Fragmented clinical, operational, and commercial data delays critical decisions. We enable integrated intelligence for trial optimisation, patient outcomes forecasting and faster, data-backed interventions.
Apply predictive analytics to trial design, protocol modelling, and site performance to reduce timelines, improve data quality, and lower the cost of bringing treatments to market.
Build real-world data strategies that connect EHR, claims, and registry sources enabling evidence generation that supports regulatory submissions and commercial decisions.
Use data-driven site scoring models to identify and prioritise investigator sites with the highest enrolment potential, reducing trial delays and improving geographic coverage.
Forecast enrolment rates and patient availability using historical trial data and patient population models to set realistic timelines and prevent recruitment shortfalls.
Map the end-to-end patient journey from diagnosis to treatment to identify drop-off points, therapy switches and engagement opportunities that improve outcomes and adherence.
Model patient response to treatments and interventions using clinical and behavioural data, enabling more targeted therapy selection and personalised care pathways.
Identify and prioritise the highest-value physicians for commercial outreach using prescribing behaviour, patient population data, and propensity models.
Standardize data pipelines, taxonomies, and reporting frameworks end-to-end ensuring consistent, audit-ready clinical and commercial data across functions and geographies.
Apply computer vision and deep learning to medical imaging workflows to accelerate diagnostic analysis, improve detection accuracy and support clinical decision-making.
Generate accurate product-level and territory-level sales forecasts using market dynamics, patient funnel data and external signals to support resource planning and target setting.
Integrate data from wearables, remote monitoring, and digital therapeutics to create longitudinal patient profiles that improve care management and clinical outcomes.
Use computational modelling and simulation to evaluate compound-target interactions early in drug discovery, reducing experimental costs and accelerating candidate selection.
Optimize field force call plans and incentive compensation design using territory analytics, workload modelling, and performance data to maximize commercial productivity.
Extract structured insights from clinical notes, trial documents, literature, and adverse event reports using natural language processing and text mining pipelines.
Segment and analyse patient populations by diagnosis, treatment history or demographic profile to uncover outcome patterns and inform clinical and commercial strategy.
Forecasting demand for new products without reliable analogue mapping often leads to inflated projections, weak planning assumptions, and avoidable commercial risk. A global pharmaceutical manufacturer operating across over the counter categories needed a scalable forecasting capability for future drug launches. ZDS built a unified modelling framework that combined analogue identification, feature engineering, and predictive forecasting to improve launch accuracy, strengthen portfolio planning, and reduce dependence on opaque estimation methods.
Online shelf performance changes rapidly when product visibility depends on scattered signals, weak keyword coverage, and inconsistent content quality. A leading healthcare and biotechnology company operating across consumer and pharmaceutical categories needed better visibility into digital shelf performance. ZDS built a scalable digital shelf analytics framework that connected marketplace data, KPI modelling, and keyword intelligence to uncover ranking drivers, improve optimisation decisions, and increase model accuracy to nearly 82 percent.
Tell us about your data challenge and we'll come back with a clear, actionable plan — no jargon, no fluff, just a path forward.
