A short "OUR"story Of Nearly Everything Data Not by Bill Bryson
Feb 16, 2017
A short "OUR"story
Of Nearly EverythingData
Not by Bill Bryson
Copyright © 2016 Dimension Data
Our world of Data is exploding with Digital Transformation technologies and platforms generating data.
Copyright © 2016 Dimension Data
Pillars: Data Governance, Data Architecture, Data Management.
The big challenge is to bring the data pillars together to ensure synergy and drive strategy.
The Goal: To align the Vision / Strategy, with Architecture, and how you will Manage & Enable and Govern all things Data.
In the future, it will become even more important for the data expert community to connect the dots between strategy,
architecture, data management and data governance.
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Business Divisions
Motor
Corporate
DirectAxis
MotoVantage
“Rest of Africa”
Rentworks
TFS
VW
Data Office
Our view of the Data Office
Information Office - CDO
Group CRO
FirstRand
Information / Data Governance
Information / Data Architecture
Information / Data Management
Program / Project - EPO
Data Management Practice
Shared Service Model / Grow Competency & Succession
PlanningWhenWho / What / How of Data
Capabilities (domains)Architect What / HowIndependent Governance of the What / How
Program Management
Project Management
Prioritization Process (incl. group projects)
Data Project Roadmaps
Administration and reporting
Change Management
Benefit Tracking in conjunction with Business
Cost tracking
Expectation Management
Business Value Management
IM & IG Report Services
Enterprise Data Capabilities (Domains):Data Development and Integration BI and Data WarehouseRef & Master Data ManagementData Quality ManagementMetadata Management Records, Document and Content Management Data Operations ManagementData Protection / Privacy / SecurityData Performance
Additional*:Enterprise Credit Data ModelInformation ClassificationDictionary Taxonomy
Information Level:StrategiesLandscapesArchitecturesFrameworksData and Information Management FrameworkMethod / ApproachesData InnovationFuture State InnovationStandards / Guidelines to support FSR PolicyStandards / Guidelines for EDMStandards / Guidelines to support Additional* Data ActivitiesData Level:Data ModellingData ArchitectureData AnalyticsBusiness Process ModelsAdditional:Key data sources identified and managedData Flow ownership
Principles, policy and standards
Ownership defined and allocated
Governance structures
Key data sources identified and managed
Material data elements defined
Governance around Data flows
Data Controls rolling up into the Information Governance Controls
Process:Data Management Practice Process
Data Services / Data Monetization
Data Management Roles and Responsibility Definition
Shared Services Model
Skills DevelopmentData Management Competency Framework
Succession planning
Data Culture Development
Promote Information Management Culture
Information Management and Governance Reporting Framework and Report ServicesTechnology Enablement – IT
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Vision/Strategy
The data strategy should link back to the following strategies; business strategy,
IT strategy and digital strategy. Governance, Risk and Compliance should
be represented, as well as the economy of data related strategic goals.
The industry trend is to setup a Data Office with a Data Office Strategy under the leadership
of a CDO - to support business strategies.
Executive buy-inSponsorshipLine of sight on strategiesTop-down / Bottom-upRoles & ResponsibilitiesBusiness Value Definition
Principles
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Architecture
The architecture should speak to the strategic objectives the company wants to achieve. The architecture should ensure that the economy of data, the governance and regulatory aspects are addressed with the same level of importance. Architect for Data Management and Business Value.
Capability Model - Unlock Business Value & Data Management
Architect for Regulatory and Governance
Architect for Business ValueAlign to Strategy
Architect with Future Trends in mind (future proof)Advisory & Guidance
Principles
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Data Management
Data Management relates to the actions being enabled through the
architecture layer.
The output and activities of Data Management should link back to
business value (the economy of data) or a governance / compliance benefit
that will be achieved.
Data Management FrameworkData Driven CultureData Capability RoadmapPeople - grow passion and knowledgeProcess - built in governance controlsTechnology - fit for purposeData Management for Governance
Principles
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Data QualityKPIs – applicable to all personnel that influence data
Linking quality to business impact – cost, complexity, operational, service delivery, reputational impact, legislative
Define quality dimensions, metrics and gateways
Process and mechanism for reporting on quality
Data policy clearly defining rules of the game
People – management control, training & guidance
Technology – automation of rules enforcement and workflow approvals
Data Processes
Organizational collaboration
KPI's definedLink Quality to business impact
Define Quality DimensionsProcess & Mechanisms for reporting
Principles
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Data Governance
Data Governance consists of 3 types of activities.
Guidance upfront during strategy definition and design.
Assistance during enablement and setup.
Controls to ensure that during the lifecycle we have implemented consistently.
OwnershipGovernance CouncilPolicies and StandardsData StewardshipData MaturityReporting and AnalyticsData Architecture
Principles
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Community of PracticeTo bring the pillars (Data Governance, Data Architecture, Data Management) together in a community of practice or a formal office where one can guide and drive alignment between the discipline areas.
This is a journey to bring the previous silo type thinking into a hive-mind type thinking culture.
Your vision and strategic focus will guide the community of practice or the data office.
Operating ModelEngagement Model
Service ModelCosting Model
Partnership ModelStrong Program and Project ManagementGovernance Models
Business & Regulatory Value Realisation
Principles
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Sun Hot
Earth Mild
Mars Cold
Jupiter Freezing
Data ThermometerOn a scale from Hot as the Sun to Jupiter Freezing, please reflect on the current state of your Data Environment.
Do you have clearly defined ownership?
What is your Data Maturity level within your Organization?
How would you rate your data quality?
Do you have a formal data governance practice in place?
How would you rate your data investment?
Thank
You