Masterclass: The CDO’s Playbook - Building a Successful Data Team for

Offense and Defense

Derek Strauss

Chief Data Officer

TD Ameritrade

1

DATA

ANALYTICS

Show me the Data! We are the Hippo Slayers!

No more decisions based on

HIPPO

Highest paid person’s opinion

2

Building a Data & Analytics Capability

Why and Why Now?

3

Data Management Mission Map

Manage Data & Information Resources to Provide: 1. Operational Effectiveness & Efficiencies from increasingly governed, quality controlled and

non-redundant application, interface, and stored data for existing and new solutions. 2. Sustained Competitive Advantage from High Integrity Reporting & Analytics 3. Reduced Enterprise Risk due to End to End Data Lifecycle Architected Controls 4. Change enabling Data and Process Management Capabilities throughout the Enterprise

Goal 1 Operational Data

Management

Goal 2 Competitive Analytic

Advantage

Goal 3 Controlled End to End

Data Risk

Operating Efficiencies, Improved Effectiveness,

High Value Solutions

Advanced, Timely, Quality, & Insightful

Reporting and Analytics

Reduced Reputational and Regulatory Risk from End

to End Data

Mission

Measures & Requirements

Measure

Requirement

Prerequisite

Efficiencies & Productivities

Operational Data Arch & Mgt.

Standard Methods & Skills

Confidence Indicators, Effectiveness ratings

Timely, Budgetary Delivery

Leverage of enterprise services and assets

4

Goals

Business Outcomes

Goal 4 Change Enabled

Adaptive Enterprise

Highly adaptive business and technical organizations

with advanced data capabilities

Data Contols Effectiveness Ratings

End to End Architected Controls & Monitoring

Data Lifecycle Mgt, Ent Data Architecture

Cost to change, Cost Prevention

Enterprise Data Mgt. Capabilities

Engaged Executive Sponsorship

Data & Analytics – why now? Why is this Important? Supports Key Initiatives

Providing an “integrated view” of enterprise data, which will be accessible, secure, described and well understood Governance processes will result in improved data and information quality Reducing reputational and regulatory risk through implementing end to end data controls

Personalization capabilities facilitating client intimacy Advanced timely, quality data and insightful reporting and analytics Reduced data redundancy means lower costs of storage Reduced data inconsistencies means improved effectiveness and facilitation of high value solutions (consistent, accessible data) Improved operations and strategic decision making, e.g. AML initiatives, Analytics for web, social media platforms; New Business

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Data & Analytics – emerging trends • Fixing the basics. To satisfy the thirst for extending Business

Intelligence and Analytics, it is essential to build a strong information foundation. Key elements are Master Data Management (with Customer Master Data and Security Master Data as the obvious top priorities), Data Quality improvements, Information Lifecycle Management and robust BI platforms.

• Social Media are hot - They can supply essential information about customers’ opinions. Combined with the actual customer behavior as captured in transactional systems this is a wealth of information

• Agile Analytics requires (real- or right time) insights into increasingly complex questions

• Do IT yourself - More and more Information users are taking over tasks that traditionally were the field of the IT developers

• Performance is key word. Look for technology solutions like appliances, in memory analytics, columnar storage

• Up in the cloud. Cloud or As A Service models are in increased

demand for both temporary as well as permanent usage. It’s all about services (like reporting or analytics) provided from a managed environment based on a (new) business model (often pay per use), making intelligence available via the internet.

• Google fast, Apple easy, Amazon intimate . Just like at home, business users are expecting an engine that searches all available data (structured and unstructured, internal and external) to quickly find answers. Navigating through the results to find patterns, trends could be improved with advanced visualizations. The corporate BI App Store is just around the corner

• Analytics Appliances come in pre-configured packages offering ease of installation, simplified operations and anticipated easier maintenance. Using commodity hardware and open source software allows vendors to aggressively price offerings & lower the total cost of ownership (TCO)

• Need for speed. Even though the size of data is increasing the

Analytics user is expecting faster answers from their Analytics environment. In memory technology (as opposed to separate disk storage) allows for new business usage

• Big data gets bigger and bigger. The rise in volume (amount of data), velocity (speed of data) and variety (range of data) gives way to new architectures that no longer only collect and store but actually use data

