Pre-Conference Tutorials and Conference Sessions -
June 8, 2015
| Monday June 8 7:30–8:30 |
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| 8:30 - 11:45 MORNING TUTORIALS | |||||||||||||||||
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T1: Getting Started with Data Governance Robert S. Seiner, President/Principal, KIK Consulting / TDAN.com There is a set of customary tasks that are required to define, develop and deploy a Data Governance program. These tasks, though they may differ from organization to organization, are consistent in what they set out to accomplish. These tasks become the backbone upon which the successful program is built and supported. These tasks include, but are not limited to, the justification for Data Governance, defining the resources required, and ultimately deciding the appropriate approach that will best match your organization’s culture and tolerance for change. In this tutorial session Bob Seiner will walk through the basics tasks of getting started with a data governance program while detailing the steps that are required to complete the primary components. Do-it-yourself templates and tools will be shared and discussed as well as how to leverage existing strengths while addressing opportunities to improve. In this session Bob will present techniques for defining:
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Monday
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T2: Successful MDM Starts with Effective Data Governance and Stewardship David Plotkin, Advisory Consultant, EMC2 Master Data Management (MDM) is a crucial exercise that many companies go through as they attempt to use their data and leverage it for competitive advantage. But doing MDM properly has certain critical success factors, such as knowing where your data is, knowing that the data quality will support MDM, determining the sensitivity of the results to false matches, and having designated business decision makers for all the steps that MDM requires. This tutorial details the decision-making steps in MDM (both in design and maintenance), discusses WHY Data Governance is critical to each step, and the specifics of HOW it is critical (the role of the data steward in that step). You will see examples of each of the steps, and sample Data Governance processes needed to execute on those steps. You will learn:
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T3: Governing
the Business Vocabulary, Cataloguing and Mapping of the Business Terms and
Critical Data Elements (CDE) Lowell Fryman, Sr. Principal, Aspen Information Solutions Data Governance programs should address the definition of business terms, aligning the terms with the critical data elements associated, and communicating the alignment and data usage across the enterprise. A Business vocabulary and business terms is a great start, as well as one that provides significant value to the enterprise. While, many organizations really care about the physical columns used in reporting and analytics, we know that we need to align those CDEs to the Business Terms in order to govern our implementations. A Business Glossary is the tool for exposing authoritative content from our Data Governance initiatives. The Glossary is used to communicate understanding and clarity across the enterprise to connect business management and knowledge workers to business information they can trust, helping to eliminate misunderstandings that cause lost time, lost opportunities and lost revenue. The Business Glossary should be an early deliverable from your DG program and mature to include the logical and physical data constructs as a quality components to drive DG maturity and value. This tutorial will be helpful for data management and Governance professionals that have been challenged with any of the following issues:
From this tutorial you will learn:
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T4: Establishing Data Policies and Standards for Big Data Sunil Soares, Founder and Managing Partner, Information Asset, LLC In this tutorial, Sunil Soares will review an overall framework to establish data policies and standards to govern Big Data. Topics include the following:
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T5: Fundamentals of Data Profiling for the Data Quality
Practitioner David Loshin, President, Knowledge Integrity There is no doubt that data profiling is the technology that has had the greatest impact in transitioning organizations from reactive data correction to proactive data quality assurance than data profiling. And no data quality management program can be complete without blending the use of data profiling techniques with well-defined processes to review data, identify potential anomalies, engage business users to assess the criticality of data flaws, and formalize business rules for preemptive data validation. As data governance moves into the mainstream, it is critical to establish a fundamental understanding of what data profiling does, how data profiling tools are used, and preparing the environment for analysis. In turn, data quality practitioners can leverage data profiling techniques to contribute to the specification of data quality dimensions, corresponding metrics, and integration within operational processes for ongoing data quality assurance. In this tutorial, attendees will learn about:
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Monday
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T6: Preempting
and Addressing Data Quality Challenges using the DMM Framework Peter Aiken, Founding Director, VCU/Data Blueprint Melanie Mecca, Director of Data Management Products and Services, CMMI Institute Susan A. Yamin, Enterprise Data Governance Director, Ally This tutorial illustrates first the overt and then the hidden costs of data quality. Participants will then learn how to dig beyond the hype and understand that data quality is a shared business/IT responsibility, that requires modern methods. The CMMI Institute ’s Data management Maturity Model can play a key role providing a holistic framework for developing solutions. Topics include:
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| 12:00
- 12:30 DATA GOVERNANCE AND IQ SOLUTIONS |
