The document discusses the importance of data governance and provides an overview of how to implement an effective data governance program. It recommends obtaining executive sponsorship, aligning objectives to business initiatives, prioritizing initiatives, getting frameworks ready, and socializing the program. The document outlines data governance building blocks, including assessing maturity, developing a master plan, selecting tools, and establishing an organizational framework. It also discusses preparing an organization for success with data governance.

1. September 9, 2020
Fuel your Data-Driven
Ambitions with
Data Governance
Lisbon, September 8th 2020
Pedro Martins, Offering Lead Analytics & Data Management
DXC Technology Portugal
pedro.martins@dxc.com

2. September 9, 2020 2
Who Needs Data Governance?

3. September 9, 2020 3
Data Governance is Data Enablement

4. September 9, 2020 4
Organizations need a consistent, shareable,
accurate, trusted version of the truth
Data volumes continue to
increase in both size and
complexity.
Businesses advance from
"collecting" data for a single data
domain to "connecting" several
data domains.
Real-time analytics are a key
requirement for enterprises.
Enterprises are looking to master
data management as the
foundation with respect to real-
time visibility and decision
making
Risk management especially
when related to financial
misstatement, inadvertent
release of sensitive data
(security breaches) is a major
source of concern for
companies.
Digitalization serves as the single greatest factor in driving the growth of Data Governance.
The need for organizations to achieve ever-greater degrees of digitalization is creating a new dynamic.

5. September 9, 2020 5
The Impact of (No) Data Governance
How often do you hear these questions?
• We know where these values come from but who can cause them to change?
• There are columns of the same name of different types and lengths, who can tell me why?
• Who is responsible for this data set?
Common signals of lack of governance
• Does every data question result in a meeting of 5+ people?
• Is knowledge about business terms and underlying data models informal and rarely written down?
• Is there a declared data management strategy with shared understanding?

6. September 9, 2020 6
Data Governance Chaos
Have you seen this before?
Marketing
Manager
Business
Analyst
Business
Analyst
Business
Analyst
Data Gov
Manager
Data Gov
Manager
Business
Analyst
Business
Analyst
Data Gov
Manager
Marketing
Manager
Marketing
Manager
Finance
Manager
Marketing
Manager
Marketing
Manager
Marketing
Manager
How do we
define the
Client Life
Cycle Value
metric?
This is how we
define the
Client Life
Cycle metric.
A Client is
someone who
we have done
business with.
For us CLV is
calculated by
who writes the
report.
There is a
new project by
the Data
Governance
team to do this
There are
different Master
Data sources, we
will provide a
recommendation.
Resends the
definition to
the Business
Analyst.
Provides the
CLV value
based on the
prior formula.
I maintain my
recommendati
on sent in
Email 6.
I need a
‘certified‘
definition of
the metric.
Requests a
meeting of the
different
stakeholders.
Requests a
new initiative
to standardize
the concept.
Requests a
meeting with
Data Gov
Manager.
Sends a detailed
definition of CLV
to Marketing
Manager.
Requests
alignment
between new
concept and
previous ones.

7. September 9, 2020 7
75% of respondents say innacurate data
undermines their ability to provide an
excellent customer experience.
Source: Gartner, 2018
https://www.gartner.com/smarterwithgartner/how-to-create-a-business-case-for-
data-quality-improvement/

8. September 9, 2020 8
Implementing Data Governance –
The First 100 Days

9. September 9, 2020 9
Analysis Implementation
1
2 3
4
5
6
9
10
Understand and document processes
Current operations and policies
Understand and document the
Technical components and
current functional and
business requirements
Jointly explore and prioritize
capabilities
business and techniques
Policies, ... guides
Business processes
CR & Controls
Training
Iterative implantation
0
Definition of scope
systems under study
Define future capacity
Establish the GAP between the
two
Maturity models
Data architectures
Informational strategy
7
Set the roadmap
DG program
DG initiatives
Metrics & Goals
Resource Plan
Change Management Plan
Communications plan
Training Plan
Transition Plan
8
Planning
Roadmap
Envision
Discovery
Planning
Design/Build/Test/Deploy
Run
Starting your Data Governance Journey with a Vision

10. September 9, 2020 10
Data Governance Building Blocks
Advisable to start with a single business area and then expand
Tool Description Deliverables
Maturity Assessment • Plan and run workshops, interviews, and surveys Assess
current data governance maturity across the enterprise
• Identify the target level of data governance maturity
• Gap analysis
• Maturity assessment
• Gap analysis
Master plan • Establish the Business Vision
• Gather data management needs
• Assess the current state of data governance capabilities
• Establish roadmap of the organizational master plan
• Data governance strategy
• Needs assessment
• Master plan
• Proyect Plan
• Executive Summary
Tool Selection • Identify data governance requirements
• Establish selection criteria
• Evaluate tool candidates based on the functional
scorecard
• Identify short list of suppliers
• Weighted selection criteria
• RFI / RFP
• Evaluation results / recommendations

