We work with our clients to align key stakeholders on the importance of harnessing and managing their data. We work with senior leadership to outline an enterprise end-to-end data strategy that includes establishing Master Data Management (MDM) governance and processes, data quality best practices, a digital endpoint strategy, stakeholder reporting governance, and continuous improvement actions based on data insights.

Our deliverables include:

• Data Literacy Training
• MDM Strategy Development
• Data Governance Framework (processes, communication channels, roles and responsibilities, accountabilities, and escalation)
• Data Migration and Integration Plans
• Streamlined System and Report Inventory with Gap Assessment
• Data Standards and Data Dictionary

We leverage our life sciences industry expertise to identify additional external data sources or partnerships that may be utilized to generate insights that inform and accelerate development and support real world evidence data generation.

We work with our clients to turn their data into insights that drive action. Using industry benchmark data and data management strategies, we tailor our clients’ reporting needs to monitor key indicators of the operational health of their studies. We recognize that there are multiple data consumers that require different levels of information and will provide a suite of automated reports and/or dashboards that include industry standard metrics and benchmarks for performance such as:

• Portfolio-Level Senior Leadership Reporting (high-level operational, quality and financial health via established KPIs, KQIs and KRIs)
• Program-Level Management Reports
• Study-Level Management Reports
• Investigator/Site-Level Reporting (comparing individual site metrics to study medians)
• Patient-Level Reporting (providing visibility into collected health information)

We combine leading and lagging indicators coupled with embedded industry performance benchmarks so clients can set clearly defined targets to measure operational and clinical data health.   

We work with our clients to define the right technology-enabled risk management processes, key risk indicators (KRIs), quality tolerance limits (QTLs), and assessment/categorization tools that enable risk-based approaches across the company, program, and study levels. These processes allow for earlier detection and mitigation of risks, proactive decision making, and cost-saving operational efficiencies.

At the sponsor, program, and/or study level, we perform a risk assessment with the key stakeholders and vendors to identify the critical risks, thresholds, and mitigations for the clinical trials and new virtual options using an efficient, pre-configured risk assessment and categorization tool (RACT). We outline the risk management strategy and clear risk triage and communication processes in an integrated quality and risk management plan (IQRMP) and update functional plans as needed. We also develop and implement a risk-based monitoring (RBM) strategy/framework that fits into the risk management process.

Our deliverables include:

• Integrated Quality and Risk Management Plan (IQRMP)
• Risk Assessment and Categorization Tool (RACT) alignment across all vendors and stakeholders
• Key Risk Indicator (KRI) Library development and customization per study
• Risk-Based Monitoring strategy and framework development and implementation support

We work with our clients to ready their organizations and their data needed to execute on proposed data and analytics strategies.  We help our clients identify the roles, skillsets, and processes needed to effectively and efficiently execute on their data strategies. We utilize a data maturity model to help us assess the gaps across the people, processes, and tools. We lead our clients through the implementation of a data readiness plan along with necessary behavioral and cultural changes necessary to sustain the changes that come with new data strategies.

A critical piece of the data landscape assessment is an initial data diagnostic to understand endemic and systemic data quality issues, data source redundancies, and automation opportunities. Related data deliverables include:

• Data Maturity Modeling
• Dataflow map of all data sources and integrations
• Data Quality Diagnostic that provides insight to current data quality issues
• Data procurement and curation best practices and recommendations