Data Strategy Development Training Course

Participants can identify and prioritise data opportunities that support business objectives.

• Business capability maps connecting strategic objectives, processes, information and decisions
• OKRs translated into data-enabled use cases and measurable value hypotheses
• Use-case portfolio scoring by value, feasibility, risk and time-to-benefit
• Benefits dependency networks linking data products to operational performance measures
• Value-stream analysis identifies decisions constrained by unavailable or unreliable data
• Investment cases separate recurring operating costs from one-time change costs

Participants can assess data maturity and define evidence-based target capability requirements.

• Current-state assessment across people, process, data, technology and governance
• DAMA-DMBOK knowledge areas used to structure discovery and evidence collection
• Data maturity model scoring with defined evidence, scales and calibration
• Interviews, document reviews and data profiling triangulate maturity findings
• Gap analysis distinguishes capability weaknesses from isolated delivery issues
• Target-state statements define measurable capability outcomes and design constraints

Participants can design governance roles, decision rights and control mechanisms for priority data domains.

• Data governance operating model: councils, domains, roles and escalation paths
• RACI matrices assign accountability for critical data elements and decisions
• Data owners, stewards, custodians and consumers define distinct accountabilities
• Decision rights cover standards, access approvals, quality exceptions and retention
• Policy hierarchy links principles, policies, standards, procedures and controls
• Federated governance balances enterprise consistency with domain delivery autonomy

Participants can define architecture and data product principles that support strategic priorities.

• Enterprise data architecture maps sources, platforms, interfaces, stores and consumption
• Data domain modelling identifies bounded ownership, shared entities and dependencies
• Data products specify consumers, quality expectations, interfaces and service levels
• Data mesh principles assessed against organisational scale, skills and governance
• Build-buy-partner decisions evaluate platform fit, capability gaps and commercial constraints
• Architecture principles guide interoperability, reuse, metadata and controlled access

Participants can set data quality and metadata requirements for high-value data assets.

• Critical data elements prioritised by regulatory, financial and operational impact
• Data quality dimensions define accuracy, completeness, timeliness, validity and uniqueness
• Quality rules, thresholds and issue workflows establish accountable remediation
• Data profiling reveals nulls, duplicates, outliers, formats and referential breaks
• Business glossary and data catalogue establish shared definitions and lineage
• Metadata management supports discovery, impact analysis, audit evidence and reuse

Participants can incorporate lifecycle, access, privacy and security requirements into data strategy decisions.

• Data lifecycle controls cover creation, classification, retention, archival and disposal
• Information classification schemes distinguish public, internal, confidential and restricted data
• Role-based access control maps permissions to job functions and data sensitivity
• Privacy-by-design embeds purpose limitation, minimisation and consent requirements
• Records retention schedules reconcile legal obligations, business needs and storage costs
• Security threat modelling identifies misuse cases, control gaps and residual risk

Participants can position analytics and AI initiatives within accountable data management arrangements.

• Analytics operating models connect business questions, data products and delivery teams
• BI, predictive models and generative AI assessed by decision context
• Model risk controls address bias, explainability, monitoring and human oversight
• Data literacy roles define self-service boundaries, training needs and support channels
• Experimentation standards specify hypotheses, baselines, success metrics and guardrails
• Responsible AI principles align data use with policy and accountability

Participants can build a prioritised implementation roadmap with governance, investment and performance measures.

• Target operating model integrates governance, architecture, quality and delivery practices
• Roadmap initiatives sequenced through dependencies, readiness, value and risk
• Portfolio prioritisation uses weighted scoring, cost estimates and capacity constraints
• Data strategy KPIs track adoption, quality, cycle time, value and compliance
• Funding models distinguish foundational capabilities from product-specific delivery investments
• Change plans address sponsorship, communications, training, resistance and reinforcement

Participants can communicate a data strategy clearly and plan immediate actions for implementation.

• Strategy narrative connects executive priorities, evidence, choices, investments and outcomes
• One-page strategy maps communicate vision, pillars, measures, owners and milestones
• Executive decision papers present options, trade-offs, recommendations and required approvals
• Implementation governance sets review cadences, escalation routes and benefits reporting
• First-90-day action plans assign accountable owners, milestones and decision gates
• Personal application plans identify a priority decision, sponsor and next steps