for Pharma
The platform
A Commercial Data & Analytics Platform is not a product you buy and switch on. It is a warehouse shaped for commercial analytics, a context layer your AI tools can use, tests at every stage, and an orchestration layer that lets a small team change it every week without breaking the launch report. Here is each piece, and where it runs.
Deployment
Worth being precise about, because it is the first thing a pharma security review asks. Your data stays in your account. The orchestration layer drives your environment without holding your rows.
The architecture
Most data architectures are shaped to maximize a vendor's revenue rather than your team's productivity. FITT trades some storage and compute for far less debugging. Open, published, and used by customers running our software without us.
F
Transformations behave like pure functions, so the same input always produces the same output. No hidden state, no order-of-operations surprises when a vendor file lands late.
I
Any job can rerun a thousand times without changing the result. When a specialty pharmacy resends Tuesday's file on Thursday, you reload it and move on rather than unpicking what the first load did.
T
Automated checks cover the pipeline instead of hope. Tests are generated from profiling rather than hand-authored, which is the only way coverage keeps up with a growing feed count.
T
Raw to final, with no chain of intermediate tables nobody can reason about. Bad data fails in staging, which is somewhere the field never sees, and the raw layer stays as the thing you can always rebuild from.
DataOps Automation
Generic orchestrators run DAGs. Our DataOps Automation product coordinates pipelines across teams, tools, and environments while managing development and production at the same time. It is the difference between scheduling tasks and running a data operation, and it is what let one launch team ship hundreds of schema and dataset changes a week without breaking anything.
Not another DAG tool. Automation coordinates pipelines across teams, tools, and environments, including pipelines of pipelines. One framework for the whole operation rather than a scheduler per tool.
Every engineer works in a production-like sandbox with its own tools, datasets, and tests, then merges when the work is ready. Nobody waits in line for a shared environment, and nobody breaks the launch report by experimenting.
Analytics move from development through testing to production on demand rather than through a manual release checklist. This is what makes hundreds of schema and dataset changes a week survivable.
Tests run at every step of every pipeline, not once at the end. A failure becomes an opportunity to add coverage rather than a fire, and alerts cut the window where anyone downstream sees bad data.
Common pipeline pieces get saved and shared across projects, which is how a new vendor feed becomes a few days of work instead of a project.
Metrics on test coverage, error rates, deployment cycle time, and features shipped. You can see whether the work is working, which is also what a quarterly business review runs on.
Testing
Testing only at the end means every failure surfaces where it is hardest to diagnose. A row-count check at gold tells you something is wrong and nothing about which of forty upstream steps broke it.
Raw
Row counts and amount sums against the vendor control record, freshness, volume bounds learned from that feed's own history, and schema drift so a renamed column fails loudly instead of filling nulls.
Refined
Cross-feed reconciliation, referential checks on every identifier, duplicate detection on keys that merges create, and alignment tested as a relationship between feed and current mapping.
Gold
Rollups tied back to source detail per grouping, funnel stages that each record belongs to exactly once, milestone dates in a valid sequence, and metric movement flagged when it exceeds what history suggests.
Catching an error at the layer where it first appears is the difference between a five-minute fix and a two-day archaeology dig. The data sources page lists what each feed does to you.
What you get
Dimensional models with multiple fact tables sharing conformed dimensions: physician, payer, product, time, territory. This is the basis for self-serve analytics in Tableau or any BI tool, rather than a report an engineer has to build for you.
Flatter views for data science inputs and for feeds out to other systems and vendors, sitting beside the stars rather than replacing them.
Schema and grain written down, business definitions for equalized prescriptions, territory alignment, and payer hierarchy, validated example queries, and live freshness state on every refresh. Without it an AI tool guesses which of 150 tables to join.
One record-linked dimension for prescribers and organizations, built from every identifier available: NPI, ME, DEA, and vendor provider IDs. It handles prescribers the MDM system never mastered, which is why launches ship on it before an MDM programme finishes.
Accelerators
Commercial pharma has been solved enough times that the shape is known. Starting from a working schema and a generated test suite is most of how 1.5 engineers covered a launch that usually takes ten.
Questions
Sixty minutes, one of your data sources, end to end on your screen. What the tests would catch, what the context layer would look like, and what your AI tool would answer pointed at the right shape of data.