• Let’s go mobile - Analytics users want to access their data anytime, anywhere

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Data & Analytics – key initiatives (page 1 of 2)

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Goal Key Initiative Emerging Trends Game Changers

O

per

atio

nal

D

ata

Man

agem

ent

A Better Security Master A Better Customer/Account Master Other Authoritative Data Hubs /

Stores (all with governance in place)

Fixing the basics Need for speed Social Media are hot

Single security master will improve efficiency and reduce risk

Rapid analysis of large data sets Integration of structured, semi-structured

and unstructured data gives true 360 degree view of Customer

Co

mp

etit

ive

An

alyt

ic

Ad

van

tage

Demand Management – a better process for managing and prioritizing information demand and requirements; 18 month Cycle Plan (quarterly re-cast)

Analytics Patterns - Next generation analytics, architectures and methods

First Enterprise Analytics Delivery – with governance in place

Agility is the new normal

Do IT yourself Google fast, Apple

easy, Amazon intimate

Let’s go mobile Analytics Appliances

Faster delivery of analytic capability - Analytics when you need it

Providing specialized environments for exploration and mining of data by the end user community

Customer-centric data model provides foundation for customer intimacy

Mobile capabilities not only for internal Analytics but also for reports that can be rendered direct to our customers

Lower entry level pricing

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Goal Key Initiative Emerging Trends

Game Changers

Co

ntr

olle

d

End

-to

-En

d

Dat

a R

isk

Incremental cleansing of back office systems data Ongoing Data Quality Improvement - Enterprise Data Officers

define, control, monitor and improve Enterprise Data

Fixing the basics Reduced reputational and regulatory risk from end to end data controls

Ch

ange

En

able

d A

dap

tive

En

terp

rise

Data Governance Role Clarity, Adoption and Competency Building – CDO Organization, Business Owners & IT Custodians

Data Roadmap – where we are and where we are going to year by year

Data Architecture Patterns – “benevolent dictatorship”; enabling us to be Google fast, Apple easy and Amazon intimate

Data Governance Toolset – e.g. Metadata Repository and DQ Dashboard

Data Architecture Standards and Methods Expanded Data Playbook Adoption – repeatable process,

supported by automation paired with training Expansion of Data Governance Toolset – Master Data

Management, Workflow, etc

Big data Performance is

key word Up in the cloud

Advanced data capabilities facilitate rapid integration of disparate types of data

Improved cost to run and cost to change operational data and BI capabilities

Data & Analytics – key initiatives (page 2 of 2)

7 Streams Playbook

Using Accelerators to Launch your Data & Analytics Program

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7 Streams for Strategic Data Management

• Establish Data Governance Council & executive sponsorship • Define Office of the CDO functions, org. structure, roles & PSP’s • Develop & implement Data Stewardship process

• Confirm Data Reference Architecture 3.0 (including Big Data) • Establish Data Modeling process & manage Data Models • Integrate Data Architecture with Enterprise Architecture

• Publish Data Asset development methodology • Iteratively plan, design, develop & deliver Corporate Data Assets • Monitor & improve performance of the Corporate Data Assets

• Profile, map & cleanse critical Data Assets • Develop Data Quality rules and reporting tools • Continuously monitor & improve data quality

• Develop & obtain agreement on Business Glossary • Implement & integrate technical metadata repository • Provide data lineage for all types of data (structured, Big Data)

• Support implementation of BI/Advanced Analytics tools • Assist in development and support predictive modeling & analytics • Leverage Data Visualization techniques to enhance user experience

• Manage Information Lifecycle of the Corporate Data Assets • Manage platform implementation (Cloud, network, OS, DBMS, etc.) • Monitor & improve performance of all infrastructure components

Data Architecture

Data Asset Development

Data Quality

Data Context

Analytics

Infrastructure

Data Governance

2

3

4

5

6

7

1

10

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7 Streams Process Flow - PEMGO

1. Plan 2. Execute 3. Monitor 4. Grow 5. Optimize

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2.Data Architecture

3.Data Asset Development

4.Data Quality

5.Data Context

6.Analytics

7.Infrastructure

1.Data Governance

• Establish Data Governance Council & executive sponsorship • Define Office of the CDO functions, org. structure, roles & PSP’s • Develop & implement Data Stewardship process

• Confirm Data Reference Architecture 3.0 (including Big Data) • Establish Data Modeling process & manage Data Models • Integrate Data Architecture with Enterprise Architecture

