The framework
A balanced scorecard for process data readiness
A GPS tells you where you are, not just that you have a vehicle. Biometre does the same for process data: it answers whether your data has actually arrived at a state where engineers, and the AI models and agents built on that data, can use it to monitor, understand, and reduce variability — versus merely existing in a system somewhere. Six data categories, six delivery qualities, one score.
The "WHAT" axis — data categories
Six categories of manufacturing data, split between process outputs (what the process produced) and inputs (how it ran). Output categories — CQAs and Yield — carry 60% of the total weight because they are the key outputs, the Y in Y = F(X), and understanding their variability is the goal of monitoring.
| Category | Type | Capability assessed | Subdimensions | Weight |
|---|---|---|---|---|
| CQAs Critical Quality Attribute (CQA) availability | Output | Can the user trend and download QC Critical Quality Attribute testing data? | DS CQAs, DP CQAs, Specifications | 30% |
| Yield Reactor & batch output; yields | Output | Can the user trend and download measures of process output per batch? | Batch Output, Reactor Output, Step Yields | 30% |
| Batch Handling of batch record data | Input | Can the user trend and download per-batch and daily batch-record data for each process step? | USP, DSP, Deviations | 10% |
| Continuous Management of continuous data | Input | Can the user trend and download bioreactor and chromatography data in context, including spectral / PAT sensor data? | USP, DSP, PAT | 10% |
| Genealogy Traceability of materials and data | Input | Can the user trend and download the linkages between unit operations and sites? | DS Intermediates, DS–DP Connection, Raw Material Connection | 10% |
| Calculated Features Calculated features extracted from continuous data | Input | Can the system extract and trend features of continuous data as batch data (e.g. HETP)? | Cell Culture, Chrom Metrics, Gen Calc | 10% |
Weights reward the information a category carries about the process, not how hard its data is to obtain — and the same logic runs one level down. Subdimension weights (currently 40/40/20) place PAT, for instance, under Continuous data at roughly 2% of the overall score: deliberately minor, because a spectral sensor's value depends entirely on whether its data can be managed, contextualized, and linked to a quality outcome.
The "HOW WELL" axis — delivery qualities
Each data category is rated against the same six delivery qualities, weighted equally. These criteria evaluate whether data reaches the people who need it, in a form they can use, with sufficient confidence to act on it.
Fresh
Data is current when it reaches you — scored on the typical delay, not the best case. Outages, downtime, and data-entry lag push the typical cadence below the nominal spec, so a frequently-interrupted feed scores toward the delay you actually experience.
- 100 live Real-time or near-real-time, and dependably so
- 75 daily Current to within a day in typical operation
- 50 weekly Weekly — or a nominally faster feed degraded by frequent outages or entry lag
- 25 quarterly Periodic (monthly/quarterly), or often interrupted
- 0 unavailable Data does not exist in any accessible form
Frictionless
Tools are friendly, responsive, and accessible — minimizing effort to reach a result.
- 100 1 min Click and see — self-service, no navigation burden
- 75 3 min Quick lookup in a familiar tool
- 50 10 min Navigate a system, run a query, or find the right dashboard
- 25 30 min Log into a source system, export, and manipulate
- 0 day Request data from someone else and wait
Accessible
Can the intended many reach it in principle — broad users can access the tool, discover how to find it, and obtain permission to use it. Reach is scored against the audience named in the capability statement, never against the people who currently hold the path. Reliability/uptime is not scored here — it lives in Fresh (best-vs-typical delay).
- 100 globally Broad reach, self-service — easy to discover, permission granted by default
- 75 many local Widely known and used by local teams — discoverable, permission readily obtained
- 50 few local A subset of trained users at the site — limited discovery or permission
- 25 data engineer Requires specialist skills or a standing permission request; most intended users cannot find or reach it
- 0 inaccessible No practical path — unknown to its intended users, or permission unobtainable
Authentic
The delivered output is only as assured as the weakest link in its chain: source system → underlying data layer → user consumption layer.
- 100 E2E Validated end to end
- 75 data layer Validated data layer
- 50 source only Validated at source only
- 25 unproven Digital, nothing validated
- 0 issues Not digital, or known integrity problems
Standard
Conformance to what the enterprise IT organization designates as the standard. Consistency and scalability follow from being on the designated standard — they are consequences, not the test; a capable one-off that IT has not designated is not Standard.
- 100 core On the enterprise standard the IT organization designates — the sanctioned tool or approach
- 75 scalable Scalable and enterprise-grade, but not the IT-designated standard (e.g. a capable one-off)
- 50 moderate Partial conformance; significant site-specific variation
- 25 limited Mostly site-specific; minimal alignment to any designated standard
- 0 local Entirely local or bespoke; no designated standard
Structured
Data is ready for ML / AI analysis.
- 100 table in lake Fully mapped, named, and contextualized in a data lake with complete metadata
- 75 table export Structured tables available for export with good metadata
- 50 most context Structured within its source system but not in an analytics-friendly format
- 25 some context Some digital structure but incomplete metadata
- 0 no context Unstructured or no digital representation
How the score is calculated
Rate each combination
For each data category, score its three subdimensions against the six delivery qualities on the 0–100 scale — a 6 × 3 × 6 assessment matrix.
Combine by weight
Delivery quality scores average within each subdimension; subdimensions combine by their configured weights (currently 40/40/20) into a category score; categories combine by their impact weight into the overall BIOMETRE score.
Identify priority improvements
Each gap is ranked by the score improvement it would unlock if raised to 100, directing attention toward the highest-impact changes first.
Biometre and FAIR data
FAIR — Findable, Accessible, Interoperable, Reusable — is the reference standard for
good data. Its north star is machine-actionability: data that software
can locate and process. Biometre's is human-actionability: data a real
person can get to and act on, in time, with confidence. The two are complementary, not
rival — but they are not the same, and the difference is where monitoring quietly fails.
A dataset can be 100% FAIR and still 0% Biometre-ready.
What FAIR covers
Findable and Accessible make data locatable and retrievable over standard protocols; Interoperable and Reusable line up closely with Biometre's Structured and Standard qualities. This is necessary plumbing.
Where FAIR narrows
FAIR-Accessible means permissioned retrieval by a machine — not the breadth of human reach Biometre scores. FAIR-Reusable proves provenance, where data came from, not that it holds up when tested — which is what Authentic asks.
The gap Biometre measures
No FAIR principle checks whether data is Fresh or Frictionless. A fully FAIR dataset can still arrive too late for the 11 a.m. meeting, or only after an engineer is summoned to produce it. Biometre scores what people actually receive.
How this differs from existing frameworks
Biometre is anchored in Quality by Design (QbD) and Continued Process Verification (CPV) — extending those regulatory expectations toward continuous improvement and process understanding. It is deliberately narrower and more actionable than frameworks designed for enterprise-wide digital transformation.
BioPhorum DPMM
The Digital Plant Maturity Model evaluates whether your plant is digitally mature. Biometre evaluates whether your process data is ready for analysis — the data foundation that DPMM's higher maturity levels assume is already in place.
ISPE Pharma 4.0
Pharma 4.0 provides a holistic view of organizational readiness for digital transformation. Biometre focuses on a single, specific question: can the right engineer see the right process data, in time, with confidence?
Generic Industry 4.0 indices
Most digital maturity indices describe aspirational end-states — AI, digital twins, predictive control. Biometre starts earlier: are the basic data accessible at all? Foundational data access is a prerequisite, not a given.