The Smart Data Platform reference material is currently being streamlined. Use the Reference Library below as the primary entry point.
Commercial BioPharma laboratories create critical operational data across client setup, quotations, study configuration, analytical definitions, sample registration, testing, review, reporting, billing, and customer delivery.
Today, that data is often spread across systems, workflows, documents, and local practices. BII's goal is to make the data easier to find, understand, govern, measure, and use for better decisions.
The initial focus is not "apply AI everywhere." The initial focus is to understand the operational domains, improve data readiness, create trusted analytics, and establish governed patterns that can later support AI and agentic workflows.
Map how commercial, study, sample, testing, review, reporting, and billing processes connect.
Improve data availability, consistency, ownership, meaning, and readiness for analytics and AI.
Provide dashboards, reports, models, and decision support where laboratory and business teams need them.
Create the semantic, data, control, and evidence foundation needed before scaling AI.
Initial scope is centered on commercial BioPharma laboratory operations. Current work lives in these operational domains, the opportunity catalog, and supporting reference material.
Customer, account, contractual, and commercial setup information that controls how laboratory work is initiated and governed.
The quotation, offer, and commercial framework that defines what services are being sold and how they connect to execution.
Structured definitions of tests, methods, service templates, and analytical requirements used to drive laboratory work.
Customer analytical projects, study activities, milestones, and billing triggers for controlled execution.
Registration, linking, storage, and chain-of-custody steps that move samples into controlled laboratory execution.
Operational planning and laboratory execution, including worksheets, scheduling, testing, and completion visibility.
The first practical lens for the Smart Data Platform is the Life of a Sample. BII uses this to understand where time is spent, where data is missing, where handoffs create risk, and where better insight can improve turnaround time and quality.
Incomplete orders and data gaps create downstream delays.
Investigation and review steps can become major turnaround-time variables.
Missing data discovered late causes expensive rework before final assembly.
These are current capabilities in an emerging platform stage. They are intentionally practical and maturity-aligned.
Dashboards and reports that help teams understand throughput, timing, quality, and operational performance.
Evaluation of whether data is findable, accessible, connected, captured, governed, complete, consistent, standardized, defined, contextual, and time-aligned.
Early work to define the vocabulary, relationships, and meaning needed for trusted analytics and future AI workflows.
A living register of BI, data, and AI opportunities discovered through operational pain points and domain analysis.
Initial patterns for governed AI use cases, human review, evidence, and controlled agentic workflows.
Architecture layers, controls, standards, and evidence models that guide platform evolution.
Business Intelligence & Insights engages in three ways:
We work with business and laboratory experts to map how the process works, where data is created, and what decisions depend on it.
We identify ownership, quality gaps, definitions, system sources, semantic meaning, and integration needs.
We deliver dashboards, data products, semantic models, opportunity analysis, and where appropriate, governed AI-ready patterns.
Focused core roles for the current stage of platform development.
Focus: Why and when.
Prioritizes work, aligns opportunities to business value, sequences the roadmap, and protects focus.
Focus: What and how.
Shapes analytical problems, defines models and evaluation methods, and helps prepare future AI/ML use cases.
Focus: Point of impact.
Works with business teams to understand decisions, build reporting, surface insights, and translate data into action.
Focus: Breadth and action.
Connects systems, builds pipelines, structures data, and makes data usable for analytics and AI.
Focus: Meaning.
Defines the vocabulary, relationships, and semantic structures that make data understandable to people and systems.
Discover: Find where intelligence, data, or automation is missing.
Catalog: Register opportunities in a living evidence-based backlog.
Prioritize: Focus on high-impact and feasible work.
Prepare: Make the data, definitions, architecture, and controls ready.
Empower: Deliver insight through dashboards, data products, workflows, or future agents.
As the platform matures, high-risk AI or automation must include human review, traceable decisions, telemetry, and evidence. In regulated laboratory environments, trust requires that important decisions can be explained, traced, and defended.
Reference material for architecture, controls, and governance artifacts.