Adaptors / Board
Board adaptors
Governed target patterns for Board planning and forecasting
Early visibility of data structures and transformation logic gives planning programmes time to validate against their models before load. Code-printed transformations replace manual rework. Planning programmes start with governed data, not assumptions.
Earlier planning scenario visibility
Faster governed data preparation
Lower manual modelling support effort
How to keep your Board programme clear, controlled, and on track
Board implementation success depends on hierarchy shape, time period structures, version management, multi-dimensional models, and reconciliation between planning and actuals. The depth and breadth of these interdependencies create significant design-stage unknowns.
Anything that remains unresolved about hierarchy behaviour, time dimension alignment, dimension combinations, and version management at design stage represents programme risk.
Planning model design establishes the intended structure, but it does not expose the full range of aggregation behaviour, hierarchy interactions, time alignment issues, and model dependency impacts that will emerge during data load and initialization. Hierarchy complexity, calendar misalignments, and multi-dimensional consolidation patterns remain implicit until they collide with real planning data.
When those details remain hidden, they surface during data load testing or planning model initialization, forcing rework after hierarchy and dimension decisions have been locked.
In most programmes, these are treated as unavoidable unknowns. In practice, they are known unknowns.
They can be revealed early in a structured way by examining real planning data structures, hierarchy patterns, time period relationships, and aggregation requirements from source systems, well before model design is finalized.
Programmes stay controlled when those known unknowns are translated into clear hierarchy validation rules, time period alignment logic, dimension combination maps, and model dependency documentation that can be planned for early rather than reacted to late.
Making the full scope, model complexity, and dimension dependencies visible early keeps decisions aligned, reduces rework, and allows the programme to progress with confidence.
The challenge is not complexity. It is how early known unknowns are revealed.
These are known risks with known mitigations. The patterns that surface are rooted in organisational specific operating behaviour, but are predictable from legacy data. Making them visible early turns risk into controlled delivery.
Use the data stream to validate earlier
As-is and to-be hierarchy designs and dimension structures capture the intended planning model, but real source data reveals the aggregation patterns, hierarchy interactions, and structural exceptions that may not be fully visible in planning sessions alone.
It shows what the planning model will actually have to handle—not just what the business expects it to handle.
Working with real data early allows programmes to validate assumptions about hierarchy integrity, dimension validation rules, time period alignment to planning calendars, and version/scenario management when change is cheap, before those issues surface later in delivery.
This enables the data stream to act as an early validation mechanism for planning readiness and model behaviour, reducing the risk associated with known unknowns and bringing greater control to programme cost and timelines.
Identify Board objects that will drive your cutover risk
Explore objects by domain and delivery impact to shape your migration strategy.
What it accelerates
- Board-ready outputs: hierarchies, dimensions, and actuals prepared in formats Board expects
- ERP-to-Board mapping: templates transform source data into planning structures with exception handling
- Validation checks: data completeness and consistency verified before handoff to Board
- Repeatable orchestration: delivery runs produce identical results, so issues found once stay fixed
- Sign-off evidence: objective pass/fail evidence at each stage supports delivery governance
How this adaptor works in your programme
The controlled non-determinism model applied to Board:
- 1Profile source structures: understand data models, hierarchies, and dimensions available from source systems
- 2Define Board-ready mapping logic: design transformation rules, handle exceptions, and define data validation criteria
- 3Build repeatable transformations and validations: code-printed transformation logic and validators run deterministically at every rehearsal
- 4Produce governed outputs for load or integration: validated data prepared for Board load or downstream integration
- 5Support rehearsal and sign-off with evidence: objective pass/fail evidence at each stage gives the programme confidence in data accuracy and readiness
AI boundary: AI never processes customer data; it supports mapping and delivery configuration only. When AI assists with code generation, the output is reviewed, QA'd, and verified in test runs before deployment to any system.
Where elfware fits in your programme
Elfware runs the data stream mechanics in a way that makes scope, dependencies, and data behaviour visible early and repeatably as solution design evolves. We provide the bridge between your in-house legacy experts and your Oracle Retail implementation partners, helping surface hidden scenarios and establish governed data assets early enough to strengthen functional design without undermining operational imperatives.
This reduces data unknowns early, shortens rehearsal cycles, and removes avoidable manual scripting from the migration stream.
Source vs target usage
As a target (planning and budgeting platform)
Preparing and delivering data to Board for planning, budgeting, and forecasting programmes. Data typically sourced from ERP systems, finance systems, and master data platforms.
- Entity hierarchies: cost centres, profit centres, business units, organisational structures
- Dimension hierarchies: product hierarchies, geography hierarchies, customer hierarchies
- Chart of accounts: GL accounts, account hierarchies, account dimensions
- Planning parameters: budget drivers, allocation bases, planning assumptions
- Actuals and forecast data: financial actuals, forecasts, and rolling projections
- Master data and time dimensions: currencies, time dimensions, version mappings, baseline definitions
Typical artefacts delivered
Data mapping specifications
Governed mapping documents covering source structures, transformation logic, and target Board data formats.
Transformation patterns
Repeatable transformation logic for hierarchy flattening, version handling, and multi-dimensional data preparation.
Orchestration and scheduling
Data flow sequences ensuring correct data domain ordering and dependency management across feeds.
Validation logic and control checks
Deterministic validators checking data completeness, accuracy, and consistency before handoff to Board.
Evidence and sign-off packs
Objective pass/fail evidence confirming data accuracy and readiness at each stage of the programme.
Interfaces and data domains
| Domain | Typical entities | Cadence | Notes |
|---|---|---|---|
| Entity hierarchies | Cost centres, profit centres, business units, organisations | Full + delta | Hierarchical structures validated for circular references and integrity |
| Dimension hierarchies | Product, geography, customer hierarchies | Full + delta | Multiple hierarchy versions and effective dating supported |
| Chart of accounts | GL accounts, account hierarchies, account types | Full + delta | Account codes validated against Board segment structures |
| Planning parameters | Budget drivers, allocation bases, planning assumptions | As defined | Driver mappings to source data validated before use in planning models |
| Actuals and forecasts | Financial actuals, budget versions, forecast versions | Monthly/quarterly | Multi-version support for rolling forecasts and scenario planning |
| Master data | Currencies, time dimensions, version definitions | Full + delta | Time dimensions aligned to Board calendar and planning periods |
Interfaces and data domains
Review the data domains and interfaces covered by this adaptor, including entities, load cadence, and delivery notes.
View interfaces and data domainsCommon risks and how we mitigate them
Complex multi-dimensional hierarchies
Adaptor handles hierarchy flattening, parent-child validation, and version management with audit trails.
Data quality issues from source systems
Pre-transformation validators identify and report issues objectively so programmes can address them at source.
Time dimension and calendar alignment
Data orchestration ensures time dimensions are correctly mapped to Board calendar before delivery.
Version and scenario proliferation
Adaptor manages version hierarchies and maintains version relationships through transformations.
Data accuracy for planning models
Pre and post transformation validators confirm data completeness and accuracy with objective evidence at each stage.
Multiple source systems
Adaptor consolidates from multiple sources with deduplication and golden-record resolution logic.
These Board-specific risks are instances of broader patterns that affect all complex migration programmes. Learn about programme-wide risk controls
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Frequently asked questions
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