11. September 9, 2020 11
Data Governance Maturity Assessment Example
Mapping Current State to Future State
Organizational Structures and
Awareness
Stewardship
Policy
Architecture
Data Quality Management
Classification and Metadata
Information Life Cycle
Management
Audit, Logging, and Reporting
Initial Managed Defined
Quantitatively
Managed
Optimizing
Scope of services
Assess current state Determine future state Identify required capabilities
and initiatives
Develop roadmaps

12. September 9, 2020 12
Business Impact
Executive Sponsor
DG Organization
Ownership Accountability
Owners Stewards Champions Users
Organization &
Governance
Process &
Controls
Data
Architecture
Technology
Architecture
Data
Maintenance
Data Quality
Monitoring
Issue
Management
Change
Control
Process
Process
Policies Data Standards
Workflow and Controls
Data Transformation & Synchronization
People Process/Workflow Documents Framework ThemesLegend
• Data Governance Council – Formal Council
to oversee Data Governance Program
• DG Organization – Dedicated organization is
established to administrate program
• Ownership & Accountability – Data Owners
and Stewards are held accountable for
correctness, stability and certainty of data
• Data Maintenance – Data Maintenance
processes are integrated & coordinated
• Data Quality Monitoring – Data Quality is
monitored within & between applications
• Issue Management – Issues are tracked &
managed through prioritization
• Change Control – Changes to data structures
are managed through a structured process
• Policies – Policies are developed/enforced &
compliance is tested
• Data Standards – Data Standards are
mandated & compliance tested
• Workflow and Controls – Workflow is
automated and key controls are enforced
• Data Transformation & Synchronization –
Data is keyed once and synchronized through
the enterprise
DXC Data Governance Organization Framework

13. September 9, 2020 13
Prepare your organization for Success
1. Obtain top management sponsorship and attention.
2. Align data governance objectives to key business transformation initiatives.
3. Prioritize initiatives. Start immediately and start small by identifying a limited set of good candidates (teams
or business processes) that generate business impact.
4. Get the underlying framework ready for use - systems, workflows, processes, communication channels,
documentation, templates, forums/committees and anything that is required to 'do the work’.
5. Socialize with different segments of stakeholders through appropriate communication channels -
workshops, emails, trainings, etc. - once the framework is ready.
6. Leverage the learnings along the way to improve the operating model. However good the model looks on
paper, it should be validated and refined before a broader roll-out.

14. September 9, 2020 14September 9, 2020 14
Thank you!
Learn more | www.dxc.technology/analytics

15. September 9, 2020
Fuel your Data-Driven
Ambitions with
Data Governance
September 8th 2020
Pedro Martins, Offering Lead Analytics & Data Management
DXC Technology Portugal
pedro.martins@dxc.com

16. September 9, 2020 23
Disciplinas a cualificar
Disciplina Descripción
Personas
Estructura organizativa involucrada en el Gobierno de Datos. Asegura la gestión de las políticas, estándares, procesos,
catálogo… Facilita el cambio cultural en la organización
Procesos
Engloba la gestión de todos los procesos: gestión de políticas, definición de términos y responsabilidades,
monitorización y reporting de los mismos
Tecnología
Diseño de la arquitectura, sistemas y aplicaciones de datos estructurados y no estructurados que permite la
disponibilidad y distribución de datos a los usuarios adecuados.
Data Pipeline
Optimizar el aprovisionamiento interno y externo, la integración y la distribución de datos para satisfacer los requisitos
del negocio y gestionarlos de manera consistente.
Master Data Management
Gestionar los datos esenciales y críticos para el negocio sobre clientes, productos, materiales, proveedores y cuentas,
The System of Record (SOR).
Catálogo de Datos
Soporte para un entendimiento común de términos y definiciones sobre datos estructurados y no estructurados que
respaldan los procesos de negocio empresariales
Metadata Management
Gestión metadatos en relación a los métodos y herramientas utilizados para crear definiciones semánticas comunes
para términos comerciales y de TI, modelos de datos y repositorios.
Linaje de datos & Análisis de
Impacto
Entender las relaciones cruzadas de los datos nos permite analizar el linaje y el análisis de impacto entre los artefactos
de datos
Calidad de datos Gestión calidad de los datos en cuanto a métodos, métrica, mejoras y certificación de calidad de los datos
Ciclo de Vida de los Datos Gestión del ciclo de vida de los datos sistemática: creación, uso, retención, borrado
Data Protection Políticas, prácticas y controles utilizados para mitigar el riesgo y proteger los activos de datos.