• Publish Data Asset development methodology • Iteratively plan, design, develop & deliver Corporate Data Assets • Monitor & improve performance of the Corporate Data Assets

• Profile, map & cleanse critical Data Assets • Develop Data Quality rules and reporting tools • Continuously monitor & improve data quality

• Develop & obtain agreement on Business Glossary • Implement & integrate technical metadata repository • Provide data lineage for all types of data (structured, Big Data)

• Support implementation of BI/Advanced Analytics tools • Assist in development and support predictive modeling & analytics • Leverage Data Visualization techniques to enhance user experience

• Manage Information Lifecycle of the Corporate Data Assets • Manage platform implementation (Cloud, network, OS, DBMS, etc.) • Monitor & improve performance of all infrastructure components

1. Plan 2. Execute 3. Monitor 4. Grow 5. Optimize

Seven Streams Roadmap

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1.1.01 Identify & manage enterprise data demand 1.1.02 Define & publish Data Governance charter 1.1.03 Identify & engage data stakeholders 1.1.04 Establish Data Governance operating model 1.1.05 Identify & appoint Data Stewards 1.1.06 Publish data policies, standards & procedures 1.1.07 Define & publish key business terms 1.1.08 Review & approve Critical Data Elements (CDEs)

1.2.01 Manage Data Governance activities & services 1.2.02 Facilitate & attend data stakeholders meetings 1.2.03 Manage & resolve data issues 1.2.04 Review & approve CDE changes 1.2.05 Sponsor Data Quality management 1.2.06 Sponsor Metadata management 1.2.07 Communicate & promote value of enterprise data management

1.3.01 Monitor Data Governance performance 1.3.02 Identify opportunities for improvement 1.3.03 Monitor compliance with data policies, standards & procedures 1.3.04 Gather & analyze data stakeholders feedback 1.3.05 Measure & report Data Governance effectiveness

1.4.01 Expand scope of Data Governance products & services 1.4.02 Design Data Governance process improvements 1.4.03 Extend Data Governance support to more data stakeholders 1.4.04 Map efficiency opportunities to data programs 1.4.05 Identify tangible business benefits resulting from Data Governance

1.5.01 implement Data Governance improvements 1.5.02 Meet & exceed data stakeholders needs & expectations 1.5.03 Communicate & promote tangible business benefits due to Data Governance

2.1.01 Define Data Architecture strategy 2.1.02 Develop & publish Enterprise Data Model (EDM) 2.1.03 Align EDM with other business models 2.1.04 Review Reference Data Architecture 2.1.05 Develop Target State Data Architecture & its components

2.2.01 Maintain Enterprise Data Model 2.2.02 Maintain Enterprise Conceptual Data Model (CDM) 2.2.03 Develop & maintain Enterprise Logical Data Models 2.2.04 Manage Enterprise Data Architecture

2.3.01 Validate Target State Data Architecture 2.3.02 Map Target State architectural components 2.3.03 Monitor Data Architecture management performance 2.3.04 Validate information requirements

2.4.01 Enhance Enterprise Data Model 2.4.02 Publish data modeling standards and notations 2.4.03 Establish data model management process 2.4.04 Incorporate data artifacts in the Architecture Review Board process

2.5.01 Implement Target State Data Architecture & its components 2.5.02 Integrate Data Architecture and Enterprise Architecture 2.5.03 Communicate & promote value of Data Architecture

3.1.01 Confirm business case & scope of data asset development 3.1.02 Plan data asset development project(s) 3.1.03 Analyze information requirements 3.1.04 Develop logical data models 3.1.05 Draft high-level solution design

3.2.01 Develop detail design specifications 3.2.02 Define & publish test plan 3.2.03 Build data asset solution 3.2.04 Test developed data asset 3.2.05 Migrate & convert data 3.2.06 Deploy data asset solution

3.3.01 Validate information requirements 3.3.02 Review data model & DB design quality 3.3.03 Review development process 3.3.04 Monitor data asset effectiveness 3.3.05 Gather user feedback

3.4.01 Conduct post-implementation review 3.4.02 Manage change requests log 3.4.03 Review data asset development approach 3.4.04 Identify improvement opportunities

