Assess. Design. Build. Sustain.
From data chaos to business clarity. A four-phase method that turns data problems into working systems, run by the team that built them.
What are you trying to solve?
Pick the one that sounds most like your Monday morning. Each is a real engagement pattern, the services shown are the ones engaged.
“We’re migrating off a legacy warehouse and reporting can’t break.”
A phased cutover with parallel-run reconciliation, a freeze-window plan, and a sign-off path that keeps the CFO comfortable.
“Our reporting is untrustworthy and we don’t know why.”
Lineage-first diagnostic, root-cause fixes at the pipeline, and quality SLAs that hold up on a Monday morning.
“We need a data platform, from scratch — done properly.”
Reference architecture, semantic layer, and a build plan your engineering leadership signs off on before any code ships.
“Our data governance is one person’s Word doc.”
Stewardship model, active lineage, and a policy framework that survives examiner scrutiny — not just an intranet page.
“We’re starting an AI/ML program and don’t know where to begin.”
A trusted, well-modeled foundation first. We won’t sell you agents on top of a warehouse that can’t reconcile last month.
“We inherited a mess and need to know what to fix first.”
Two-week partner-led assessment, a ranked findings memo, and a phased roadmap you can budget against.
One method. Four phases. Nine practices as the work demands.
Every engagement follows the same rhythm. Switch phases to see which of the 9 IDS services engage where.
Assess
Source-of-truth inventory
Every producer, every consumer, every hand-maintained spreadsheet the finance team quietly relies on.
Quality & lineage baseline
Profiled against real workloads — not the vendor’s demo dataset.
Prioritized findings memo
A defensible, ranked list of what’s worth fixing first — signed off with your CFO and CIO in the room.
Design
Target-state architecture
Reference architecture chosen for your regulatory and reconciliation needs.
Data modeling
Transactional or Dimensional, Operational or Analytical, with explicit trade-offs documented rather than defaulted.
Written build plan
Reviewed with your engineering leadership before a line of code ships.
Build
Production-grade pipelines
ELT with orchestration, observability, and runbooks written by the same people who write the pipelines.
Warehouse cutover
Parallel-run reconciliation and a freeze-window plan the CFO signs.
End-to-end lineage
Wired into the pipelines that produce it. Not a bolt-on after the fact.
Sustain
Standing office hours
A Slack channel where the seniors who built it still answer questions.
Quarterly stewardship reviews
Data-quality trends, drift, and governance posture reviewed with your team.
Handoff on our own end
We’re gone as soon as your engineers own the platform outright — which is when it should be.
How a mid-market insurance carrier rebuilt its claims data pipeline in eight weeks — and passed a state examiner review three months later.
A 40-year-old mainframe extract, six brittle Access databases, and a claims analytics team that had stopped trusting its own numbers. We rebuilt the pipeline on Snowflake with parallel-run reconciliation and zero downtime.
From the IDS Journal
Playbooks, industry briefings, and technical deep-dives from the partners and the team! Read on the way to your next meeting.
What clients say when the deck is closed.
Real testimonials from the teams we’ve stood alongside.
Their deep technical knowledge coupled with my internal data hire allowed us to stand up a data environment quickly, well within my budget. Communication was clear and timely — they explained what needed to be done to a non-engineer so I felt confident in what I was paying for.
They brought deep technical expertise in data architecture, Snowflake, dbt, integrations, modeling, and quality. Their documentation and knowledge transfer were exceptional — through hands-on training and continued support, I became confident managing the environment they built.
One person owned the whole governance program in a Word doc. They passed an examiner review without a data finding — the first in the client’s history.
Let’s talk about your situation.
A partner reads every message and responds in under 4 business hours. No discovery-call funnel.