Watch the companion webinar at: http://embt.co/1r6IqZA Over the past 20 years, the role of data architects and modelers has changed significantly. Many initiatives are now business-driven as opposed to IT-driven, significantly changing the dynamics of solution delivery. This is compounded by complex environments consisting of a variety of solutions on disparate platforms. Corporate governance is also a growing concern, driving Data Governance, Data Quality and Master Data Management activities. Today's data architect must be prepared to address all these needs across business and IT. Join this session to learn about: - Team dynamics and changes in methodologies within IT - Defining an enterprise modeling strategy in a complex environment - Modeling techniques to help address these initiatives Learn more about ER/Studio at: http://embt.co/ER-Studio

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EMBARCADERO TECHNOLOGIES
Top 5 Data Architecture Challenges
Ron Huizenga
Product Manager –ER/Studio
Ron.Huizenga@embarcadero.com

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5 Challenges
•Evolution of methodologies & culture
•Adapting to changing architecture
•Complex data environments
•Data quality
•Business focus
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Evolution of Methodologies & Culture
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•Traditional/Waterfall (up to 1999)
–Big design up front
–Rigid organizational structure
•Agile –The Rebellion? (1996 –present)
–Incremental, time boxed delivery
–Self organizing team (who needs roles?)
•The Hybrid (200x? –present)
–Agile operational approach
–Leverage individual roles/skill sets
–Importance of architecture

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Agile Process
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ER/Studio –Agility Support
•True multi-level sub-models
•Repository
–Check-out/Check-in of sub-models
–Individual data objects
–Named versions
–Annotation
•Reverse Engineering
•Compare and Merge
•Naming standards capabilities
•Macros
•Collaboration & publishing
•Data interchange bridges
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Adapting to Changing Architecture
•Relational database
•Object oriented
•Unstructured data
•Document management
•Service oriented architecture
•Big data
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Service Oriented Architecture (SOA)
Publish & SubscribeFederated QueriesOperational Data StoreData WarehouseUnstructured Data & DocumentsMetadata RepositoryAuthentication& Security ServicesApplication ServerBusiness Process ManagementBusiness Rule ManagementComplex Event ProcessingLifecycle ManagementPolicy EnforcementBusiness Activity MonitoringAdaptersCommunicationsMessagingMetadata ManagementMaster Data ManagementTransformationCanonical ModelCanonical MessagingData IntegrationData FederationData Access ServicesDocument ServicesContent Management ServicesEnterprise Service BusMapping & TransformationBusiness Objects
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Big Data
•Volume
•Velocity
•Variety
•Veracity
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ER/Studio –Native Big Data Support
•MongoDB
–Versions 2.4 and 2.6
•Hadoop Hive
–Versions 0.12 and 0.13
•Capabilities
–Diagramming
–Reverse Engineering (JSON, BSON)
–DDL supported for Hive
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ER/Studio –Big Data Notation Enhancement
•Physical Model
–Objects instead of Tables
•Nested Objects
–“Is Contained In” relationship type
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Complex Data Environments
Evolution:
•38 years of construction
•147 builders
•No Blueprints
•No Planning
Result:
•7 stories
•65 doors to blank walls
•13 staircases abandoned
•24 skylights in floors
•160 rooms, 950 doors
•47 fireplaces, 17 chimneys
•Miles of hallways
•Secret passages in walls
•10,000 window panes (all bathrooms are fitted with windows)
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Complex Data Landscape
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•Proliferation of disparate systems
•ERP, mismatched departmental solutions
•SAAS (externally controlled and managed)
•Obsolete legacy systems
•Poor decommissioning strategy
•Point-to-point interfaces
•Data warehouse, data marts, ETL …

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ER/Studio –Conquering Landscape Complexity
•True multi-level sub-models (hierarchy)
•Reverse engineering (extensive list of platforms)
•Comprehensive metadata extensions (attachments)
•Naming standards capabilities
•Universal mappings
•Macros
•Data lineage
•Business Architect –data context (processes)
•Repository, collaboration & publishing
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ER/Studio –Compare and Merge example
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C&M cont.
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C&M cont.

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C&M cont.

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C&M cont.

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C&M cont.

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C&M cont.

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Data Quality
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•Accuracy
•Timeliness
•Completeness
•Consistency
•Relevance
•Fitness For Use

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Poor Data Quality Implications
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•Costs a typical company the equivalent of 15% to 20% of revenue
–Estimated by US Insurance Data Management Association
•Low Quality = Low Efficiency
•It is insidious –most data quality issues are hidden in day to day work
•From time to time, a small amount of bad data leads to a disaster of epic proportions

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Space Shuttle Challenger
QueCreek
Mine Flooding
When Data Flaws Happen…

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Poor data quality isn’t a new problem
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Mitigation Best Practice
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•Adopt the philosophy of prevention
•Show thought leadership
•Be accountable at the points of data creation
•Measure, control, improve
•Establish data culture

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Technique: Attachments (Metadata extensions)
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Business Focus
•Governance
•Champion for data value, data quality
•Consultative approach –business acumen
•Establish a data culture
•Business collaboration
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Addressing Governance through Models
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Data Governance
Data Architecture Management
Data Development
Database Operations Management
Data Security Management
Reference & Master Data Management
Data Warehousing & Business Intelligence Management
Document & Content Management
Metadata Management
Data Quality Management

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Data Management
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•Shared responsibility and collaboration
–Data Management Professionals
–Business Data Stewards
•Cultural change leadership
–Adopting the principles and practices of data management requires leadership from change agents at all levels

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•Powerful enterprise glossary, model & metadata collaboration
•Integrate key business terms and definitions with business systems
•View, store, and manage a single source of business definitions
•Attach business policies to daily workflows with contextual alerts and tips
Business Collaboration
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Concluding Remarks
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Thank you for attending!
•Learn more about the ER/Studio product family: http://www.embarcadero.com/data-modeling
•Trial Downloads: http://www.embarcadero.com/downloads
•To arrange a demo, please contact Embarcadero Sales: sales@embarcadero.com1 (888) 233-2224
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