3.5.01 Implement development process improvements 3.5.02 Apply agile development approach

4.1.01 Define Data Quality Strategy 4.1.02 Profile data 4.1.03 Select critical data elements (CDEs) 4.1.04 Define data quality rules 4.1.05 Define threshold levels for CDEs

4.2.01 Control CDE versions 4.2.02 Map CDE’s to System of Record (SOR) 4.2.03 Apply data quality rules to CDE’s

4.3.01 Implement data quality reporting tools 4.3.02 Report data quality results 4.3.03 Monitor data quality of CDE’s 4.3.04 Analyze data quality issues 4.3.05 Monitor data quality management process 4.3.06 Measure data quality improvements

4.4.01 Extend data quality management to more CDE’s 4.4.02 Distribute data quality results to all data stakeholders 4.4.03 Identify business inefficiencies due to poor data quality

4.5.01 Remediate identified data quality issues 4.5.02 Develop transformational and cleansing rules 4.5.03 Identify & apply controls to prevent bad data 4.5.04 Measure tangible business benefits due to improved data quality

5.1.01 Analyze & document metadata needs 5.1.02 Define Metadata Strategy 5.1.03 Develop Metadata Architecture 5.1.04 Establish standards & procedures 5.1.05 Define Metadata management process 5.1.06 Define & publish Business Glossary 5.1.07 Support & publish CDEs 5.1.08 Create & promote Metadata Wiki

5.2.01 Select & implement Metadata Repository (MDR) 5.2.02 Administer Business Glossary 5.2.03 Moderate Metadata Wiki 5.2.04 Gather & integrate metadata 5.2.05 Support MD reporting & analytics 5.2.06 Promote metadata use by data stakeholders

5.3.01 Monitor Metadata management processes 5.3.02 Evaluate Metadata Repository performance 5.3.03 Analyze Metadata management effectiveness

5.4.01 Query, report & analyze metadata 5.4.02 Distribute & deliver metadata 5.4.03 Automate metadata collection & integration 5.4.04 Include metadata for unstructured content & big data 5.4.05 Identify metadata usage improvement opportunities

5.5.01 Leverage metadata reporting & analytics 5.5.02 Optimize Metadata Wiki use 5.5.03 Analyze metadata to consolidate data assets 5.5.04 Measure & report tangible business benefits due to metadata use

6.1.01 Analyze business analytics needs 6.1.02 Define analytics delivery approach & architecture 6.1.03 Select analytical tools 6.1.04 Plan analytics deployment

6.2.01 Implement selected analytical tools 6.2.02 Educate analytics stakeholders 6.2.03 Roll-out analytical solution

6.3.01 Review analytics solution performance 6.3.02 Gather user feedback 6.3.03 Monitor & support analytics

6.4.01 Extend analytical solution 6.4.02 Manage analytics change request log 6.4.03 Identify opportunities for improving analytical solutions

6.5.01 Implement improvement opportunities 6.5.02 Roll-out self-service analytical tools

7.1.01 Define infrastructure requirements 7.1.02 Define data security requirements 7.1.03 Plan infrastructure implementation 7.1.04 Develop & publish business continuity & disaster recovery plan

7.2.01 Obtain infrastructure resources 7.2.02 Deploy infrastructure components 7.2.03 Integrate infrastructure components into corporate technology architecture

7.3.01 Monitor performance of the infrastructure components 7.3.02 Review compatibility of the infrastructure components 7.3.03 Validate data security requirements

7.4.01 Enhance infrastructure components 7.4.02 Identify opportunities for improving infrastructure performance

7.5.01 Implement identified improvement opportunities 7.5.02 Optimize infrastructure components 7.5.03 Ensure effective integration of the infrastructure components into corporate technology architecture

2.Data Architecture

3.Data Asset Development

4.Data Quality

5.Data Context

6.Analytics

7.Infrastructure

1.Data Governance

1. Plan 2. Execute 3. Monitor 4. Grow 5. Optimize

Seven Streams Roadmap

Gavroshe Proprietary and Confidential 14

1. Plan 2. Execute 3. Monitor 4. Grow 5.

Optimize 7 Streams – play card layout

4.1.01 Define Data Quality strategy

4.1.01 Define Data Quality Strategy 4.1.02 Profile selected data 4.1.03 Select critical data elements (CDE’s) 4.1.04 Define data quality rules 4.1.05 Define threshold levels for CDE’s

Objectives: Data quality is an integral part of the enterprise data governance program. Effective data quality program requires serious commitment from all stakeholders starting with the definition and approval of the data quality strategy that should be communicated to all parties involved. Data quality strategy should define the business objectives that can be achieved by improving the quality of enterprise data. It should position data quality within the data governance program and should describe the relationships that data quality has with other activities within data governance programs, such data modeling, data stewardship, metadata management, etc.

