Preliminary Conference Agenda

• 1. Panel Discussion: Identifying Key Ways To Measure Bad Data And Revenue Lost In Order To Fund Future Data Quality Projects
  
  • Identifying root causes of bad quality data
    
  • Examining ways to measure the value of bad data in $$
    
  • Effectively assessing the cost of correcting bad data
    
  • Calculating risk from bad data and the cost to the business
    
  • Establish and sharing KPI’s with other business units
    
  • Leveraging data as a business enabler and the need for greater investment

• 2. Enhancing Operational Efficiency By Establishing An Automated Data Quality Reporting Framework
  
  • Achieving the infrastructure needed to create a centralised data quality reporting framework
    
  • The importance of defining the metrics before beginning the measurement
    
  • How to decide the frequency of reporting: quality vs. timeliness?
    
  • Overcoming the complexity of report processing
    
  • Governance and ownership: who owns and repairs your bad data?

• 3. Reference Data Quality As A Business Enabler: Linking Greater Quality With Revenue Enhancement
  
  • Allocating profit and loss to data quality
    
  • Policy vs. Execution: Measuring data in line with business objectives
    
  • Using data quality and metrics as a tool to identify where further investment is needed as well as where success has been achieved
    
  • Using quality to reinforce value add of data departments to the business

• 4. Panel Discussion: The Future Of Market Data: Positioning Your Business For Success In The Changing Global Data Landscape
  
  • Forecasting the impact of rising data volumes & latency demands on the market data landscape
    
  • Examining the future of market data technology: What are the most promising developments on the horizon?
    
  • Can we expect to see further consolidation in the vendor market, and what are the consequences likely to be for innovation and market data pricing?
    
  • Understanding how the emergence of alternative trading venues is likely to impact volumes & latency concerns

• 5. Optimizing Data Management Through Strategic Alignment Of Business, IT And Operations
  
• Identifying where data efficiency breaks down
  
• Examining the data supply chain: where can inefficiencies be identified across business lines?
  
• Overcoming challenges of working with IT when forming data policy
  
• Effective strategies for developing data policy with operations
  
• Harmonised approaches and the move towards an enterprise wide data budget

• 6. Keynote Presentation: Creating An Enterprise-Wide Data Strategy
  Focusing on controlling the scope of your project
  
• Setting the right architecture into place
  
• Choosing and working with an integration partner
  
• Identifying which systems are setting priority
  
• Understanding the full cost of developing a connection using internal resources vs. what is available off the shelf

• 7. Creating A Customer-Centric Data Model That Meets Regulatory Requirements And Provides The Right Data, At The Right Time, For The Right Users
  
• Examining a critical first step towards enterprise-wide data accuracy: data capture
  
• Fostering a collaborative environment between business and IT to create a joint model at inception
  
• Focusing on producing data in a format that people can use
  
• 1. What do people want?
• 2. How do they want to see it?
• 3. Why do they want to see it?
• 4. Where do they want to see it?
• Taking a proactive approach to understanding current and future data requirements and building the necessary functionality into your architecture
  
• Building in service level agreements to ensure data quality from supplier to end user

• 8. Panel Discussion: Strategies For Collecting And Maintaining Global Legal Entity Data That Meets Your Risk And Compliance Standards
  
  • Identifying a trustable and definitive source for accurate counterparty data
    
  • Working within your existing infrastructure
    
    • Integrating counterparty data into your systems, file formats and processes
      
  • What is the right model for counterparty data accuracy?
    
    • Do you buy the data?
      
    • Do you rent it?
      
    • How often do you update it?
      
    • What is the technology needed to support it?
      
  • Examining how to secure a 3rd party for counterparty data and steps for implementing them into your systems
    
  • Determining the optimal operating model once you’ve gone live

• 9. Panel Discussion: Evaluating The Critical Issues Associated With Counterparty Reference Data Integration
  
• Determining the optimal strategy for integrating and maintaining usable counterparty reference data
  
• Is your data usable and able to be cross-referenced from a reporting and process standpoint?
  
  • If not, what does it need?
    
• Creating processes to effectively integrate vendor products into your data operation
  
• Figuring out how to interoperate with your vendors as they ship data to and from the institution
  
• Finding ways to verify that the data streams you are getting are valid and cleansed upon entry to your system
  
• Determining if the quality of your data is restricting automation
  
• Identifying and overcoming the problems associated with integrating data with your legacy systems

• 10. Improving The Automation Of Reference Data Management To More Accurately Manage Risk While Cutting Costs And Improving The Customer Experience
  
  • Utilizing reference data management to address competitive pressures arising from globalization, risk, regulatory demands and lower margins
    
