How to increase your resource management maturity to support data-driven decisions

How big data means big opportunities for pharma industry

Big Data refers to the humongous volume of data, which can be structured and unstructured. To make sense of this data is the latest interest of any data scientist. It can help with various predictive models, analyzing trends, helping businesses make better decisions, and make operations more efficient. With the introduction of big data analytics in the pharmaceutical and life sciences industries, the complex business processes were streamlined, and the efficiency of the process was improved. Thus, various investors from the healthcare and pharma domain have invested around $4.7 billion in big data analytics. Big data analytics enables businesses to dig deep into their data and gain insights from them. This data can be historical or real-time and can come from various sources like PPM tools, sensors, log files, patient enrolment. With the help of big data analytics, you can identify hidden data patterns to make data, etc. According to the McKinsey Global Institute, the application of big data strategies would lead to better decision-making. This will lead to a value generation of $100 billion across the US Health-care system. It will serve to efficient research work, advanced clinical trials, and innovation of new tools. Effective utilization of these data will help the pharma companies to identify new candidates for drug trials and develop them into effective medicines. Big Data Can Be Beneficial in the Life Sciences Industry Due to the Following Applications Reduced research and development cost Did you know developing a single drug could cross over $2.6 billion (about $8 per person in the US) over a period that usually lasts for over 10 years? According to Joseph A. Dimasi, director of economic analysis at Tufts CSDD, drug development and research are costly undertakings across the pharmaceutical industry. Medicines to fight diseases like ALS (Amyotrophic Lateral Sclerosis) are not being developed because the cost of developing the medicines outweighs the demand. Big data can help in fast-tracking the research work with the help of artificial intelligence to minimize the time needed for clinical trials. This will reduce the required research, thus lowering the cost of medicine in the long run. Solving complex protein structure is another mystery for the pharma researchers. The researchers need to ensure that the drug does not have any reverse effect on the patients. To ensure this, a machine-learning algorithm was developed at Carnegie Mellon University to test and analyze the interaction of different drugs with protein structure. The accuracy of the results obtained through the machine learning algorithm saved valuable time, thus getting the drug from the clinical to the market at a faster rate. Better clinical trials There can be a lot of applications for big data analytics in conducting clinical trials. The process of matching or recruiting a patient can be done using various Machine-Learning algorithms. These algorithms can reduce manual intervention by 85%, thus leading to cost and time saving during large trials. Machine learning techniques like association rules and decision trees help in determining trends relating to patient acceptance, adherence, and various other metrics. Big data can help in designing flowcharts to match and recruit more patients in clinical trials, which will in turn increase the success rate of the drug. A predictive model can help in analyzing the competitors of the new product based on several clinical and commercial scenarios. Big data models can also save the company from undergoing any adverse situations, which can be caused due to operational inefficiencies or other unsafe measures. Escalated drug discovery With primitive techniques, drug discovery took much time owing to the physical testing of these drugs on plants and animals, which was an iterative process. This caused inconvenience with patients requiring immediate attention like the ones suffering from Ebola, or swine flu. With the help of big data analytics, researchers use predictive modeling to analyze the toxicity, interactions, and inhibition of the drug. These models use historical data collected from various sources like clinical studies, drug trials, etc. for near accurate predictions. Controlled drug reaction With the help of predictive modeling, real-world scenarios are replicated to test the harmful effects of drugs in their clinical trials. Data mining on social media platforms and medical forums to perform sentiment analysis helps in gaining insight into adverse drug reactions (ADRs). Precision medicine Big data empowers precision medicine by providing insights into the complex interplay between genetic, environmental, and lifestyle factors influencing health and disease. By harnessing the power of data analytics, researchers, healthcare providers, and life sciences professionals can unlock new insights, accelerate innovation, and revolutionize healthcare delivery. Focus on sales and marketing Big data can help the pharma companies predict the sale of a particular medicine while considering the various demographic factors. This will help companies predict customer behavior and build advertisements accordingly. External and internal collaboration Big data fosters collaboration which enables a comprehensive understanding of genetic, environmental, and lifestyle factors impacting health and disease. By pooling resources and expertise, stakeholders can collectively interpret data, identify trends, and develop innovative solutions in precision medicine. Such collaboration enhances the efficiency of research, accelerates the discovery of novel treatments, and improves healthcare delivery for individuals worldwide, ultimately advancing the field and benefiting patients through tailored interventions and improved outcomes. Big