Mastering Data Strategy and Data Quality to Remain Competitive

Just how well do you know Boston? Brush up on your fun facts in preparation for Trivia Tuesday! Get to know your fellow attendees at your table during this early morning quiz. Nobody likes a cheater, so put the laptops, tablets and smart phones away. The winning table will start the morning off with mimosas!

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Interested in demonstrating thought leadership as Chairperson? To find out how to chair FIMA, contact meg.mcneel@wbresearch.com

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Implementing a bank-wide data management program to service an organization 85,000 employees strong requires full buy-in and unwavering commitment on all levels. Invoking change management principles and taking a multi-channel approach to mobilize the enterprise is fundamental to TD Bank Group's Office of the Chief Data Officer (OCDO) communication and engagement strategy.
Driving business value is not only critical to effective data management, articulating it is also fundamental to a advancing the data story. To demonstrate this impact, the team developed a causality chain mapping the bank’s guiding principles to those data points identified as leading or lagging indicators and depicting any direct correlation to the bank's overall financial performance.
Join us and hear from Glenda Crisp, Senior Vice President and Chief Data Officer, and Mary Munro, Vice President, Data Governance and Communications where they will share key successes and lessons learned to date, including:
• Developing and launching an intranet portal enabling data stewards to access program information supporting the Bank's Enterprise Data Strategy comprising of Data Governance, Data Quality and other capabilities
• Leveraging a value causality chain to monitor business value and contribute to the 'bottom line' story
• Leading a successful storytelling campaign in 2016 resulting in increased global employee awareness, engagement, and program execution
• Galvanizing the bank's data stewardship community through dedicated relationship management and guidance supporting the organization's data management program deployment

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The only product financial institutions have is data and everything the company does is in support of data. Now that many have built programs in compliance with regulations, how is the office of the CDO helping to support the broader business beyond box checking? How do you position services that are universally understandable and for people to consume them? How is the data management function aligning with strategic growth efforts? What is the CDO expected to produce or be as a strategic business partner? What actions provide the most benefit to the firm? What is the future of the CDO?
Hear from those in the Office of the CDO weigh in on:
• How programs are organizing to manage data between technology and the business
• What the data management function can deliver outside of the business from an innovation perspective
• How to keep messages clear and crisp and ensure the executive team understands how you’re changing outcomes

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Huge investments are being made in infrastructure to meet regulations and many are now looking to leverage the investments made in technology for compliance to deliver additional value. What does that look like? What are some of the challenges that must be addressed when it comes to being viewed as a competitive capability versus a cost center? How can you use your infrastructure to engage a wider audience? How can you provide value back to the investment division and consumers? Hear from two Heads of Data Management on how they are addressing this next level approach, including:
• The governance frameworks in place as an enabler
• How to articulate data services so that it is viewed as an enabler and to support innovation.
• Linking this service offering to increased digitization and mobility

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Data leaders are challenged with the ability to cope with constant change while balancing current regulatory requirements and maintaining sustainable growth. It’s a given that the global regulatory environment can be characterized by change and 2016 brought about major political events that will further impact future regulations, from Brexit to the new Administration in the US. How can you adapt quickly to change while still having good visibility of risk exposure? How do you account for transparency of data? How will data lineage be used in the future and by consumers? Panelists will discuss data lineage strategies to help data management organizations remain nimble and transparent, including:
• Automation and traceability throughout the project lifecycle
• Determining levels that are important, what each level provides and how to present it
• Maintaining, gathering and keeping data lineage up-to-date and verifiable

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Most financial firms regularly spend a substantial amount of time and money installing and improving their Enterprise Data Management (EDM) systems. However, many of the tasks that they perform are repetitive in nature. Find out how a single source of truth system that leverages supervised machine learning can enable data governance teams to do things cheaper and faster, saving on average, 25% of time in installing and running EDM systems.
Learn deeper insights into supervised machine learning, including:
• Machine learning based data quality and business rule engine
• Persistent data structures for source tables and fact tables
• SQL-HTML based live manual that uses machine learning to provide Decision Support

