— Industries · Life sciences
Life sciences data governance on a lakehouse your QA team can inspect.
Pharma, biotech, medical device and distribution teams run on LIMS, MES, QMS, ERP, clinical and supply chain systems that rarely share a key. Our life sciences data governance work brings them into one governed lakehouse with lineage, audit trails and validated pipelines, so a batch question, a deviation or an inspector’s request is answered from records, not rebuilt from exports.
30+ years inside IBM · EY · J&J · McKesson
Lean Six Sigma Master Black Belt
AIMContext from $3,500
60-Day Ship Guarantee
— The pattern · Where it breaks
The records exist. The trail between them does not.
LIMS, MES and a spreadsheet.
Test results, batch records, deviations and inventory sit in separate systems. The link between them is a workbook someone maintains by hand, outside any audit trail.
Symptom: a batch investigation that starts with an export.
Analytics outside the controls.
Reports and models are built on copies pulled from validated systems, so nobody can show which version of the data a decision used or who changed it.
Symptom: a dashboard QA will not sign.
AI pilots with no decision record.
Teams test AI on deviations, complaints or documents, but the inputs, model version and human review are not kept anywhere an auditor could retrieve them.
Symptom: a promising pilot parked at the quality review.
— The work · What we build
What life sciences data governance looks like on the lakehouse.
Regulated data inventory
Every source holding GxP-relevant data catalogued in Unity Catalog with owners, classification and retention, so scope is a query rather than a meeting.
Lineage, source to report
Table and column lineage from LIMS, MES, QMS, ERP, clinical and supply chain systems through to the reports and models that use them.
Data integrity checks
Expectations built around ALCOA+ principles: attributable, complete, consistent and original records, with failures routed to a named owner.
Audit trails and history
Platform audit logs and table history retained and reviewable, so changes to regulated data carry who, what and when.
Validated pipelines
Pipelines defined as code with versioned releases, test evidence and change records, packaged so your validation team can review them in a risk-based way.
AI with human review
AI in regulated processes reading governed data only, with a decision record of inputs, model version, output and the person who approved it.
— The approach · Evidence by design
Data integrity is a pipeline property before it is an audit finding.
21 CFR Part 11 expects electronic records to carry secure, computer-generated, time-stamped audit trails, and ALCOA+ sets the bar for what a trustworthy record looks like. Both are far easier to meet when lineage, history and access are designed into the platform than when they are reconstructed after the data has spread.
So we build those controls into the lakehouse from the first table, and package the evidence the way a risk-based approach such as GAMP 5 expects. We build the evidence and controls; your QA and regulatory team makes the compliance call.
— The method · AIM-IT
Five phases. Every build, every time.
Assess, Innovate, Model, Implement, Track. Each phase ends with something you can inspect and sign off, not a slide.
01 · ASSESS
Assess
Scope and inventory
GxP-relevant sources, reports and models in scope, with owners and risk ratings.
02 · INNOVATE
Innovate
Control design
Lineage, access, audit trails, integrity checks and decision records designed once.
03 · MODEL
Model
One flow end to end
One regulated flow, for example batch release data or a deviation trend, governed and evidenced.
04 · IMPLEMENT
Implement
Roll out
Further sources and flows under the same controls, with your team pairing.
05 · TRACK
Track
Control plan
What is checked, how often and by whom, ready for the next audit or inspection.
— The deliverable · What you get
Life sciences data governance with a fixed scope.
AIMContext
Governed, GxP-aware data foundation, fixed scope.
Most engagements start with AIMContext, the fixed-scope build of a governed foundation:
- An inventory of GxP-relevant data with owners, classification and retention.
- Lineage from source systems to the reports and models that use them.
- Data integrity checks and retained audit trails on one regulated flow.
- Pipelines as code with the test and change evidence your validation team reviews.
- Decision records for any AI used in that flow, with human review captured.
We build the evidence and controls; your QA and regulatory team makes the compliance call.
$3,500+
14 days
5 phases
60 days
60-Day
Ship Guarantee
On every Sprint engagement: if we do not deliver the agreed working artifact in 60 days, you do not pay the final invoice. That is what AIM-IT is for.
— The discipline · Process first
Map the hand-offs before you validate them.
Our team has led process and change work inside large life-sciences and healthcare organisations, and the lesson is consistent: most data integrity gaps sit in the hand-offs. A result retyped from an instrument, a batch file emailed for review, a serialization event reconciled by hand at a distributor. We map those with Lean Six Sigma discipline before we touch the platform.
For distributors the same applies to supply chain data: DSCSA tracing depends on clean, connected transaction and product records, which is a data engineering problem first. Fix the process, then automate it on the lakehouse.
— Related reading · From the blog
Go deeper on governed regulated data.
Lineage
What auditors ask for and where lineage breaks.
AI
The fields a decision record needs.
Quality
Quality as a measured process.
— Straight answers · FAQ
Questions we get asked first.
Are you a Databricks partner or certified?
No, and we will not imply otherwise. Certification is in preparation, and we are not in the Databricks partner programme. What we bring is thirty years of delivering the enterprise systems this data comes out of, inside IBM, Ernst & Young, Johnson & Johnson and McKesson, plus Lean Six Sigma at Master Black Belt level. Ask us for the work, not the badge.
Will this make our systems 21 CFR Part 11 compliant?
No platform does that on its own. We build the controls and evidence Part 11 and ALCOA+ ask for: audit trails, access rules, lineage and retained history. Your QA and regulatory team decides whether it meets the requirement for your records and processes.
Do you validate the platform for us?
We deliver pipelines as code with versioned releases, test evidence and change records, packaged for a risk-based review in the spirit of GAMP 5. Your validation and quality teams own the validation decision and sign-off.
Can AI be used in a regulated process?
Only with governed inputs, a named human reviewer and a decision record of inputs, model version, output and approval. We design that before any pilot, so quality can review it rather than discover it.
We are a distributor. Can you help with DSCSA data?
We work on the data side: landing transaction and product records from your ERP and partner feeds, reconciling them and keeping lineage. Your compliance team and solution providers remain responsible for the DSCSA programme itself.
What does it cost?
AIMContext starts at $3,500 for a fixed-scope governed foundation, with a typical first build in 14 days. Larger programmes are quoted after scoping, and every engagement carries the 60-Day Ship Guarantee.
Start with one regulated flow, not every system.
One call, your systems and your next audit on the table, and a straight read on where the data integrity and lineage gaps are.