data can help pharmaceutical representatives identify appropriate medicines for each patient by leveraging laboratory data and analyzing vast volumes of pharmaceutical data This will help in creating customizable medicine plans for each patient owing to their unique blend of diseases. Whether it is the application of big data in precision medicines, or to decrease the rate of drug failures or to lower the cost of research and drug discovery, there is a bright future for big data analytics in the pharma world. With data being the new oil, harnessing this resource is a must for any pharma company to provide better and quicker medicine to humankind. Big Data Challenges for Life Sciences Owing to the data complexity and stringent regulations, adoption of big data is rather slow for the life sciences sector. Organizations often face operational and technical challenges which can become roadblocks in achieving data-backed decisions. It is crucial to deliberately tackle these challenges to ensure their data transformation succeeds: Data silos and fragmentation: Life science companies often have data spread across various departments, legacy systems, and geographical locations, leading to data silos and fragmentation. Integrating and harmonizing these disparate data sources is a significant challenge, and when not resolved may lead to institutional decision-making. Data quality and governance: Ensuring data quality, consistency, and integrity is crucial for accurate analysis and decision-making. However, maintaining data quality can be challenging, particularly when dealing with diverse data sources and formats. Implementing robust data governance practices, including data validation, standardization, and access controls, is essential but often complex. Regulatory compliance and data privacy: The life sciences industry is heavily regulated, and companies must comply with strict regulatory requirements, such as Good Clinical Practice (GCP), Good Manufacturing Practice (GMP), and data privacy laws (e.g., HIPAA, GDPR). Ensuring compliance while leveraging big data can be a significant challenge, as it requires implementing robust security measures, anonymization techniques, and audit trails. Talent and skill gaps: Big data analytics requires specialized skills in areas such as data science, bioinformatics, and computational biology. However, there is often a shortage of professionals with the necessary expertise, making it difficult to build and retain a skilled workforce. Legacy infrastructure and technology limitations: Legacy systems and outdated infrastructure can hinder the ability to handle and analyze large volumes of data efficiently. Modernizing and integrating these systems with new technologies can be challenging and resource intensive. Cultural resistance and change management: Adopting a data-driven mindset and embracing new technologies often requires significant cultural shifts within organizations. Overcoming resistance to change and fostering a culture that values data-driven decision-making can be a substantial challenge. Collaborative partnerships and data sharing: Life science companies often need to collaborate with external partners, such as academic institutions, research organizations, and other pharmaceutical companies, to access and share data. Establishing effective partnerships, navigating data ownership and intellectual property concerns, and ensuring secure data sharing can be complex. Return on investment (ROI) and value realization: Implementing big data solutions can be costly and resource intensive. Demonstrating tangible value and ROI from these investments can be challenging, particularly in the early stages of big data adoption. Best Practices to Make the Most out of Big Data Big data presents immense opportunities for pharmaceutical companies to transform their huge R&D data. By harnessing the wealth of data now available, companies can accelerate innovation, enhance pipeline decisions, improve clinical trials, and sharpen their focus on real-world evidence. However, to make the right use of the data life science companies should follow some best practices, such as: Collecting high quality data: High-quality data collection is crucial for life science companies to reap the benefits of big data. A few best practices to achieve this are: Standardizing formats and coding systems ensure consistency in data.Robust validation and cleansing processes can identify and address issues early on.Detailed metadata aids interpretability and traceability. Audits verify accuracy against original sources. Tracking provenance ensures compliance.Automated capture technologies reduce errors and save time.Continuous monitoring with metrics and alerts enables prompt issue detection. Centralizing data ownership: Pharmaceutical companies generate and collect data from various sources, including clinical trials, electronic health records, genomic data, and real-world evidence. Integrating and harmonizing these diverse data sources is crucial for gaining comprehensive insights and making informed decisions. To facilitate data sharing, enhance accountability, and enable a more data-centric view, it is recommended to break down organizational silos and appoint centralized owners for each data type. Data quality, governance, and technology infrastructure: Ensuring data quality and implementing robust data governance practices are essential for accurate analysis and regulatory compliance. This includes data validation, standardization, and establishing clear data ownership and access controls. Legacy systems must be updated and connected to reduce data fragmentation, and analytical capabilities need enhancement to extract maximum value from the data. Piloting analytics projects: Rather than waiting for an ideal end-state, begin with small-scale pilots to demonstrate value and build capabilities incrementally. These objectives should be aligned with broader business