Description: Data Quality Strategy document must present all aspects of the data strategy in a clear manner, especially the data quality management process that will be implemented as part of the data governance program. Below is a possible table of contents of a Data Quality Strategy document: Data Quality Strategy Table of Contents (provisional) :

1. Executive Summary 2. Data Quality definitions

2.1 Cost of poor data quality 2.2 Challenges of Data Quality 2.3 Data Quality & Data Governance 2.4 Data Quality dimensions

3. Data Quality Management program 3.1 Objectives 3.2. Data Quality Management process 3.3 Data Quality Management - current state 3.4 Data Quality Management - future state

4. Data Quality Management architecture 5. Data Quality Management road map

5.1 Implementation schedule 5.2 Milestones & deliverables 5.3 Resource requirements

6. Risk management

Inputs/ dependencies:

Templates/examples: • Data Quality Strategy

template

Outputs/outcomes: • Data Quality Strategy

document

4.Data Quality

List of all plays in this phase of

the work stream

Work stream for this play

Phase for this play

Play’s name and numeric code, where the first digit represents the work stream (1 thru 7), the second digit represents a phase (1 thru 5) and a sequential

number of the play within this stream and this phase, for example 4.1.01 means first play in the Planning phase for the Data Quality work stream

Objectives and description of

the play

Detailed description &

activities of the play

Links to sample documents or templates that this play might need to create the

required outputs Names of required

outputs or outcomes

RACI: CDO A Data Owner R Director, EDG C Lead Data Steward R DG Consultant I Data Steward I Data Custodian I

Play’s RASCI (Responsible, Accountable,

Consulted and Informed) Any prerequisites or

inputs for the play

Gavroshe Proprietary and Confidential 15

1. Plan 2. Execute 3. Monitor 4. Grow 5.

Optimize 7 Streams – play card example

1.Data Governance

1.2.01 Manage Data Governance activities & services

1.2.01 Manage Data Governance activities & services 1.2.02 Facilitate & attend data stakeholders meetings 1.2.03 Manage & resolve data issues 1.2.04 Review & approve CDE changes 1.2.05 Sponsor Data Quality management 1.2.06 Sponsor Metadata management 1.2.07 Communicate & promote value of enterprise data management

Objectives: The two main areas of functionality of the Data Governance team can be summarized thus:

• Governance of the enterprise data and support of the data stakeholders, and • Product management for Data Services provided by the Data Governance team

The principal responsibility of the data governance is to organize and support the necessary information about the enterprise data assets that help the data stakeholders make informed and effective decisions based on them.

Description: Data Governance activities & services need to be defined in the Data Governance charter. The operating model of Data Governance program typically (variations of the Data Governance operating model may be appropriate for a particular business organization) contains the following levels of responsibility of the data governance team and the data stakeholders: Teams Data stakeholders Data Governance team Data Governance Data Owners Chief Lead Data Steward (CDO) Council Data Stewardship Lead Data Stewards Director, Enterprise Data Governance Committee Subject Area Data Data Stewards Data Governance Consultants Working Groups Detailed definition of the Data Governance teams and individual roles can be found on the next page.

Inputs/dependencies: • Subject scope from

completed opportunity scoping template

• Defined business terms • Business data glossaries or

other business metadata sources, other application or reporting specification

• Business reports, dashboards and statements

Templates/examples: • Critical data element

definition template

Outputs/outcomes: • Business data catalog • De-duplicated list of data

elements with instructional metadata

• Data ownership assigned and signoff of definitions

• Business data catalog updated, stakeholders informed of the update

RACI: CDO A Data Owner R Director, EDG C Lead Data Steward R DG Consultant I Data Steward I Data Custodian I

