Tutorials - September 29, 2014
| Monday September 29 8:00-4:30 |
Registration | ||||
| Monday September 29 8:00-9:00 |
Continental Breakfast | ||||
| 9:00 - 11:45 MORNING TUTORIALS | |||||
| Monday September 29 9:00-11:45
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T1: Beyond Compliance – Why Data Governance is Critical
for Operational Success, Not Just Regulatory Compliance Kelle O'Neal, Founder and CEO, First San Francisco Partners Many times a data governance program is initiated to address a regulatory requirement or a compliance program. However, just focusing on the compliance aspect of governance isn’t taking advantage of the full potential benefit. In order not to be treated like a project, governance needs to add value to multiple lines of business and across geographic boundaries. By extending the scope of governance beyond compliance, it can truly become a value-producing discipline in your institution. Using examples from the financial industry, we will review how to extend your data governance program beyond the compliance focus to achieve operational efficiencies and other improvements in your organization. We will look at what can be leveraged and re-used from a compliance-driven program, and some considerations of new capabilities to further embed governance into your operations. We will review:
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| Monday September 29 9:00-11:45
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T2: Sustainable Enterprise Data Strategies John Ladley, President, IMCue Solutions Enterprise data strategies are everywhere. In fact, a common issue is too many of them. Compliance, regulations, big data, analytics, MDM are all IM disciplines being considered by organizations, and all require cohesive data strategies But shades of data strategies have been proposed by IT for years. How can an organization develop a sustainable and useful enterprise data strategy without stigma of a cumbersome program? This tutorial session will cover the critical components and steps to develop a sustainable enterprise data strategy. Topics to be discussed include:
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| Monday September 29 9:00-11:45
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T3: Key Challenges in
Reference Data Management in Financial Services Malcolm Chisholm, AskGet Inc Reference data - particularly Customer and Financial Instrument - is the fuel that drives large areas of financial services. Underlying Reference Data Management (RDM) are key challenges that must be met, but which are often not addressed optimally. This tutorial describes these key challenges and approaches to meeting them. Some challenges are common to all classes of reference data, such as managing data vendors, working in a global context, and architecture. Others are more specific to particular classes, such as data stewardship for Customer on-boarding, and business rules management for Financial Instrument. The variety of these topics makes them more difficult to address in a holistic manner, but they can be fitted together into a framework, as the tutorial explains. Attendees will learn:
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| 12:00 - 12:30 DG SOLUTIONS | |||||
| Monday September 29 12:00–12:30
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Operational Data Governance Marty Loughlin, Vice President, Financial Services, Cambridge Semantics Inc It is very hard to combine Data Governance and operational data processes. The former faces the challenge of gathering governance information and establishing policies and procedures to keep the governance up-to-date and ensure that data standards are followed throughout the organization. The latter faces the challenge of meeting deadlines and cost targets, satisfying business requirements, and delivering high-quality results to their customers. Anzo Smart Data Integration lets you have the best of both worlds. You can achieve high-quality governance that directly drives efficient operational processes AND you can derive up-to-date metadata as a by-product of your data integration or data services projects. Level of Audience: |
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| Monday September 29 12:00–12:30
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LEI Integration & Governance Arka Mukherjee, Founder and CEO, Global IDs Financial Services Institutions around the globe are starting to integrate Legal Entity Identifiers (LEIs) into their systems environment. LEI’s are expected to provide the foundation for risk measurement & aggregation. This presentation describes how large banks have implemented LEIs using software automation. A step by step approach will be discussed. Level of Audience: |
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| Monday September 29 12:30–1:30 |
Lunch | ||||
| 1:30 - 4:15 AFTERNOON TUTORIALS | |||||
| Monday September 29 1:30–4:15
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T4: Progressive Data Stewardship in the Financial
Sector Robert S. Seiner, President/Publisher, KIK Consulting / TDAN.com Conventional wisdom tells us that Data Governance programs should focus on getting the Data Stewards to actively engage in the governing of data. This same wisdom often requires that individuals are assigned the task of being Data Stewards in addition to the responsibilities spelled out in their job description. The truth is that it does not have to happen that way. What if someone told you that there is a more progressive way to identify and recognize the data stewards in a financial organization? What if someone told you that there was a way to hold everybody with contact with "finance" data accountable for how they defined, produced and used data? This approach, presented in this tutorial with Bob Seiner, addresses financial data governance and the stewarding of data in a way that is practical and do-able in most financial organizations. This session will cover:
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| Monday September 29 1:30–4:15
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T5: Measuring the Value of a Data Governance Program in
a Financial Services Organization John Ladley, President, IMCue Solutions Kelle O'Neal, Founder and CEO, First San Francisco Partners One of the biggest challenges in implementing and sustaining a data governance program is determining the real impact the program has made to the organization - the ROI of the investment in governing data. It is relatively straightforward how to measure the progress of a data governance program in terms of identification of data accountability, creation of standards and policies, improvement in data quality. But how do we determine how all of this progress has improved the bottom line? This tutorial will review how to measure the impact of a data governance program to risk management, operational efficiency and cost reduction. Using Case Studies from Banking and Insurance companies, we will identify metrics that measure the impact a governance program provides to business processes within the organization such as loan on-boarding, regulatory reporting or premium calculation. We will review:
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| Monday September 29 1:30–4:15
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T6: How to Design, Develop, and Implement an Award Winning Enterprise DQ
Program Barbara Deemer, Chief Data Steward, Navient Michele Koch, Director, Enterprise Data Management, Navient In this tutorial, Michele Koch (DG Program Director) and Barbara Deemer (Chief Data Steward) will provide an overview of Navient ’s approach to designing and implementing an award winning enterprise DQ Program. Attendees will learn about their grass root tactics, the development of a DQ Cookbook and how they engaged their data stewards to become data approvers for the formal DQ Program. This tutorial will also provide a detailed, step-by-step account of Navient’s successful approach to developing business value calculations to quantify the impacts to generating revenue and avoiding costs. Topics that will be covered include:
Our hope is that our lessons learned with help others in this area since we know how difficult determining metrics can be. Level of Audience: |
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| Monday September 29 4:30–5:00 |
KEYNOTE: Evolving Goals and Architectures for Data Governance S. Ramakrishnan, Chief Data Officer, Citi Arka Mukherjee, Founder and CEO, Global IDs Data governance for financial firms needs to focus on delivering value – in the form of supporting a system of reliable and verifiable results and outputs. While the current emphasis in the industry is on understanding and corralling the data in the company, the effort needs to move forward to unifying the results, reporting and BI processes – in effect shifting the focus from “how we conduct business” to “how we manage the business”. This has implications for data representation, the internal and external constituencies that interact with results and the changing nature of disclosures. Level of Audience: |
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