goals, such as improving drug discovery efficiency, optimizing clinical trials, or enhancing marketing effectiveness. Begin with small-scale pilot projects that focus on addressing specific use cases or challenges. Starting small allows you to minimize risk and demonstrate value more quickly. For example, you could start with a pilot project to analyze clinical trial data to identify patient populations that are most likely to respond to a particular treatment. Change management: Adopting a data-driven mindset and embracing new technologies often requires significant cultural shifts within organizations. Overcoming resistance to change and fostering a culture that values data-driven decision-making can be a substantial challenge. Clearly communicating the vision and benefits of adopting big data to all stakeholders, including executives, managers, and employees might reduce resistance. Providing training and resources to help employees develop the necessary skills to work with big data effectively. Regularly evaluating the impact of big data on business outcomes, such measures can help in accurate and effective change management. Partnering with technology experts: Partnering with technology experts is key for life science companies to maximize big data's potential in R&D. Life sciences companies can benefit a great deal from the expertise the technology partners bring in novel technologies like artificial intelligence, machine learning, cloud computing, and advanced analytics. Collaborating with software providers, data analytics firms, or academic institutions with strong data science programs can establish access to cutting-edge tools and talent. By anticipating and tackling these adoption barriers head-on, rather than viewing them as roadblocks, pharma companies can unlock the breakthrough potential of big data. Partnering with the service providers that are experts in the domain is one of the best ways to achieve a smoother transition. i2e can help navigate the complexities of big data implementation with ease. Our tailored approach addresses all the challenges and ensures a seamless integration of big data into your operations. Schedule a demo with us to realize the full potential of Big Data. 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Project Structures: Overview and establishing with Planview, Planisware, and Project Online

From a pharmaceutical R&D context, project structures can be seen as a reliable process to organize, track, and deliver the tasks while keeping the stakeholders informed. If you don’t have a well-planned project structure: Portfolio decisions usually suffer from low visibilityTeams start losing alignmentMissing critical deadlines become normal With a strong project structure, key decision-makers can: Have a clear view throughout the entire pipelineEasily prioritize high-value portfolio assets Manage risks more efficiently Project portfolio management tools like Planisware, MS Project Online, and Planview help establish organized structures that accelerate pharma projects, ensure compliance and success. That said, let’s dive into what project structures mean in pharmaceutical context, and how these tools help establish them seamlessly. Types of project structures in pharmaceutical R&DSelecting the right structure is your first step to ensure on-time project delivery with the right resources, without bleeding budgets. Here are three types of project structures to consider: 1. Functional structure As the name suggests, functional structure involves grouping all teams as per specialized functions – be it clinical, manufacturing, regulatory etc. In this model, the departmental head usually manages the team members, while coordinating the projects within functional silos.Functional structures are best for:Small-scale pharma companiesLimited cross-functional project needsWhat to look out for?On the flipside, these models are also known to create communication gaps and slow down decision-making in accelerated R&D settings. 2. Projectized structure In this model, the project manager has 100% authority over the research team and key resources, unlike the former. The PMOs assign their teams to specific projects, often outside their everyday functional roles.Projectized structures are best for:Large-scale pharmaceutical project management needstime-sensitive, high-priority, high flexibility research initiatives What to look out for?Unfortunately, this model can also cause resource duplication and higher costs, particularly when multiple projects are running at the same time. 3. Matrix structure (Best suited for Pharma)Last, but most importantly, the matrix structure brings the best of both functional and projectized structures under one umbrella. Here, teams report to both functional and project managers. This way, resources can be shared across different projects without compromising on functional supervision.Matrix structures are best for:Mid-to-large organizations juggling between numerous programsHigh operational continuity & innovation-led settingsWhat to look out for?Despite being the most common project structure in pharma R&D, it calls for strong communication and higher role clarity, to avoid conflicts or confusion. How to establish Pharma-specific structures using Planisware, Project Online and Planview?Designing and implementing project structures that suit the complex requirements of pharmaceutical R&D become easier with PPM tools. Users can configure project hierarchies, governance models, allocate resources efficiently and support decision-making at every phase. Here are your options and steps to do it:Tool #1: PlanviewFirst on our list, Planview is a major contender in terms of portfolio management and resource optimization. With a little upfront tailoring, its project templates can align well with pharma workflows.How to set up a project structure in Planview?Configure project types: label templates (for example, “Phase