Conference Sessions - June 8, 2010
| Tuesday 8 June 7:30–8:30 |
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| 7:30 - 8:15 SPECIAL INTEREST GROUPS | ||||||||||
Tuesday |
Health Care and the Challenges for Data Governance
David Loshin, Knowledge Integrity Health care is a topic that is currently being hotly debated in the halls of government across the US. In turn, numerous countries have tackled the challenges of managing universal health care in a variety of ways. Whether the issue to be addressed is enterprise patient master indexes, fraud analysis and detection, determination of coverage, pay for performance, all solutions are clearly going to be dependent on the effective management of sensitive data. This SIG is intended for an open discussion of perceptions of the upcoming challenges in data governance in the health care arena for the next 3/5/10 years. |
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Tuesday |
Meta-Data Professional Organization Meeting Metadata and Data Governance-A Two Way Street William Brooks, Meta-Data Professional Organization and MFS Investment Management Robert S. Seiner, KIK Consulting & TDAN.com The Meta-Data Professional Organization (MPO) is a non-profit international association comprised of business and IT professionals in all areas of meta-data practice. The MPO brings together individuals with interests, expertise, or hands-on experience in meta-data use from all areas of private and public enterprise throughout the world and seeks to disseminate technical and professional information to meta-data practitioners of all levels of experience. Join us on Tuesday morning June 8th and learn more about the MPO, including the progress with the MPO meta-data tool functionality matrix and participate in a discussion on Meta-Data and Data Governance led by Robert S. Seiner. This meeting is open to all conference attendees. You do not need to be a member of the MPO to attend this meeting. For further information about the MPO please visit www.metadataprofessional.org. |
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Tuesday |
The Job of the Information/Data Quality Professional
Christian Walenta, IAIDQ For the first time, a broad group of information/data quality practitioners across the globe has reached consensus on the definition of the job of information/data quality professional. This achievement represents a major milestone in the establishment of information/data quality as a distinct profession. This breakthrough is the outcome of a job analysis study recently conducted by IAIDQ. This presentation describes the consensus definition of the job of an Information Quality Professional, organized into a framework containing six performance domains, twenty-nine tasks, and more than three hundred distinct knowledge areas and skills. After it was validated by a large international group of information/data quality practitioners, the framework was used to develop the specifications for IAIDQ’s upcoming CIQP exam. Beyond this primary purpose, the framework is also expected to drive an increase in the quality and consistency of the information/data quality training available in the market place, and to provide a benchmark against which organizations can assess their information/data quality practices. |
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| Tuesday 8 June 8:30–8:45 |
Welcome |
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| Tuesday 8 June 8:45–9:45 |
KEYNOTE: Building
the Business Case for Data Quality Through Data Governance Andy Hayler, Information Difference Drawing on research conducted by the Information Difference, and his practical experience on major data initiatives, Andy will discuss the state of data quality today, what can be done to improve things and how the profile of data quality has increased due to data governance and MDM initiatives. Topics:
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| 10:15 - 11:15 CONCURRENT SESSIONS | ||||||||||
Tuesday
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Enabling the Business Governance of Data
Ian Rowlands, ASG Data governance approaches in organizations today are often piecemeal at best. In the context of the enterprise data landscape they are managed, but often still have critical gaps. Enterprise metadata technology has been used successfully to bridge those gaps. However, at a technical level there still remains a nagging disconnect between business and technology users' understanding of key business concepts, which can leave the door open to other governance exposure. In this session we will describe how two ASG customers are linking technical metadata to the formal management of business terms to achieve true enterprise data governance. |
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Tuesday
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The Novo Nordisk Data
Governance Journey - from 0 to 60 in 6 months
Christopher McBride, Novo Nordisk Inc Novo Nordisk's Master Data Management initiative kicked off in 2007, but needed to show more progress to both our business partners and IT. We were able to take some not-too-difficult steps to the point where believe we can be a center of excellence for Novo Nordisk on a global basis. Among the things we did:
This presentation will let you know what we did, how we did it, and what you may be able to do to replicate our successes and avoid our problems. |
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Tuesday
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Utilizing
an Effective Support Team to Augment and Actually Improve Your Data Governance
and Data Quality Dan Holle, Pfizer In many cases, organizations focus on Data Governance only during the Design and Development phases of a project. Once the application goes into Production, data governance becomes an afterthought that is left to Data Stewards at its best and ignored completely at its worst. On a daily basis, application support teams see the reality of what happens to data, starting at the source system, as it progresses through the various layers of an application, and finally how it is consumed by downstream systems. All of these areas are likely to conflict with what might have been originally envisioned by the Project Data Governance Teams. With proper processes put into place, an effective support team will routinely communicate with Data Governance groups to continually improve processes, constraints, and other data processes as it proceeds through these systems. This presentation will address:
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Tuesday
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Lessons
Learned in Data Governance at Lexmark Sreedhar Srikant, Lexmark Laura Avent, Lexmark Lexmark It has been two years since a formal Data Governance initiative was launched at Lexmark. We have successfully delivered the first phase of Master Data Management at Lexmark in 5 different domains including Customer, Vendor, Product, Material and Person. This includes successfully supporting the first phase of ERP rollout that used clean master data from MDM as part of data migration. In this presentation, we will share our lessons learnt around data governance including change management, organizational structure and as well chart out upcoming challenges. The presentation will provide the following benefits:
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Tuesday
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Value
Driven Information Quality – Best Practices to Emulate and Pitfalls to Avoid Altug Gurer, Avaya and IAIDQ Avaya uses a value-driven approach to prioritize information quality efforts based on business benefits. Over the years, we have positioned our Data Quality organization as a “business within a business” with its own P&L, and built a formal process and methodology that provide a proactive approach to Data Quality Management. This presentation gives an overview of our approach, describes the factors that led to our successes, and identifies pitfalls along the way. Topics include:
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Tuesday
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The
Stepping Stones for an Enterprise Data Quality Program Brett Medalen, Sallie Mae Danette McGilvray, Granite Falls Join us to hear how Sallie Mae evolved from grass roots efforts to a funded, enterprise Data Quality program. Starting with a point solution for a single business area to multiple uses of a data profiling tool, we are now implementing a formal enterprise program. We will share lessons learned along the way. Topics include:
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| 11:30
- 12:00 GOVERNANCE AND IQ SOLUTIONS |
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Tuesday
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Collaborative IM Governance Equals Competitive Advantage In many organizations today, the practice of perpetuating information silos is still acceptable, even though this is an inefficient way to manage information assets. Each organization has a base set of knowledge about their data and has people who understand the data's definition and use. These individuals also understand the issues and pitfalls of how this data can be leveraged for competitive advantage. Typically, organizations try to control and maintain key operational and decision support data via various types of processes and methodologies, often unsuccessfully. Given these challenges, the need to deliver successful Data Governance has become extremely critical as the volume and complexity of data has increased exponentially. While Data Governance is viewed as something that is extremely important, it often seems to be the first program that is cut due to budget constraints when compared with other mission critical initiatives. An effective collaboration between existing mission critical data intensive projects and information management efforts can result in a pragmatic approach which provides an “entry point” for an organization- wide Data Governance program. This dynamic makes a Data Governance program more effective and provides organizations with a real return on investments that yield concrete benefits. The collaborative approach sets attainable business and technology expectations and can be completed incrementally through alignment with key data initiatives such as Enterprise Data Management, Business Intelligence, Master Data Management, Data Quality Management, ERP package implementations and Meta-Data Management. Using case studies we will present the approaches and processes needed to develop and implement successful collaborative Data Governance programs. We'll use the case studies to describe:
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Tuesday
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From Data Discovery to MDM: The Single-Platform Approach to Data Management Companies worldwide are struggling with the spread of data throughout the enterprise – and an ever-growing mix of technologies to manage that data. In this session, participants will learn how a single platform for the key requirements of data management, including data quality and data integration capabilities, can help companies fix their data challenges today and realize immediate benefits from their data assets. The session will also demonstrate how these capabilities can be extended to build the foundation for more complex, resource-intensive goals like data governance and MDM. |
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Tuesday
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Effective Data Governance through Automated Feedback Control - A Case Study Data Governance is seen as the solution to solve all data problems. However this governance process could be solitary and mono directional without proper feedback, thus making it ineffective, slow and very costly. To be successful, governance should start as early as possible (during SDLC) and has to be dynamic and constantly evolving. Governance always circles around timeliness, completeness and data quality, hence an automated system that consistently measures deviation and provides deviation feedback to the Governance is the need of the hour. Attendees will learn:
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Tuesday
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From Chaos to Order: Crossing the Chasm from Reactive to Proactive Data Governance When data is not made available in the right form to the right users at the right time, the business as a whole suffers. Traditionally the management of data consistency has been reactive – a small group of data stewards, who on behalf of the entire enterprise, manage data consistency, creating bottleneck. As a result, business users do not have access to reliable data within their applications in a timely manner. Leading organizations are adopting proactive data governance to deliver immediate efficiencies within business functions, thus improving business user productivity and increasing business adoption. In this presentation you will learn:
