DataDrill Blog
Technical insights, case studies, and best practices from our work in life sciences data engineering and software development.
Taking Over a Legacy Data Pipeline Nobody Else Understands
Most life sciences organizations have at least one: a data platform that hundreds of people rely on every day, built years ago, held together by nightly scripts, and understood in full by one person.
Why Life Sciences Digital Products Lose Momentum After Launch
A life sciences digital product can launch on time, pass its release checks and still lose momentum within months. The problem is often diagnosed as weak adoption or a slow roadmap. Those are visible symptoms. The underlying issue is that the product has moved into production without a complete ope
Pricing Data Has a Shelf Life. Most Market Access Models Assume It Does Not.
A pharmaceutical pricing model can be analytically sound and still produce the wrong answer. The formulas may be correct. The assumptions may be documented. The analyst may have checked every cell.
AI in Horizon Europe Health Projects: Why the Model Is the Smallest Problem
In an AI health project, the model is the part everyone can see. It produces the prediction, classification, simulation, summary, or recommendation that gives the proposal its technical edge. It is also often the smallest part of the delivery problem.
AI in Clinical Trial Workflows: Five Gates Between a Pilot and Practice
The clinical-trial industry does not have an AI capability problem. It has an absorption problem. The models can classify, summarize, predict, retrieve, and draft. Pilots can demonstrate those capabilities on a controlled dataset with an expert team watching every output.
FAIR Data Is Not a Data Management Plan: Building Reusable Infrastructure in Horizon Europe Health Projects
A Horizon Europe health project can have a detailed data management plan and still finish with data that nobody outside the original work package can use. The plan may describe storage, metadata, access, preservation, and sharing correctly.
Building the Data Foundation for Virtual Human Twins in Cancer Research
A virtual human twin in cancer research is not simply a model with more patient variables. It is a living computational system that must combine molecular data, medical images, pathology, clinical events, treatment history, and patient outcomes across time. If those inputs cannot be linked, trusted,
Post-Merger Data Integration in Life Sciences: What Breaks After the Deal
Post-merger data integration in life sciences determines whether an acquisition creates operational value or leaves systems fragmented.
Clinical AI Data Operations: The Real Constraint on Scale
Clinical AI data operations determine whether promising models move beyond pilots. Learn where pipelines, lineage, and governance break.
External AI on Regulated Data: How to Preserve Lineage and Governance
External AI on regulated data needs clear lineage, access controls, and governed workflows. See the engineering controls production teams need.
Why Healthcare AI Pilots Fail to Scale
Healthcare AI pilots often stall when fragmented data, manual preparation, and weak integration cannot support reliable production workflows.
Multi-Region Clinical Trial Data Integration: Building One Submission Backbone
Multi-region clinical trial data integration needs a shared backbone for traceability, transformation, and consistent evidence across markets.
Evidence-Grade Data: What Changes When the Model Becomes Evidence
Evidence-grade data makes model outputs traceable, reproducible, and defensible when AI becomes part of the clinical development record.
Standing with Dečije selo at YouthSpeak Novi Sad
DataDrill joined YouthSpeak Novi Sad to support young people, community leadership, and the work of Dečije selo through practical action.
Agent-Ready Data for Life Sciences AI
Agent-ready data for life sciences must be continuous, governed, traceable, and safe for AI systems that act across real workflows.
Data Lakes for AI: Building a Reliable Data Foundation
A data lake for AI unifies fragmented sources, supports reliable analytics, and creates a governed foundation for production automation.
How DataDrill Supports Young Leaders Through JCI in 2026
DataDrill supports young leaders through JCI programs focused on leadership, entrepreneurship, international cooperation, and community impact.
FHIR for Developers: Healthcare Data Integration in Practice
FHIR for developers provides an API-based approach to exchanging healthcare data across systems, applications, analytics, and AI workflows.
Digital CRO Technology: How Clinical Trials Are Changing
Digital CRO technology connects trial data, cloud platforms, automation, and analytics to improve visibility across clinical operations.
AI and Data Engineering in Life Sciences
AI and data engineering in life sciences work together when governed data, reliable pipelines, and production controls support real workflows.
Supporting Global Talent: DataDrill's Internship Program with AIESEC
The DataDrill AIESEC internship program brought international talent to Novi Sad and supported cross-cultural learning and collaboration.
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