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Maximizing Data Quality using Microsoft Excel (for business users) Victor W. Fehlberg, Chief Technology Officer, Aim Smart Too often data stewards are forced to depend entirely on IT for data quality systems because the tools in the marketplace are complicated to use. Aim-Smart is an easy-to-use Microsoft Excel companion that provides powerful data quality algorithms such as profiling, matching, deduplication, parsing and more, within a tool users are already familiar with – Excel. Users can leverage all of Excel’s features – filtering, sorting, coloring, functions, etc. and then, using the same tool they can employ the power of a full-featured data quality engine. This tutorial will show how to leverage this new Excel Add-In to make the most of your data. We will cover how to:
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Self-Service Data Preparation for IT & Business Srini Kumar, VP & Head of Product Management, Enterprise Information Products, SAP As the world transitions to an information-based economy, we rely on accessing the right information to do our work. But finding and accessing the needed data, validating its accuracy, and mashing it with other data is a challenge. Analysts often spend more time looking for and preparing information for analysis than conducting the analysis itself. SAP ’s solution to this challenge is a new self-service data preparation tool, SAP Agile Data Preparation. In this demonstration, you will see how to get fast and easy access to a variety of sources through a visual, interactive interface that simplifies data preparation for any initiative. Level of Audience |
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Data Lineage Case Study - DFAST/CCAR Regulatory Reporting Siddhartha Banerjee, Sr. Principal, Compact Solutions One of the top five Canadian Banks data governance program focuses on meeting data lineage and ancestry requirements for DFAST regulatory reporting. Phase 1 of the project involves proving data linage from AXIOM SL GRC software to Big Data Cloudera EDPP (Enterprise Data Provisioning Platform). One of the key elements of this initiative was deriving end-to end data lineage, providing the regulators capability track data flows all the way from a CDE in FY14 reports to the Hadoop Distribution layer. What attendees will leave from this session:
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| 1:30 - 4:45 AFTERNOON TUTORIALS | |||||||||||||||||
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T7: The
Successful Data Governance Manager Nicola Askham, The Data Governance Coach Calling all Data Governance Managers. Can you articulate your role clearly? Are you confident that you understand what you are trying to achieve? Do you feel confident when selling and implementing data governance? Join Nicola Askham, The Data Governance Coach, in this interactive session to:
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T8: Developing and Implementing Policies and
Standards to Manage Data as an Enterprise Asset Janet Lichtenberger, Director of the Enterprise Data Governance, Walgreens Policies and standards are a foundational pillar of a successful Enterprise Information Management program. Policies and standards can support the establishment of a program, and can help to mature an existing program. Policies and standards provide EIM programs with a way to build key partnerships, and to engage with both business and IT on topics that are important to the business and to executive sponsors. Polices and standards enrich a program, and have been central to usage, control, and valuation of enterprise data. Topics to be covered include:
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T9: Sustaining
Data Governance and Adding Value for the Long Term Kelle O’Neal, Founder and CEO, First San Francisco Partners Data Governance is becoming a more mature and better understood practice that reduces risk and creates value across all industries. Many organizations have launched Data Governance Programs either to support technology initiatives like Master Data Management, or to address compliance issues - such as Basel & LEI in the Financial Services industry, or The Sunshine Act & Unique Device Identification in the Health and Life Sciences sector. And many companies have even launched Data Governance more than once! Part of the reason many companies launch a Data Governance program again and again is that over time, it is challenging to maintain the enthusiasm and excitement that accompanies a newly initiated program. In this tutorial we will talk about the challenges of making Data Governance a “going concern” in your organization and how to sustain a program for the long term. We will cover:
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T10: Advanced Metadata Requirements for Data Governance Robert S. Seiner, President/Principal, KIK Consulting / TDAN.com Beginner and advanced Data Governance programs manage metadata as a significant resource used to improve the value and understanding of the data being governed. This holds true when you are governing the data in your data warehouse, the data in your MDM solution, the data in your enterprise platform(s) or data that ’s quality is being improved. Metadata can make or break an effective program. In this tutorial session, Bob Seiner will focus on moving beyond basic metadata requirements to expose tough questions that need to be asked and answered when considering the purchase versus the internal development of a Data Governance Metadata Platform. Bob will draw from corporate and client experience as a metadata repository administrator and share where he faced his most difficult challenges. The session will result in a useful list of requirements and questions for tool vendors and internal developers alike when defining requirements for your metadata efforts. In this session Bob will cover requirements for:
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T11: Defining and Using Data Quality Rules David Loshin, President, Knowledge Integrity Data quality rules provide the methods by which the results of data quality assessment can be employed for data validation. Yet there are different types of data quality rules that can be applied at different levels of precision in an assortment of platforms, products, and utilities. In many organizations, the absence of a formal framework for defining, managing, and deploying data quality rules allows for inconsistency in data validity despite a replicated effort in application. In this tutorial, we provide a framework for defining data validation and data transformation rules that can be managed as content, shared across the enterprise, and implemented consistently. We will show how data quality rules are to be aligned with enterprise metadata, how rules can be deployed in different execution models, and how data quality services can help operationalize key facets of a data stewardship capability. Attendees will learn about:
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T12: Setting Up a Data Quality Risk
Management Program at Your Organization Dr. Alexander Borek, Senior Consultant, Gartner Poor quality data often leads to major risks in all parts of an organization, affecting operational efficiency, bottom-line results, customer satisfaction and strategic decision making. Monitoring, measuring, and quantifying how information quality impacts business objectives on an enterprise wide scale - both financially and non-financially, can become, however, a tricky task. Based on best practices from several industries and key insights from his book, "Total Information Risk Management: Maximizing the Value of Data and Information Assets," Dr. Borek shows step-by-step how a data quality risk management program can be set up successfully and how data quality risks can be systematically identified and mitigated. As a result, a value tag can be put on data quality, which allows higher executive buy-in to data quality initiatives to reduce related risks. Level of Audience |
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| 5:00
- 5:50 Conference Sessions |
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Data Quality Centre: Lessons Learned from Three Years of
Managing and Sustaining Data Projects Noha Radwan, Senior Master Data Analyst, Schlumberger Lisa Cutler-Farwig, IT Project Manager, Schlumberger Schlumberger ’s Master Data program consists of data design projects which, upon closure, transition to maintenance in the Data Quality Centre (DQC). Data projects began about the same time the DQC was formed, in the beginning of 2012. Over the last few years we, master data project managers and DQC analysts, have grown together and learned what works well to enable a successful transition of a data design project to the DQC. We have also learned what we can do better in future projects to ensure the DQC is empowered to ensure high quality Master Data for the enterprise.
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Using Ground Level Data Quality Metrics To Drive Strategic Change Ed Cuoco, Director of Data Science, EnerNOC Detailed, recurring metrics for data quality can be more than indications of the fitness of the data for commercial purpose. When placed in context, these metrics can be used to drive strategic change in organizations and demonstrate financial value across your organizati In this talk, we will discuss how to contextualize ground-level data quality metrics, interpret them in terms of cultural, procedural and operational issues, and then communicate these findings to drive needed changes and demonstrate the financial value to the organization. These points will be illustrated via 3 mutually supporting case studies:
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The Role of the Data Steward in
Managing Unstructured Data, No Matter How Big It Is Mike Grosvenor, Technology Manager, Access Sciences Corp Big Data can be described as a significant increase in whichever type of information your organization is least comfortable managing. If that is unstructured content, then the volume of data will influence the selection of technology to classify, store and analyze it. Data Stewards balance the different systems, groups and activities they support in accordance with governance standards by shaping the way metadata is valued and developed in their organization. This focus on business value is a key success factor in Big Data initiatives and justifies involvement of a Data Steward. Major points:
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Driving a Data Council Meeting with an
Actionable Data Governance Dashboard Mario Cantin, Chief Data Strategist, Prodago Based on a real-life Data Governance Dashboard used in data driven organizations and actual meeting agenda, this session will focus on the following elements:
The objective of this presentation is to provide attendees with an interactive context where they will:
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Jump Starting Data
Governance Initiatives: Lessons Learned Angela Boyd, Data Steward Healthcare Informatics, BJC Healthcare This session includes the steps BJC HealthCare took to scope initiatives, obtain leadership buy-in, and overcome data management roadblocks. In the past three years, BJC HealthCare has partnered with peer groups and industry leaders to begin a data governance program, draft data management policies, and standardize a metric reporting template. Additionally, attendees will learn about the organization ’s current three year roadmap for an integrated strategy for enterprise data governance and stewardship. Attendees will learn about:
BJC HealthCare is one of the largest nonprofit health care organizations in the United States, delivering services to residents primarily in the greater St. Louis, southern Illinois and mid-Missouri regions. With 2013 net revenue of $4 billion, BJC serves urban, suburban and rural communities and includes 12 hospitals and multiple community health locations. Services include inpatient and outpatient care, primary care, community health and wellness, workplace health, home health, community mental health, rehabilitation, long-term care and hospice. Level of Audience |
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PANEL: Addressing Data Governance Challenges in the Financial Sector Moderator: Sunil Soares, Founder and Managing Partner, Information Asset, LLC Panelists: Lisa Brown, Director of Data Stewardship, Fannie Mae Robyn Lussier, VP, Capital Group Barbara Deemer, Chief Data Steward, Navient Harold Finkel, Managing Director, Business Data Management, TIAA-CREF This panel will address the unique challenges of the financial sector in implementing data governance programs. Topics include:
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