Research Management Challenges and Roles

TetraScience Ideal Buyer Personas (5-2024)

• Key Personas: Chief Data Officer / AI Officer, Head of Research, Head of Development / CMC, Head of QC / Quality, Chief Information Officer, Head of Scientific IT, Scientific IT (Data Engineers, Data Architects)

Chief Data Officer (CDO)

• Role in Purchase Process: Budget holder/economic buyer
• Background: Data/business leader, typically the economic buyer, understands how data science contributes to the bottom line and business strategy. Seeks to increase efficiency, reduce costs, and drive innovation.
• Job Titles: Chief Data (and AI) Officer, Chief Data Innovation Officer, Chief Analytics Officer, Head of Insights and Analytics, Head of Business Intelligence, Head of Data Science (and Data Management), Head of Data and ML Platforms, Head of Computational Chemistry
• Responsibilities: Manage organizational data/analytics operations, drive data use/governance, set data strategy, deliver data-driven insights, drive framework development, bring together data silos across business domains, foster a data-driven culture.
• Goals: Align data strategy with business objectives, optimize business support with data insights, ensure data readiness, demonstrate data value, remove process bottlenecks, optimize productivity while avoiding risk/cost, ensure data security.
• Challenges: Inefficient/ineffective data management, integrating business/data silos, multiple versions of truth, scaling data management, lacking access to AI-ready data, identifying safety issues/risks, building a case for change, measuring data/outcome success.
• Discovery Questions: What is company data strategy?, Biggest data management inefficiencies?, How to scale data solutions?, Time spent finding/processing data?, How to measure business value from scientific data?, How to minimize data risks?, What is data maturity/AI readiness score?
• Key Messages: Improve scientific data management with a partner experienced in centralization/management, reduce data prep for scientific AI with an automated solution, make scientific AI projects successful with large-scale, AI-native datasets, be confident in AI algorithms, identify impactful AI use cases.

Head of Research

• Role in Purchase Process: Budget holder/influencer
• Background: Often economic buyer/final decision maker, understands how science, IT & data science affect research, wants to drive innovation/increase efficiency while reducing costs, prioritizes "Fail early, fail cheap", focused on business value of a solution, delivers treatments to clinic faster.
• Job Titles: Head/ESVP/SVP/VP of Research/Discovery, Head of R&D/Research & (Early) Development, Chief Scientific Officer.
• Responsibilities: Identify/prioritize research opportunities, establish IP, optimize productivity, define vision/roadmaps for solving scientific problems, manage budgets, lead research projects on time and on budget.
• Goals: Discover disease targets/novel therapeutics faster/cheaper, drive product innovation, support business/financial objectives, manage partnerships, proactively embrace scientific challenges, attract/retain talent, build high-performance teams.
• Challenges: Cost pressure, flat/declining budgets, keeping pace with tech, managing data from collaborations, inefficient/ineffective data/knowledge management, leveraging AI for decision making, making sure costs are appropriate for projects being performed.
• Discovery Questions: Biggest obstacles to accelerating time to clinic?, How to use data to innovate?, Level of readiness for AI/in silico methods for discovery?, Costs/time of wet lab experiments?, Improvement opportunities in research productivity/efficiency?, collaboration with other departments/CROs?
• Key Messages: Protect data/IP securely through cloud-based solutions, optimize data workflows/use cases with proven methods and impact, compare vendor-agnostic data for insights, build foundation for scientific AI, enable knowledge exchange, collaborative innovation.

Head of Development / CMC

• Role in Purchase Process: Budget holder/influencer.
• Background: Often economic buyer/final decision maker, understand organization's impact on drug development, seeks to increase efficiency and productivity/reduce costs, focused on business value of solutions, wants to deliver treatments to market faster, collaborations with different stakeholders to enable tech transfer.
• Job Titles: Head/ESVP/SVP/VP of R&D, Head of Process Development, Head of CMC, Head of Development & Manufacturing (Analytical Development, QC), Chief Scientific Officer
• Responsibilities: Develop/optimize production/quality testing of drug substances/products, optimize/scale-up formulations, transfer/support analytical test methods, manage budgets/deliver financial analysis, oversee data generated across development, interface with partners, formulate projects on time/within budget, allocate resources, etc.
• Goals: Faster, cheaper new treatments, minimize costs/maximize productivity, optimize formulations and processes, focus on stability, yield, and quality, keeping pace with new technologies/assessing their value, balance quality/compliance, efficiency/speed, collaborate with partners.
• Challenges: Minimize costs, maximize operational productivity, optimize formulations, maximize therapeutic stability/yield/quality/safety, cost pressure, keeping pace with new technologies, balance quality/compliance/efficiency/speed, collaborative efforts with partners, ineffective/inefficient data & knowledge management.
• Discovery Questions: Biggest obstacles to advancing new drug candidates to the clinic?, How to leverage data to optimize formulations, processes?, How to keep up with regulatory changes?, improvement opportunities for productivity/efficiency?, Level of readiness for Al for drug development?, What are data costs for tech transfer?, collaboration with other departments/CROs?
• Key Messages: Secure data/IP via cloud-based env., optimize data workflows, harmonize vendor-agnostic data/insights, build Al-native data foundation, facilitate exchange among parties, promote collaborative innovation.

