The Build Follows
The Blueprint.
Organizations whose completed Blueprint findings support a defined-scope Build.
Teams ready to move from a data-audit finding into a working, governed platform.
Marketing organizations that need audiences and segments activated across real channels, not just reported on.
Data and analytics leaders who need senior Databricks delivery accountable for a defined outcome, not more headcount.
A Representative Baseline
Exact inclusions come from your Blueprint findings, but a representative Build typically includes:
A configured Databricks workspace with access controls and governance in place from day one.
Source ingestion and production data pipelines built to run reliably, not as a one-time export.
Scoped data cleaning, identity or entity resolution where relevant, and models built for marketing use, not just reporting.
Activated audiences and segments, dashboards, and recurring jobs connected into CRM, email, SMS, paid media, and sales destinations.
Consent and governance controls, measurement and reporting, documentation, runbooks, training, and a priced 30-day stabilization period.
Four Delivery Phases
Configure the platform, access controls, governance, and ingestion.
Build pipelines, perform scoped cleaning, and create trusted data models.
Implement the agreed segments, dashboards, jobs, and destination connections.
Test the agreed strategy and system, confirm measurement, train your team, and transfer documentation and runbooks.
What This Is Not
Your team may own selected workstreams when agreed in advance, but Stratosphere remains an external team accountable for the design, integration, and contracted outcome.
Seven to ten weeks is typical. Severe data-quality or remediation needs may extend the schedule, and we won't promise a quarter or a fixed 60 or 90 days.
Foreseeable remediation is priced from the Blueprint. A material issue that couldn't reasonably have been identified becomes a fixed-fee change order, not open billing.
Existing dashboards, pipelines, and marketing workflows keep running until replacements are validated, using explicit parallel-validation and cutover criteria.