Data Strategy and Analytics Built for Confident Execution
Data only creates value when it supports the decisions people actually need to make.
We help Life Science companies design data strategies and analytics foundations that turn fragmented data into reliable insights.
Common challenges within data strategy and analytics
Data exists, but insight does not
Most Life Science organisations sit on large volumes of data across RIM, QMS, TMF, ERP, and clinical systems. Inconsistent metadata, parallel sources of truth, and unclear ownership make it difficult to trust what analytics actually show.
Teams spend time validating numbers instead of using them.
Analytics disconnected from regulated operations
Analytics initiatives often fail because data teams lack regulatory and operational context, while business teams ask questions that data cannot answer.
Dashboards end up reporting activity rather than insight, and rarely support the decisions QA, RA, or Clinical leaders actually need to make.
Technology decisions made before data foundations
Tools are selected before data structures, governance, and use cases are clear. This leads to complex architectures that are expensive to maintain and hard to adapt.
When analytics become fragile or opaque, adoption drops and reporting returns to the spreadsheets teams were meant to replace.
We solve this by
Data strategy grounded in real decisions
We start with how decisions are made across regulated operations. What needs to be known, when, and by whom.
Data domains, ownership and governance are then designed to support those decisions, rather than producing generic reports that measure activity instead of insight.
Trusted data foundations before dashboards
We establish consistent, understood data before building analytics layers. Data quality is assessed, metadata aligned, and structural gaps resolved first. The teams can rely on the foundation without constant manual validation or reconciliation.
Analytics designed for regulated workflows
We design analytics that reflect how regulated processes actually work across quality events, regulatory submissions, clinical milestones, and inspection readiness. The insights are actionable, explainable under regulatory scrutiny and auditable when questions arise.
Technology-agnostic analytics architecture
We do not sell platforms. Analytics solutions work across your existing tools, vendors, and data sources. Architectures fit your current environment and maturity, while remaining adaptable as requirements evolve. We make sure the technology follows business needs, not the other way around.
Ready to build analytics your teams can trust?
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What effective data strategy and analytics look like in practice
One version of the truth
Data from multiple systems is aligned, governed, and traceable rather than existing in parallel sources of truth. Teams stop debating numbers and start acting on insight.
Analytics that support real decisions
Dashboards answer operational questions rather than simply reporting activity. Leaders gain earlier visibility into risk, progress,and bottlenecks across regulated operations.
Less manual reconciliation
Reporting refreshes consistently and reflects operational reality. Spreadsheets, manual checks, and duplicated tracking are reduced or eliminated.
A foundation for automation and AI
Clean, governed data enables automation and AI initiatives to succeed without introducing new risk or rework.
Hear from our clients
Practical results delivered through deep life science expertise.
Why life science companies choose Epista
Deep regulatory and operational understanding
Our consultants have worked inside quality, regulatory, and clinical organisations. We understand how data is created, used, and challenged under regulatory scrutiny.
Strategy and execution combined
We do not stop at frameworks or future-state recommendations. We design, implement, and operationalise data strategies that hold up in daily work.
Technology-agnostic by design
Recommendations are driven by business and regulatory needs, not vendor incentives. You retain flexibility as systems and requirements evolve.
Built for long-term value
The goal is not dashboards for today, but data foundations that support decision-making, inspection readiness and continuous improvement over time.