Fostering an Enterprise Data Mindset

Transcription

1 Fostering an Enterprise Data Mindset Hand-Engineering Data Revolution Rick Kochhar, PhD SVP & CDO, TD Bank Group

2 Transformational Growth TD 2002 $18.9 billion Market Cap 1,328 Business Locations Retail, Business, Wealth 42,817 Employees As at October 31,

3 Transformational Growth TD 2014 $105 billion Market Cap 2,470 Business Locations Retail, Business, Wealth 85,000+ Employees *As of July 31,

4 Snapshot of the U.S. The U.S. market is a very different environment Approximately 7,200 banks compared to Canada's 'big five' Complex regulatory demands with unique challenges (e.g. Dodd Frank) TD's US operations: Auto Finance, Retail Banking, Wealth, & Securities More stores (branches) in the US than in Canada Unique data-driven culture required Market Share of Top 5 Banks 85%+ 44% 4

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6 Why should we care about data? The path to gaining a competitive advantage is based on having a complete understanding of our customers' needs. We re not far from meeting those expectations; we just need to get the data right! The 360 view is no longer the source of sustainable advantage but rather, a matter of competitive survival. Getting the data right requires us to change the way we think and compels us to tap the data before we act. Today s competitive laser hologram is D: our customers expect us to have an intimate knowledge of their interactions with us, and to anticipate their needs both for today and beyond. This means recognizing and investing in the intrinsic value of our data, and transforming ourselves from quarterly thinkers to restless yet patient seekers. 6

7 What challenges are we facing today? Data is Not Viewed as an Enterprise Strategic Asset Data is viewed and treated as an extension of technology and not as a strategic asset No Enterprise View of Data Lineage It is difficult to trace the flow of data and identify data sources to support re-use No Formal Data Quality Processes or Goals Enterprise policies for data quality are non-existent and data quality is not a business priority Fragmented Data Governance Data governance is not formalized at the enterprise level and data policy / standard enforcement is limited and disparate Non-Standardized Reporting Reporting is non-standard, lacks functionality, and is dispersed across multiple teams; Integration for enterprise reporting requires extensive manual intervention Data Collection, Storage, Quality and Aggregation are Siloed Business data storage and access is siloed across disparate systems, creating redundancies and duplication 7

8 Outcomes of using right data Reduce regulatory burden Cost to comply & sustain Basel II AMA, A-IRB FATCA BCBS 239 DODD FRANK (398) CCAR Improve customer experience Integration across LOBs Up/cross sell Customer retention Customer acquisition Customer service Increased wallet share Improve operating efficiencies Cost to manage data Reduce duplication Reduce reconciliation Improve data quality Actually perform analytics vs. scrub data Improve decision making Unlock hidden value of data Common definitions across LOBs Right data at right time Improved business insights Metadata Reference data Master data RUN OUR BUSINESS BETTER! 8

9 Where do we start? 9

10 Align to Guiding Principles 10

11 How do we keep the data wheel round? Reporting and Analytics Metadata Management Model Management Enterprise Data Data Governance Reference / Market / Master/ External Data Management Data Quality Delivering legendary customer experiences, operating with excellence, understanding our business and mitigating risk Data Protection Office of the CDO Data Storage Data Integration 11

12 What elements are foundational? 1 Data Governance Developed policies and standards Established data governance processes Defined roles and responsibilities Operationalized bank-wide data governance structures Identified data assets under management Supported use of tools & technology 2 Data Quality Defined stewardship and decision rights Developed policies and standards Established data quality processes Deployed data quality metrics and reporting Supported use of tools & technology 12

13 How to start a data revolution Establish the Office of the CDO to provide leadership on data management Establish data governance and stewardship within Business Segments & Corporate Functions Establish processes to enhance data quality Establish and rollout an Enterprise Data Strategy Standardize commonly used data and identify solutions to more easily share data Build data-driven communities and enhance capabilities 13