Improve confidence in your model by documenting the data behind every decision.
MADE Annotations helps engineering teams capture, monitor and assess the quality of data, assumptions and parameters used across model-based analysis — improving traceability, auditability and confidence across the asset lifecycle.
Data Sources
Capture where model parameters and assumptions come from.
Model Confidence
Assess confidence based on source quality and coverage.
Auditability
Maintain traceability for decisions, values and analysis inputs.
Task Reminders
Prompt users to complete pending annotations.
In model-based engineering, poor data creates poor decisions.
The quality of assumptions, sources and parameters used in a model directly affects the integrity of analysis outputs. As models evolve and multiple stakeholders contribute data, it becomes increasingly difficult to know where values came from, who provided them and why they were used.
Reduce GIGO with structured data quality traceability.
MADE Annotations gives teams a structured way to document data sources, key assumptions, narratives and comments against the live state of the system model — supporting better validation, review and engineering confidence.
From data entry to model confidence.
MADE Annotations connects model parameters to supporting evidence, tracks pending annotation tasks and generates dashboard indicators that help teams assess data quality, model quality and confidence level.
Capture Sources
Attribute data sources, assumptions and comments to model parameters.Assign Status
Track whether annotations are pending, completed or require further review.Apply Policies
Use configurable policies to define annotation requirements and confidence rules.Assess Confidence
Generate outputs that show model quality, data quality and confidence level.Model quality becomes visible.
MADE Annotations supports dashboard indicators that help engineering teams understand confidence across coverage, quality and source reliability.
MADE Annotations helps answer the questions behind the model.
It provides a structured approach to documenting the evidence, assumptions and decisions that support model-based analysis.
Where did the data come from?
Attribute model parameters to data sources so users can understand the origin and reliability of each value.
Who sourced the data?
Capture the person, source and status associated with parameters and supporting information.
Why was this value used?
Document assumptions, narratives and comments that explain engineering decisions and modelling choices.
Can the analysis be trusted?
Generate dashboard indicators that reflect model quality, data quality and confidence level.
What still needs attention?
List active annotations and pending annotation tasks so data quality gaps can be closed.
How should quality be governed?
Configure annotation policies to match organisational workflows, confidence settings and review requirements.
See model confidence, data quality and source coverage.
Turn hidden data quality risk into visible engineering evidence.
The MADE Annotations dashboard generates an overall confidence level in the model based on data source quality and the coverage of annotated data. It also provides an overview of the data sources used and supports confidence comparison using alternate data sources.
Document parameters, narratives, assumptions and comments.
Give every model input the context needed for review and audit.
MADE Annotations lists model parameters created or edited by users and allows details such as data source, person responsible and annotation status to be captured. It also supports narratives, assumptions and comments linked to items, analyses and modelling activities.
Download the MADE Annotations brochure.
See how MADE Annotations helps document data sources, capture assumptions, manage annotation tasks and assess model quality across the asset lifecycle.
Core capabilities of MADE Annotations
MADE Annotations provides a configurable workflow for managing data source quality, annotation completion and confidence assessment.
Dashboard Reporting
Generate model confidence outputs based on the quality and coverage of annotated data.
Parameter Annotation
List user-created and edited parameters with data source, owner and annotation status.
Narratives & Assumptions
Capture supporting narratives, assumptions and comments to preserve decision context.
Annotation Policies
Define annotation requirements for model parameters and adjust confidence policy settings.
Task Reminders
Automatically remind users to complete pending annotations and provide missing source information.
Source Confidence Ratings
Assign confidence ratings to data sources based on inherent source reliability.
How MADE Annotations improves data and analysis quality.
The workflow supports dashboards, parameter annotation, annotation policies, narratives, assumptions, comments and task reminders.
What MADE Annotations outputs
Once annotations are captured, MADE generates outputs that support validation, review and confidence assessment.
Asset Model Confidence
Generate a summary of overall asset model confidence based on the quality and quantity of annotated data.
Annotation Overview
Review annotations by type, status and source to understand the state of model evidence.
Active Annotation List
View all active annotations in the model, including pending tasks that still require user input.
Impact for engineering teams
MADE Annotations helps improve the reliability of analysis outputs by making model data quality visible, traceable and manageable.
Improve analysis quality
Strengthen confidence in outputs by documenting data sources and assumptions.
Reduce GIGO risk
Identify incomplete or low-confidence data before it undermines downstream analysis.
Preserve expert knowledge
Retain decision context inside the model as stakeholders and project teams change.
Support auditability
Maintain a traceable record of parameters, sources, assumptions and modelling decisions.
Improve model governance
Use configurable annotation policies to align with organisational review workflows.
Increase confidence
Use dashboard indicators to assess model quality, data quality and confidence level.
Start making better engineering decisions.
Explore what MADE can do for your team.