  • Uncovering the impediments to creating a single view of the customer
    
    • Managing multiple counterparty data feeds
      
    • Cross indexing the external feeds with internal account and transaction data
      
    • Reconciling conflicts to form the golden copy
      
    • Assessing how to distribute the information across the organization
      
  • Evaluating master data hubs to connect reference data with accounts and transactions to improve profitability by offering a unified view of the customer
    
  • Assessing the criticality of counterparty data accuracy to manage credit risk, client profitability and overall operational efficiency

• 11. Establishing Data Quality Initiatives To Improve Reporting & Compliance
  
  • Understanding how regulations affect data applications
    
  • Creating an efficient data infrastructure that supports operational efficiency and regulatory reporting
    
    • Proving best execution
      
    • Verifying your transactions
      
    • Managing your risk
      
    • Providing transparent audit trails
  • Meeting emerging regulatory requirements without compromising data quality and integrity
    
    • Abstaining from diverting resources for short-term solution

• 12. Focusing On Data Integration As A Key Step Towards Enterprise Data Management
  
• Evaluating a system integration firm that is right for your organization
  
• Examining where to start on your integration project
  
  • Distinguishing between consolidation and integration
    
  • What level of work must go into the project?
    
  • What budget will you need to work with?
    
• Managing your legacy architecture with an appropriate migration path
  
  • Utilizing enterprise services
    
  • Leveraging message translation technology
    
  • Feeding your downstream systems
    
• Getting to a level where you can utilize your EDM system and leverage data throughout your operation

• 13. Panel Discussion: Overcoming The Key Challenges Of Cleaning & Integrating Legacy Data
  
• Identifying the best ways to measure the quality of your historical data
  
• Should inaccurate legacy data be cleaned and corrected?
  
• Strategies to ensure access to historical data is integrated and centralized
  
• Using legacy data as a benchmark for measuring the success of current data quality
  
• Regulatory reporting: fulfilling the need for greater management of historical data for customized client reporting

• 14. Streamlining Your End-To-End Trade Lifecycle By Efficiently Managing Your Product Identifiers
  
• Overcoming the challenge of maintaining multiple security master files
  
• Determining whether a particular identifier is capable of supporting your entire processing chain
  
• Examining obstacles to the implementation of identifiers
  
  • Identifying and understanding complex transactions
    
  • Utilizing multiple data sources to manage all transactions
    
• Exploring the increased demand for more sophisticated types of data identifiers

• 15. Examining The Contractual Issues Of Market Data Feeds To Determine The Proper Licensing And Ownership Model
  
• Identifying the restrictions on your feeds: Can they be used in the front, middle and back offices?
  
• Examining what modifications need to be taken to data before it’s considered proprietary to you
  
• Determining the limits to data replication within your organization
  
  • Ensuring compliance with data distribution requirements through security and tracking of vendor data
    
• Analyzing the future business model for vendor data feeds

• 16. How To Establish An Effective Framework For Service Level Agreements To Improve Data Management Across Your Business
  
• Identifying the need for SLA’s across your business and the need to hold staff accountable in the current regulatory climate
  
• Evaluating where best to originate SLA’s:
  
  • Business
    
  • Operations
    
  • Technology
    
• Establishing regular SLA reviews, metrics and amendments
  
• Implementing a minimum standard of data quality and the advantages to your business
  
• Overcoming the complexities of managing SLA’s and examining the infrastructure for internal reporting

• 17. Panel Discussion: Overcoming Data Management Challenges Relating To New Product Variations – OTC Derivatives And The Need For A More Flexible Data Strategy
  
• Meeting the growth in derivatives trading and the challenges to data managers
  
• Evaluating variants in approach for differing OTC derivatives:
  
• Credit derivatives
  
  • Interest rate derivatives
    
  • Equity derivatives
    
  • Foreign exchange
    
  • Commodity derivatives
    
• Identifying key processes need to effectively manage OTC derivatives data storage and distribution
  
• Centralization and business ownership: Effective strategies for vertical expansion from horizontal models

• 18. Sourcing Accurate And Consistent Corporate Actions Data For Operational Efficiency
  
  • Assessing why the sourcing of corporate actions data continues to be a challenge
    
  • Determining the impact of corporate actions integration on product security masters and the issuers of securities
    
    • What are the true implications of poorly sourced corporate actions data?
      
  • Taking steps to align the strategies of the vendor community and custodians to source better corporate actions data and increase efficiency
    

• 19. Panel Discussion: Understanding Your Risk And Compliance Application Requirements
  
  • Gaining an understanding of the importance of the data that supports risk and compliance
    
    • Why is this data important and why do you need to get it right?
      
    • How is the data being used?
      
    • What is wrong with the data they are currently getting?
      
  • Exploring the benefits of having clean, organized and accessible data for risk and compliance
    
    • Meeting your KYC/AML requirements
      
    • Running your risk models
      
  • Reinforcing the business case in terms of why institutions should be investing time and money into cleaning their counterparty information