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| 1:15 - 2:15 CONCURRENT SESSIONS | ||||||||||
Tuesday
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How Farmers Insurance
Governs Metadata Dan Myers, Farmers Insurance Group Attendees will learn how to govern metadata collection during the software development life-cycle phases based on a case-study at Farmers Insurance. It became clear that due to increasing IT development and maintenance costs for BI projects, metadata management should be implemented to shorten the life-cycle and reduce analysis time. Business Intelligence users at Farmers Insurance were also struggling to understand and find the data they needed prior to implementation of this governance process. By establishing a concrete and comprehensive metadata governance process, validating metadata at each phase of the project, your organization can maximize data search ability and usefulness of your enterprise metadata repository. Attendees will learn how metadata governance can:
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Tuesday
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Data
Governance: Balancing People, Policies, & Procedures with Technology Becky Briggs, ARC By implementing a flexible Data Governance Business Model over time, ARC has created a successful program managing data processes and data quality while balancing the ability to measure and manage change as the program evolves. Data governance means strategically managing data assets in a way that promotes business value, integrity, usability, security and consistency across the company. Managing industry-wide data as the fundamental building block for developing products and services, ARC has formalized and improved their data-oriented business processes by instituting a comprehensive, best practice award winning, multi-year implementation data governance program. ARC's now mature approach to data governance is innovative in its use of data warehouse infrastructure (which is designed and extended to serve both internal and external customers and partners) and its comprehensive data governance policies and procedures for data classification, security, access, data quality and responsible data and information distribution through our products and services.
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Tuesday
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Governing
the Cloud - Impact of Cloud Computing
on Data Governance Cloud computing is a new paradigm that is proving highly attractive to many enterprises. Its basic value proposition is to reduce capital expenditure in infrastructure by utilizing services that are paid for as they are used. However, one of the major issues is that of governance. Within its own firewalls an enterprise can have as little governance as it can get away with. The cloud is a shared environment with multi-tenancy and worries about data governance are a major concern. Data movement auditing, contractual aspects concerning promises made to stakeholders about data, chargeback management, data provisioning and purging rules, are just a few examples of data governance issues that are poorly recognized in private infrastructures, but which are important in the cloud. This presentation looks at the enhanced governance required for cloud computing. It also looks at cloud beyond the pure infrastructure play, to private clouds and columnar databases, and the governance challenges they raise. Attendees will learn:
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Tuesday
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Setting
up Data Stewardship in an Ever Changing World Data governance and especially data stewardship should be implemented relative to at least two dimensions: 1) organizational maturity and 2) organizational "size." Understanding these dimensions will permit organizations to determine the most successful "starting point" for their respective organizations. It also permits organizations to forecast likely data governance growth and maturation paths. We will illustrate using experiences drawn from organizations that are constantly evolving to stay competitive. We will describe the
This type of guidance has been helpful for organizations when establishing Governance/Stewardship initiatives but has proven especially useful when interaction protocols with business partners of differing levels of governance maturity and in merger/acquisition situations. |
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Tuesday
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Selling
the Data Quality Strategy Unlike enterprise application projects which have tangible and more visible results, data quality initiatives are a tougher sell to the executives who need to fund and support the projects. Data quality projects don’t necessarily produce new applications or reports – things the executives can see – and often require more coordination across the enterprise than other types of projects. Obtaining executive buy-in – and the financial support to make these projects successful – is a different challenge for data quality leaders This session presents a proven approach towards making the case for a data quality initiative and selling the case to executive decision makers. Some of the topics that will be covered include:
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Tuesday
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Making it Real: Operationalizing Data Governance Kevin Cusack, Accenture Winston Chen, Kalido Companies that can successfully turn data governance into a core competency will improve the quality of their data and realize pervasive business benefits. But data governance programs can be difficult to implement, sustain and expand due the lack of clarity in prescribed activities on a day-to-day basis. Data governance becomes real only when it’s fully operationalized. This session presents a framework and best practices for making data governance a clearly structured business process through data policy management, process operationalization, and compliance monitoring and measurement. We will illustrate, using real-world use cases from Accenture and Kalido, how to transform your business by making data governance transparent, orchestrated, measurable, and aligned with business objectives. |
