Explore Tasks
Define the target data architecture, principles and roadmap
Data, engineering and investment owners receive a target-state data architecture with principles for shared entities and integration, a current-state and technical debt assessment, and a sequenced roadmap with cost and risk that individual service architectures (P-T76) follow.
1job does it
3jobs help with it
41skills it needs
Does itHelps with it
- Evaluating software architecture for data and AI systems
- Classifying AI uses against regulatory obligations
- Writing evidence-led findings and auditable recommendations with their limits
- Designing semantic models for governed analytics
- Designing relational schemas: keys, constraints and normalisation
- Designing dimensional models with explicit grain
- Explaining data and AI platform architecture patterns and building blocks
- Planning and validating controlled data migrations
- Recognising personal data and privacy duties
- Applying data classification and handling rules
- Writing verifiable requirements and acceptance criteria
- Comparing options against explicit decision criteria
- Making accountable decisions under uncertainty
- Assessing supplier dependency, data-handling and exit risk
- Building data protection by design into data products and pipelines
- Reading and tracing unfamiliar code
- Reviewing human- and AI-generated code changes for correctness and risk
- Sampling and reviewing a team's work against standards
- Diagnosing misleading data visualisations
- Verifying reports and dashboards before and after release
- Verifying AI-assisted outputs against sources
- Disclosing AI contribution to professional work
- Testing competing explanations with discriminating evidence
- Independently checking analytical conclusions and claims
- Escalating unresolved risks with evidence and clear ownership
- Giving specific developmental feedback on data and AI work
- Judging the materiality of defects and deficiencies for the intended use
- Designing and comparing decision scenarios
- Communicating quantified uncertainty
- Presenting and defending evidence-based recommendations to decision-makers
- Testing investment business cases under uncertainty
- Identifying and quantifying decision downside and exposure
- Prioritising work by evidenced value, cost, risk and confidence
- Eliciting the decision behind an analytical request
- Judging whether external evidence applies to the decision context
- Reasoning about systems, feedback and second-order effects
- Appraising options on a common incremental basis against a do-nothing baseline
- Revising prior decisions when evidence changes
- Facilitating group decisions using disputed evidence
- Rating risks against appetite and choosing proportionate treatment
- Recognising and resolving ethical conflicts in data and AI work
Paths that teach the skills it needs
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Strategic and Commercial Decisions: Options, Trade-offs and Priorities
Frame and decide commercial and strategic options, prioritise portfolios under capacity and review decisions afterwards.
- Modules
- 5 open
- Practice data
- Raket Parts
Skill Path
Diagnostic Investigation and Root Cause Analysis
Resolve contradictory evidence and find, verify and prevent the causes of business and data anomalies.
- Modules
- 5 open
- Practice data
- Raket Parts
Coming soonFinancial Modelling and Decision SupportData Platform Architecture, Integration and Economics