Innovation, Data Quality and Compliance

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Live events generate huge excitement for passionate fans. From an NBA Finals Game to the latest Broadway show to multi-day music festivals, chances are everyone has logged into Ticketmaster to buy tickets for a must-attend event. During large event sales, Ticketmaster becomes one of the top 3 largest eCommerce websites globally at the point of sale, which creates huge challenges from a data perspective. John Carnahan, Chief Data Officer for Ticketmaster has been analyzing user and sales data to build a platform of data as an asset to support the business. John will shed light on two high priority areas for the business and how his team is using machine learning and data science to add value to both the business and customer experience.
Find out:
• Behind the scenes insights on ticket sale abuse prevention and how to use data to make predictions on behavior to respond accordingly
• How to pivot to solve a known data challenge from the customer perspective to better serve fans
• Ways to identify new opportunities to add value to the business and customers through data activities

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After setting the roadmap, launching the program, establishing stewardship and working on data quality, how do you maintain your program at a steady pace? What happens when you have a stable first year and something happens to affect the second year roadmap? Two data leaders will sit down to compare notes and share lessons learned on the journey.
Executives will explore:
• Addressing culture change as you progress the program
• Complying with constantly shifting regulations, coupled with internal and external demands
• Keeping up with goals and working on value added activities

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After building out sound data governance and data quality systems, how can you leverage quality data to drive decisions? How can you ensure that analytics are reliable, lineage is traceable and that you can prove to the government that information hasn’t been tampered with? How can you proactively look for new opportunities using unstructured data?
Find out:
• Where to manage analytics and how to apply the solution to address business needs
• How to deliver more robust self service capabilities to business users
• Keep up with the growing appetite for analytics from different lines of the business

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Nearly two years ago, Michael Gardner was brought in to lead BNY Mellon’s Innovation Center in Silicon Valley with the aim to apply Silicon Valley technology to financial services. The center is aligned with the company’s overall strategy to use emerging and disruptive technologies as the core of it’s transformative digital enterprise strategy, to transform customer experiences, and to create a new digital ecosystem in financial services.
Find out:
• Critical capabilities to accomplish enterprise innovation
• Techniques for digitizing customer experiences, and streamlining operations

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The global financial crisis stimulated the need for a common financial language. The industry is actively collaborating to cure these problems by building a free, reusable and open source common financial language. FIBO is becoming transformational for both regulators and companies to align on a common language and vocabulary that is expressive. After ticking the boxes for BCBS239, what else can FIBO support?
Get up-to-date on:
• The various operational capabilities that the industry will benefit from by committing to FIBO overtime
• How FIBO can provide a searchable and interactive multi-dimensional view of data and application for various use cases
• FIBO as a unified scaffold to develop future data models without reinventing the wheel

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Fixing data quality is the plumbing of a huge skyscraper, it cannot be fixed overnight. After building in metadata, terms and definitions, the next phase is a strong framework around data quality that can be continuously monitored.
Find out how to:
• Develop metrics to measure and prove data quality while reducing redundancy
• Quickly get up the data quality curve if you’re behind
• Balance data quality activities with new opportunities, including advanced analytics

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#1 Leveraging Predictive and Prescriptive Analytics to Drive Decision Making
Marcelo Labre, Head of Quant Analytics, Market Data and Counterparty Credit Analytics, OCBC Bank

#2 Developing a Business Glossary and Related Metadata
Gidget Delos Reyes, Vice President, Enterprise Data Steward, MUFG Union Bank, N.A.

#3 The FIBO Value Chain, Exploring New Operational Capabilities Beyond Regulatory Compliance
David Newman, Strategic Planning Manager, Senior Vice President, Innovation Group, Wells Fargo

#4 Bringing Value to the Business Through Data Management Activities

#5 Evaluating the Risks and Leveraging the Scalability of the Cloud
CP Singh, Managing Principle, CPSI Consulting

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The Data Capability Assessment Model (DCAM) is a standard way for organizations to view, assess and measure the progress of their data management program. The DCAM spells out the capabilities and sub-capabilities that describe what is needed to establish and sustain an effective data management program – an infrastructure and best practice that is critical to ensure that data, fit for purpose, is delivered in a timely manner to critical business functions.

In this panel session, listen to the practitioners who have utilized the DCAM model to anchor their data management program. Learn about the standardized model, why it is important to use, and the value it can provide. Learn how the DCAM model has been mapped to the principals of BCBS 239 and how this disciplined approach can help firms prepare for their data management regulatory examinations.

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One of the great benefits to prioritizing data governance and data quality is having clean data to take advantage of new computational opportunities, including machine learning. Machines have an ability to discover patterns that humans may not find without expert training. But such patterns may also include spurious noise due to fundamental data errors. The need for data quality becomes especially acute when non-linear machine learning techniques are used. With clean data and appropriate interpretation of the patterns, machine learning techniques have the potential to improve investment decision making across the business.
Find out:
• How data management teams can begin to make use of machine intelligence
• How machine intelligence can help with the recognition and interpretation of data patterns.
• How to take advantage of recent advances in deep learning

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Hear from the discussion facilitators what the key learning outcomes were from the task forces and return to the office prepared and ready for tackling the challenges that await.