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| 2:30
- 3:30 CONCURRENT SESSIONS |
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Tuesday
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Data Governance for Security and
Privacy, a Technical Framework Javier Salido, Microsoft Corporation Organizations struggle every day with the tradeoffs resulting from the need to maximize benefits derived from IT, while preserving the confidentiality of customer and company data. Creating a technical framework for data governance can help you deal with privacy and security issues, and provide a roadmap for the implementation and execution of governance, risk and compliance. Find out what components need to be considered when implementing a technical framework; and participate in a discussion of the data lifecycle for privacy and security, how it can be leveraged to identify threats to data resources, and the evaluation and implementation of controls that support your organization's data governance efforts. We will also discuss an extension of this framework to cloud computing. |
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Tuesday
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An
Information Governance Case Study – Not Just for Data Anymore
David Bartholomew, Southern California Edison Southern California Edison (SCE) Power Procurement Business Unit (PPBU) is implementing an “Information Governance Practice” to manage risk and improve the quality of selected information by focusing on people, process, data and the use of technology. The Information Governance Practice is directed at effective and efficient use of information to improve decision making leading to improvements in operational management and risk mitigation. Information Governance at SCE has been defined as “formalizing and guiding the behavior over the definition, production and use of information and information related assets.” During this presentation, David Bartholomew will describe how SCE PPBU designed the Information Governance Practice to achieve two levels of “formalizing behavior” around information:
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Tuesday
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Establishing
a Data Management Function -The First Step Towards Governance Laura Madison, Rabobank International This is a real-world example of how I have learned about Data Management and then applied what I learned to my own organization. Successes & Challenges of Trying to Build a Data Management Initiative – 1 year later
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Tuesday
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Data
Governance Is a Journey-Not a Destination
Tom Orrick, Hallmark Cards, Inc Debbie D'Agostino, Hallmark Cards, Inc Like many other businesses, Hallmark Cards has launched a data governance initiative with a mission to establish data as a strategic asset. In this session, participants will see a real life case study showing how it began and deployed their data governance program. Keep in mind that data governance is a journey … not a destination. The itinerary for this journey includes:
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Tuesday
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Data Quality Challenges and Solution Approaches in Yahoo!'s Massive Data Environment
Aparna Vani, Yahoo Dan Defend, Yahoo After significant initial Data Quality wins in Yahoo!’s Audience pipeline, there are many technical challenges in identifying next level DQ issues and organizational challenges of institutionalizing the data quality program in Yahoo!’s massive data environment. Areas of focus includes: Data Monitoring and alerting challenges, Browser Cookie Churn, Traffic Protection (robot filtering). |
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Tuesday
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The U.S.
Federal Data Quality Framework
Suzanne Acar, U.S. Federal Data Subcommittee Federal agencies and Communities of Interest often have a number of data quality disciplines at their disposal, but rarely will they implement all disciplines at once because improving data quality is a process and not an event. The Federal Data Quality Framework is designed to provide guidance for consistent understanding and practices of data quality across government agencies and Communities of Interest. Dr. Suzanne Acar, Co-chair of the Federal Data Architecture Subcommittee, will provide an overview of the Framework and how it is being used by agencies. Points of emphasis will include:
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| 4:00 - 5:00 KEYNOTE | ||||||||||
Tuesday
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KEYNOTE:Mastering
The Human Side of Data Governance and Data Quality: Inevitable Scenarios and
Powerful Principles to Effectively Deal With Them Len Silverston, Universal Data Models, LLC In most data governance and data quality efforts, there are common and inevitable scenarios involving human dynamics that occur and the success of these programs depends on how we handle them. So why not be prepared and learn principles and techniques that have been proven to work and be aware of common mistakes to avoid? A key to data governance and data quality is to understand the personal, cultural, and political environment and to consciously employ best practices regarding the human element that is involved. The most effective enterprise wide data governance and data quality efforts usually share one thing in common: they have had or have developed effective ways to handle common scenarios that are bound to occur, and they develop an environment that provides fertile ground for success. This presentation will share principles, techniques, and exercises to help tackle these common scenarios and to understand and move toward an environment that enables data governance, data quality and data integration. It will provide stories of successful and unsuccessful efforts, illustrating why some programs succeed and others fail, as well as what we can do to avoid pitfalls. You Will Learn
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| 5:00 - 7:45 EXHIBITS AND RECEPTION | ||||||||||
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