Head of QC/Quality

• Role in Purchase Process: Budget holder/influencer
• Background: Influences decisions relevant to quality/regulation, trusts vendors with data handling proficiency.
• Job Titles: Head of Quality/Quality Control/Quality Unit, Head of Global Quality/Compliance, Head of Quality & Regulatory/Pharmacovigilance.
• Responsibilities: Strategic decision-making for regulations, quality guidance, mitigate quality/regulatory risks, oversee Quality Control, regulatory matters, ensure organizational compliance with regulations, ensure product quality/safety.
• Goals: Company-wide regulatory/quality compliance, ensure drug product safety, minimize product release times, minimize quality/regulatory risk/events, minimize compliance activities, promote a culture of quality.
• Challenges: Compliance with global regulations, ensuring compliance with product specs/increasing market combinations, high validation efforts that are non-value adding, uncertainty about data integrity/traceability, ensuring product safety/efficacy, and maintaining timely product/release with siloed systems, ensuring data integrity and traceability, high effort of documents.
• Discovery Questions: How to ensure integrity/traceability of data?, How to ensure fast batch/product release?, Minimize quality/compliance efforts?, Validation efforts/scientific data workflows?, How to measure data for reports, etc., Consider Al for optimizing compliance, and address data modernizations?
• Key Messages: Eliminate human error with data centralization/harmonization, minimize validation efforts/improve productized integrations, support Al/GxP principles.

Chief Information Officer (CIO)

• Role in Purchase Process: Budget holder/economic buyer.,
• Background: An IT/business leader, who understands organizational/IT contribution to bottom line, increases efficiency/reduce costs/drive innovation, and understands business value of solutions.
• Job Titles: Chief Information (and Digital) Officer (CIO), Chief Technology Officer (CTO), Chief Digital Officer (CDO), Head of Scientific Platforms, Head of Enterprise Architecture.
• Responsibilities: Oversee company’s digital strategy, manage IT profitability, optimize productivity/process efficiency, manage digital transformation initiatives, fund critical technology, run information services, maintain vendor relationships.
• Goals: Minimize IT costs/optimize business support, remove process bottlenecks, optimize productivity/avoid risks, improve/boost business results, and ensure IP security.
• Challenges: IT investments without ROI, high cost of legacy/complex systems, managing multiple/disparate systems, high re-work costs, projects over-budget/delayed, low operational efficiency, adapting to changing requirements, losing IP/issues with compromise.
• Discovery Questions: Company IT strategy?, Costs of scientific informatics landscape?, Process inefficiency areas?, Plans to digitally transform labs?, Managing scientific instrument/software vendors?, Minimizing security/compliance risks?
• Key Messages: Be confident about data security with a centralized/trusted cloud-based platform, build a future-proof foundation for Al while satisfying current needs, support scientific AI initiatives, ensure data optimization/support, and expertise to guide science, data, AI and business outcomes.

Head of Scientific IT

• Role in Purchase Process: Decision maker
• Background: IT leader for a specific scientific area (Discovery, Research, Development, QC, Manufacturing) likely the final decision maker, considers ROI/IT budget, value of vendors with pharma experience, and chooses solutions based on scientific needs, requiring value add over existing systems.
• Job Titles: Head of Scientific IT, Scientific Informatics.
• Responsibilities: Manage scientific/lab IT, drive a cloud/data-driven IT environment, provide scientists with tech to optimize productivity, ensure data integrity/compliance, keep systems updated, establish operational policies for testing methods.
• Goals: Optimize scientific productivity, ensure teams' ability to reuse data for analytics, optimize IT costs, ensure security and future-proof data, and ensure compliance.
• Challenges: Provide scientists with data from different sources, increasing complexities of instruments/applications, data incompatibility/manual data processes, keeping up with tech advances, complexity in managing IT, complying with highly regulated environments.
• Discovery Questions: Biggest IT challenges supporting scientists and data scientists?, Implement digital transformation, handle fragmented/siloed data, ensuring Al readiness/data security, how to modernize labs?
• Key Messages: Build a solution that enables scientific data workflows, build a future-proof foundation for AI, and utilize a data-centric approach that is secure, compliant, easily accessible, and automated.

Scientific IT (Data Engineers, Data Architects)

• Role in Purchase Process: Primary user/influencer
• Background: Build solutions for scientists, determine data processing/management needs, understands scientific data formats/applications.
• Job Titles: Data & Knowledge Engineer, Systems Engineer, Integration Engineer, Research Data Engineer, Lead Data Architect.
• Responsibilities: End-to-end process providing scientists with data, partner w/ scientists to bridge technology gaps, integrate lab instruments, automate data collection, provide data access through dashboards, troubleshoot/resolve data issues.
• Goals: Minimize time/cost of integration, enable data flow among tools/applications, provide access to data wherever needed, enable reuse of FAIR data.
• Challenges: Growing complexity of instruments/applications, accessing multiple siloed systems, inconsistent data formats among vendors, manual data processes.
• Discovery Questions: Time for integrations?, Various instruments/applications/data formats?, Ease of data pipeline creation?, How are scientists accessing data now?, How to ensure FAIR data?, How data workflows are defined presently?, Preparing data for analytics/AI/ML?
• Key Messages: Data-centric solutions will optimize efforts, ensuring data quality, facilitating advanced analytics and Al, minimizing prep time for Al use.

This quiz explores the various challenges faced in managing research as a business. It focuses on the roles and responsibilities of key positions such as the Chief Data Officer and the importance of collaboration and data optimization in research management.