2. 2Information Classification: General
Dan Power joined State Street Global Markets as
Managing Director and Head of Data Governance in
March 2017.
He’s an expert in data governance, data quality and
master data management (MDM).
Prior to joining State Street, he was the principal
consultant for Hub Designs, where he focused on strategy
development, solution delivery and thought leadership.
Dan’s clients at Hub Designs included Apple, AIG, CIT
Group, LPL Financial, LexisNexis, Dun & Bradstreet, Red
Hat, and EMC.
Dan is frequently an invited speaker at technology
conferences, and has written more than 35 articles and
white papers on information management.
Dan has twenty-plus years of experience in enterprise
software, management consulting, strategic alliances,
marketing, corporate strategy, project management and
entrepreneurship at companies like Dun & Bradstreet,
Deloitte, and CSC Consulting.
He studied Computer Science at Princeton University and
has a B.S. in Entrepreneurial Studies from Babson College
in Wellesley, MA.
Dan Power
Managing Director and
Head of Data Governance,
State Street Global Markets
About Dan Power
3. 3Information Classification: General
About State Street
State Street Corporation is one of the world's leading
providers of financial services to institutional investors,
including investment servicing, investment management
and investment research and trading.
With $32.9 trillion in assets under custody and
administration and $2.95 trillion in assets under
management (as of Sept. 30, 2019), State Street operates
globally in more than 100 geographic markets and employs
approximately 39,000 people worldwide.
For more information, visit State Street's website at
www.statestreet.com.
4. 4Information Classification: General
Agenda
Rethinking your data quality strategy for the cloud 5
Moving to the cloud is an opportunity 7
• ensure the quality and lineage of your data
• implement automated governance models
Cloud management is fundamentally different from
traditional ecosystems
8
• how data and analytics leaders should address these
differences
Exploring best practices 9
Building a collaborative framework 10
Conclusion 11
Questions & Contact Info. 12
5. 5Information Classification: General
Rethinking your data quality strategy for the cloud
Benefits
• Easier scaling of services
• Cost efficiency - can significantly
reduce infrastructure costs
• Unlimited storage, backup and
recovery
• Reduced bottlenecks, faster
deployment, improved performance
• Some cloud vendors also provide
good built-in analytics tools
Challenges
• Migrating to the cloud can cause
formatting problems or data losses
• Rapidly increasing volumes make it
harder to maintain quality & usability
• Frequent changes by the cloud
provider can cause quality issues
• Data security, privacy, operational
and legal concerns; “data lock-in”
• Data governance is a key
component for any enterprise cloud
Moving applications & data to the cloud brings benefits, but also some challenges for data quality
6. 6Information Classification: General
Rethinking your data quality strategy for the cloud
Potential Solutions
• Start by making sure your data
strategy aligns with and supports
your overall business strategy
• Don’t try to avoid the cloud
completely
• Probably need to plan for multiple
cloud environments eventually
• Pay close attention to data quality,
lineage, data security and privacy
• Same basic practices apply: govern
data; improve quality; and manage
metadata (including lineage), master
data and reference data
• Build automated data quality
measures into your cloud migration
• Minimize the number of separate
cloud migrations
• Plan to archive / delete data when
its no longer needed
Approaches to addressing those challenges
7. 7Information Classification: General
Use this as a tollgate to improve the quality & lineage of your data
• Various vendors can help you improve data quality and map
your data’s lineage as you migrate it into the cloud
• DQ tools with cloud capabilities; cloud integration tools;
and machine learning data catalogs
• Need to track lineage within the cloud as well
Automated governance models, for both cloud and on premise:
• Data governance framework (policies & standards)
• Data governance platform (lineage & metadata management)
• Data quality tool (rules to enforce accuracy, completeness,
timeliness, and consistency – ACT+C)
• Automated lineage discovery
Moving to the cloud is an opportunity
8. 8Information Classification: General
• Plan from the beginning to manage the cloud environment(s)
• Will probably need a multi-cloud management platform
• Able to handle Amazon, Microsoft, and Google, smaller
providers like Oracle, IBM, etc. (and private clouds)
• Data governance for the cloud
• Helpful paper: Data Governance Taxonomy: Cloud versus
Non-Cloud
• More complex: involves more stakeholders; will require
changes to your current data governance approach
• Still very much an evolving discipline
Cloud management is fundamentally different from traditional ecosystems
How data and analytics leaders should address these differences
9. 9Information Classification: General
• Make sure your current data governance approach is sound
• Address people, organizational bodies, and culture;
policies, processes, and standards; technology for data
governance, data quality and master data management
• You’ll have to adapt & strengthen it during your cloud migration
• More complex environment, with more parties involved
• Increased importance of security, privacy, legal,
accountability, interoperability and service level agreements
• Look at your migration to the cloud as an opportunity to
strengthen your enterprise’s approach to data governance,
data quality and metadata management / data lineage
Exploring best practices
Optimize data as its migrated to ensure you are maximizing its potential
10. 10Information Classification: General
Building a collaborative framework
For analytics users, data collection specialists, and the data governance team
• Giving different groups their own ways to work with the data
• Analytics users: facilitating self-service BI, based on an
enterprise data catalog and curated, certified data sets
• Data collection specialists: using integration & automation
to build secure data pipelines to deliver the right
information to the right consumers at the right time
• Data governance team: extending the current approach to
handle setting strategy and policies to govern data well
across multiple internal and cloud-based environments
• Challenging: an ongoing process, but relying on multiple cloud
providers makes it critical to get this right
• Has to become just part of “how we do things here”
11. 11Information Classification: General
Migrating to the cloud is a great time to re-examine data
governance & data quality strategies
Hold the enterprise to a higher standard in order to
improve the quality & lineage of your data
Cloud management & governance is more complex than
what’s required for a traditional on-premise approach
Strengthen your current strategy for data governance,
quality and lineage as part of your cloud migration
Take this as an opportunity to leapfrog competitors, build
competitive advantage, and deliver digital transformation,
based on new data assets that are trustworthy and usable
Conclusion