Exec Sponsors

Data Owners Council

ENTERPRISE Data Governance/Stewardship Model Level 3

Ente

rpri

se D

ata

Ow

ner

ship

an

d

Pro

gram

Go

vern

ance

Responsibilities

• Establish strategic vision and corporate guidelines

• Executive sponsorship & support

• Provide client experience impact and perspective due to data quality issues

• Prioritization for enterprise projects related to data

• Resolve issues that are escalated by Officers & Stewards

• Signoff key Stewardship Deliverables

• Operationalize corporate data stewardship procedures

• Participate in data stewardship discussions and standardization

• Business definitions & data quality specifications

• Establish corporate data quality metrics and measurement guiding principles

• Maintain and communicate performance against established metrics and measurements

Level 4 – each of the Data Officers will have Data Stewards from their organization who will

manage and drive data quality in their respective business units.

Le

ve

l 2

L

eve

l 1

…. BU #1 BU #3 …… …… BU #2 BU #n

Ente

rpri

se D

ata

Off

icer

s &

Ste

war

ds

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Data Stewards Versus Data Owners

Data Officers & Stewards (Judiciary & Enforcement)

Data Owners (Legislature)

1. Defines and advises on standards and policies

2. Documents BU and Enterprise data related initiatives/efforts and passes these to Data Owners for prioritization

3. Manage Data & Analytics demand

4. Defines performance measures to help determine how well the data governance effort is working

5. Develops standard operating procedures for the storage, retention and disposal of corporate information

6. Ensures quality, availability, understandability, accessibility, security and distribution of information

1. Ratifies and drives standards and policies

2. Identify and resolve opportunities and issues

3. Review cost/benefits analysis and business case

4. Prioritize opportunities and make investment decisions; ensure Enterprise thinking versus silo thinking drives prioritization

5. Support standard operating procedures for the storage, retention and disposal of corporate information

6. Secures on-going funding

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Data and Information Quality Improvement Role of the Business in the Data Life-cycle

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Data Stewardship – involvement in the Data Life Cycle

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Data Officers & Stewards – detailed responsibilities

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Balancing your Team

Right Mix of Technological Expertise and Business Knowledge

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CDO Organizational Components Fu

nct

ion

al F

ocu

s

Define, develop, test and support deployment of competitive analytics approaches, methods and solutions for all enterprise clients. Partner with Data Resource Development (Data Dev) and App Dev on development, deployment and production support. Develop research & development partnerships to continue advancements.

Competitive Analytics Center

A technically centered persistent function of the CDO that re-architects and re-engineers enterprise data assets (ODS, DW, DM, EW, ...) to leverage DW 2.0 and 7 Streams while expanding and re-training key technical resources.

Leverage Data Architecture & Data Administration provided 7 Stream Data Services & Methods for Data Development. Re-tool enteprise data assets, services and skills.

Re-integrate enteprise data assets and data dev skilled workers into IT integrated development organization.

Data Resource Development Center

A technically centered persistent function of the CDO that defines and deploys 7 Streams based DRM Capabilities and DW 2.0 competencies that integrate with Business Data Governance to provide Enterprise Data Services

Provide 7 Streams Architecture and Methods for data administration with training and support.

Partner with Data Resource Development Center to deploy data administration and integrate with Data Development

Develop Data Porfolio Management approach and performance monitoring.

Data Architecture & Data Administration

Define & Deploy Data Stewardship, Governance, Quality and Cataloguing across business areas leveraging data services from Data Administration

Deliver Data Academy Curriculum with Rich Media

Develop and Deploy Demand Management for Data Services, Issues and Iniatives across the busines

Data Governance & Demand Management

Re

spo

nsi

bili

ties

A business centered persistent function of the CDO that establishes, trains and supports Data Officers and Stewards who Define, Control, Monitor & Improve business data and manage the business demand, prioritization and funding for data intensive initiatives and issues. Provides inputs for Data Portfolio Planning and Management

These centers collaborate with CTO and App Dev to define Data Dev approaches and methods and apply them to re-tooling key data assets and managing the data portfolio.

A persistent function of the CDO that operates as a blended business and technology area providing next generation analytics, data science and supporting architectures and methods (e.g. Data Visualization, Mobility & Virtualization, Big Data Streaming Analytics, ...) based upon CDO vision and support.

