What’s included
What’s included
Decision mapping
The recurring decisions each team makes, and the evidence they need to make them.
Built with
- Amazon Quick Sight
- Amazon Redshift
- Amazon Athena
- Amazon SageMaker AI
Why it matters
Why it matters.
Dashboards fail when they answer questions nobody asked.
Starting from decisions fixes that.
Self-service only works when the underlying metrics are defined once and agreed by everyone.
Good analytics also tells you which AI use cases are worth funding, because you can finally size the problem.
What’s included · how it runs
Frame. Define. Build. Adopt.
Step 01
Frame
Workshop the decisions and success measures with each team.
What’s included
Decision mapping
The recurring decisions each team makes, and the evidence they need to make them.
Step 02
Define
Agree metric definitions and build the semantic layer.
What’s included
Metric layer
Agreed definitions and a semantic layer, so numbers reconcile across teams.
Step 03
Build
Deliver dashboards and analytics on curated data.
What’s included
Dashboards
Reporting in Amazon Quick Sight or your existing BI tool, designed for the decision, not the dataset.
Advanced analytics
Forecasting, segmentation and anomaly detection where they change what you do.
Step 04
Adopt
Train users, gather feedback and refine.
What you walk away with
- Decision and metric catalogue
- Semantic layer on your data platform
- Production dashboards
- Enablement and adoption support
Case studies
Customers we’ve delivered this for.
Case studyRepurpose.io
A platform that scales with demand.
How Repurpose.io moved a fast-growing content automation platform onto containers, managed data and automation on AWS, so it stays available, ships faster and costs less to run.
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[The promise in 2 lines].
[How the client got the outcome: what Assembly built and where it runs. One sentence.]
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