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Across the industry firms are spending huge sums individually and utilities offer the opportunity for rationalization across firms. The appetite for utilities is on the rise and asset managers are beginning to look to the opportunities following the pioneering investment banks. What have we learned so far? Are utilities a way to band together to find rationalizations in the face of disruptions? Who bears the burden of risk and cost if there is a mistake? What is happening on the outsourcing side?
Panelists will discuss:
• Projections on savings and improvements to be made
• How to make sure you have the right technology in place that’s scaleable
• Overview of barriers to entry with utilities and how the model will become a reality

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Legacy systems continue to be problematic for the financial industry, and the problem is unlikely to go away easily. This challenge has become even more convoluted with the increase in regulations, including Basel III, MiFID II and more. To further complicate matters, the digital revolution is quickly sweeping the industry and banks are looking to modernize platforms to take hold of new capabilities, such as robotic process automation (RPA). Preetham Kamesh will unpack all of these topics and more to help you:
• Understand the technology landscape and new opportunities
• Update and layer on new technologies to legacy systems while remaining compliant
• Take hold of new technologies to remove manual procedures and increase quality

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Blockchain is the ultimate lineage tool, which has tremendous implications for data management and data quality. Now that big banks, industry consortia and even public groups have been actively working on Blockchain for over year, where does the industry stand? What are the short- and long-term goals? How financially stable are innovations in payment systems, including Blockchain? How does Blockchain integrate with other standards if at all? How can you integrate external parties in a trusted manner? What does the path to global regulatory compliance look like for payments through Blockchain? What other use cases make sense?
Panelists will discuss:
• What kinds of data and business operations make sense for Blockchain 

• Outcomes of proof of concept tests 

• New problems and security issues with everything in distributed ledger

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Given the investments in data quality, analytics are becoming of much greater value. Regulations and risk are central to designing systems for performing analytics, but how can you develop systems that will fit your needs from the ground up? How are financial institutions building out infrastructure and processes to provide access to information relevant to users of all skill levels from basic users to analytics power users? How are organizations building services and drawing value from that? How do you get the data to people to support those kinds of services?
Find out:
• Where information is coming from (internally or from clients) and how data is being handled
• What do good analytics look like versus qualitative analytics
• How to determine how deep you want the service and different levels to offer

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Since the earlier part of the day was jam-packed with detailed presentations and thought-provoking panel discussions, here’s your opportunity to be part of the discussion! Sit with many of the day’s speakers to ask questions about their presentations, share your thoughts, and dive deeper into your challenges. Additional roundtable topics not yet discussed in the earlier sessions are available too. You pick which roundtables to partake in – choose the ones relevant to your most pressing initiatives and send your team mates to others to cover full ground. Each Roundtable discussion lasts 30 minutes – choose 2!

1. Impact On Risk Management and Regulatory Compliance From New Cloud Investments
Mike Pickart, Senior Manager, Financial Services Solution Architecture, Informatica, LLC.
2. Fighting Governance Fatigue by Keeping Metrics Meaningful
Seth Earley, Chief Executive Officer, Earley Information Science
3. Driving and Implementing Workflow Tools to Improve Data Quality
Susan McIvor, Vice President, Nomura Securities
4. Big Data and Analytics: Overcoming Challenges in the Institutions
Luis Estrada, Global Chief Executive Officer, BI Geek
5. Overcoming the Innovation Challenge of Driving Advanced Analytics
Geoff Finnie, Director, Strategic Analytics, BMO Financial Group
6. Delivering Data Quality Across the Investment Process
Brent Knapp, Director, Bloomberg PolarLake Buy-Side Solutions, Bloomberg PolarLake
7. Legacy Meets Regulation and Digital
Preetham Kamesh, Vice President, Global Head of IDMS, Capgemini

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Data governance approaches vary widely across the industry, with different functional groups getting involved in stewardship and governance. What considerations should be made when integrating different groups? How do you overcome challenges when it comes to people, projects, technology and location? What’s the recipe for success? Andy Steele, Global Head of Reference Data Operations & Technology for Barclays integrated operations and technology to advance data governance. Hear lessons learned from his program, including:
• The approaches used to integrate
• Assessing organizational readiness and managing friction
• Results for year one and next steps

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