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CDO Organization – Data Governance & Demand Mgt

Enterprise Data Officers (Centralized/Virtual

Team)

Finance

HR

Operations

Retail Institutional

Risk &

Compliance

Marketing

Marketing Data Stewards Marketing

Data Officer

Risk & Compliance Data Stewards

Institutional Data Stewards

Retail Data Stewards

Finance Data Stewards

HR Data Stewards

Operations Data Stewards

Risk Data Officer

Institutional Data Officer

Retail Data Officer

Ops Data Officer

HR Data Officer

Finance Data Officer

Enterprise Data Officers define, control, monitor and improve Enterprise Data as well as manage demand for Enterprise Information; they are supported by Data Stewards in their local Functions /Business Units, who define, control, monitor and improve Local Data as

well as manage demand for Local Information. 23

CDO Organization – Competitive Analytics Center

Enterprise Analysts (Centralized/Virtual

Team)

Marketing Analytics Team Marketing

Lead Analyst

Risk & Compliance Analytics Team

Institutional Analytics Team

Retail Analytics Team

Finance Analytics Team

HR Analytics Team

Operations Analytics Team

Risk Lead Analyst

Institutional Lead Analyst

Retail Lead Analyst

Ops Lead Analyst

HR Lead Analyst

Finance Lead Analyst

Enterprise Analysts and Data Scientists use Enterprise Data for exploration and data mining; in addition they perform R & D with leading edge methods, tools and techniques and promote use of enterprise information assets and tools for analytics; they are supported by

Analytics Teams in their local Functions /Business Units, who provide exploration and data mining services to meet local needs.

Finance

HR

Operations

Retail Institutional

Risk &

Compliance

Marketing

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CDO Organization – Data Architecture and Data Administration

Data Architects/Administrators

(Centralized Team)

Data Officers & Data Stewards

Analytics Teams

Data Resource Development

Business Analysts

Application Developers

Production Support

Data Architects and Data Administrators form a centralized team of data specialists who provide data architecture, data standards and data methods to App Dev, the CIOs and the CTO Organization; secondly, data services (data modeling, metadata, data quality, etc.) are

supplied to the business community via the data officers/stewards and analytics teams; thirdly, through partnering with the Data Resource Development Center, architectural direction is set for re-engineering existing data assets and for new initiatives, e.g. MDM.

Finance

HR

Operations

Retail

Institutional

Risk &

Compliance

Marketing

25

CDO Organization – Data Resource Development Center

Data Solution Architects (Centralized Team)

Other Application

Development Teams

Data Warehouse Team

Data Hub Developers

A centralized team of Data Solution Architects collaborates with Application Development teams to re-architect and re-engineer enterprise data assets (ODS, DW, DM, ...) to leverage Data Architecture patterns, standards and methods, while expanding and re-

training key technical resources.

Data Architects & Data Administrators

Business Analysts

Data Officers & Data Stewards

Analytics Teams

Finance

HR

Operations

Retail

Institutional

Risk &

Compliance

Marketing

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Data & Analytics Capability Map

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Governance and oversight

Awareness and engagement

Services Analytics

Messaging & Education

Adoption promotion

Demand capture

Service support

Product / Service Management

Demand mgmt.

Program mgmt.

Service deployment

Work effort mgmt.

Quality assurance

Talent mgmt. PMO Finance Contract mgmt.

Strategic planning

IT Risk &

compliance

Organization alignment

Platform Engineering Technology capability demand

Rapid prototyping

Platform architecture

Platform engineering

Platform deployment

Data

Data governance

Data stewardship

Data custody Data

provisioning

Usage / service

monitoring

Data

Analytics

Data Integration

Information Delivery

Turn Key Analytics

Advanced Analytics

Data Sciences Information

Delivery

Data for Analytics

Analytics

Big Data

Self-Service

Data Modeling

DaaS

Quality Data

Metadata

Master Data

Data Governance

Data Architecture

Metadata mgmt.

Data quality Canonical

data modeling

Logical data modeling

Physical data modeling

Needs assessment

Analytics

Data Governance

Data Architecture

Data Delivery

Organization Alignment

PM

O

PM

O

PM

O

PM

O

PM

O

Team Skills for Offense and Defense

Ensuring appropriate depth and breadth

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Bu

siness A

nalyst

Data Stew

ard/SM

E A

nalytics Lead

s

Data A

rchitect

Data M

ovem

ent Sp

ecialist

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DATA

ANALYTICS

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