:30

 

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Data Analysis – the Forgotten Component of Data Governance
Cheri Mallory, North American Information Management Practice Lead, SAP

Most information governance teams are well-tuned on designing the process for managing enterprise data. First, they create policies and standards, as well as enforcement procedures at the point of entry or when harmonizing the data. Next, they build the data governance team and discuss oversight and how they will track and support those policies. Then, they talk about the technical support to do the data monitoring. However, what you rarely see called out is the middle part of that process – data analysis.  This type of research work can consume a large amount of time. Yet, you never see this called out in the plan or budget. This session discusses the forgotten component of data governance in detail so you can better account for and build it into your data governance plan.

Level of Audience
Intermediate

Speaker:
Cheri Mallory Cheri Mallory
North American Information Management Practice Lead
SAP

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5:30 - 7:45 EXHIBITS AND RECEPTION
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