I asset”, “Platform”)Define swimlanes as per function (clinical, regulatory, manufacturing)Set up a clear stage-gate workflow in roadmap view with well-defined gatesAttach gate checklists and deliverables to each stageEnable demand/capacity views for cross-functional resource alignment Tool #2: PlaniswareThe Planisware project management tool is purpose-built for life sciences projects, and mirrors pharma R&D workflows like none other in this list. For example, it embeds stage gate logic at every level. The platform natively supports clinical/CMC deliverables, molecule hierarchies, as well as regulatory milestones. Here, teams can easily set up projects that map exactly to asset phases, while having total control over gates and finances.How to set up a project structure in Planisware?Create a project template with WBS (discovery, preclinical, phase 1/2/3)Embed gates after every single phase, linking back to the decision criteria and business case scorecardsAdd key deliverables (for example: IND, CMC dossiers, clinical trial authorizations)Link back the financial/resource modules to WBS for seamless budget/version trackingRoll up to portfolio to maintain proper visibility across molecules and indications Tool #3: Project OnlineLastly, Project Online is a great choice for schedule-level planning; it’s not entirely built around pharmaceutical project management structures in focus. Here, teams need to build everything from ground up. The good part, however? Integrating with Power BI, Teams, and Power Automate.How to set up a project structure in Project Online?Start by designing your custom project template Define tasks for regulatory deliverables and project milestonesTrigger notifications at gate milestones by integrating Power Automate Integrate Power BI to access customized dashboards aligned to R&D phases’ progressManage resources via PWA to assign functional roles How to choose the right tool for your project structure needs?More than chasing latest features, choose project portfolio management tools that fit your team’s needs the best. This means judging the tool by how well it aligns with your project structure, people, and strategic goals. Choose Planisware if you would:Manage a mid-to-large pharma/biotech firm with a complex R&D asset portfolioRequire built-in stage-gate models, modifiable for drug development lifecyclesNeed strong portfolio governance & what-if scenario planning at scaleUse Project Online if you would:Require a quick, at-budget tool for basic-level project scheduling/ trackingFocus on daily task management, instead of strong portfolio governanceNeed hassle-free, end-to-end automation integration (Teams, Power BI, Outlook) Go for Planview if you would:Be transitioning from project-level to portfolio-level strategy while scaling upWant to have strong resource demand vs. capacity modelling Need visualized roadmaps, high financial visibility, cross-functional planning etc. Key takeaways at a glanceProject structure informs smart portfolio decisions, points out risks early and surfaces resource conflicts Matrix project structure is the best-suited for pharma/biotech teams, as it comes with a mix of agility, governance, and resource efficiencyMore features don’t ensure success; choose your tool that best fits your team’s maturity level, complexity of portfolio assets, and other key indicators.Planisware project management tool is best for enterprise players with deep pipelines; Project Online suits mid-sized teams focused on scheduling; Planview is the go-to option for strategy-driven PMOsNeed help getting started? Let’s talk about how we can help. i2e Consulting brings 15+ years of PPM expertise. We’ve partnered with leading pharma organizations to establish fit-for-purpose project structures that drive clarity, speed, and smarter portfolio decisions. Connect with us – let’s build a project structure that accelerates your portfolio growth.

Decoding custom resource management in life sciences: A Q&A with Nicola Clear

Effective cross-functional collaboration in resource management is no longer optional—it's essential. In life sciences, where complexity, speed, and precision matter, organizations must evolve beyond spreadsheets and fragmented systems to unified, data-driven frameworks like Alloc8.Check out this blog where PPM Subject Matter Expert Nicola answers a few critical resource management questions in life sciences project management.1. How can organizations improve cross-functional collaboration in resource management?When getting started with resource management, organizations must assess PMO processes weighing the level of their maturity and interconnectedness with other functional teams. Following aspects need assessing:Resource management tools – stand-alone or connected to other PPM tools?Project management tools – are they paired with good reporting to bring the portfolio together?Is strategic portfolio management linked to integrated schedule, cost and resource planning?Is the PMO/Resource ecosystem a collection of Individual tools or a unified smart integration with data warehouse strategy?While larger teams in global pharmaceutical organizations have excellent strategic and project management tools, with resource management, often things fall short. Despite maturity in portfolio planning, most resource allocation still relies on disconnected Excel spreadsheets—built on the fly during crunch time. This slows decision-making, making it inefficient and reactive. There is also no clarity on which projects can be delivered in-house, what needs to be outsourced, or making the case for additional resources.This disconnect is a pain point in large organizations, especially in R&D environments with complex, fast-moving portfolios with tight budgets, and limited resources.To tackle this, i2e’s purpose-fit digital resource framework Alloc8 becomes critical in bridging the gap between high-level strategic planning and day-to-day resource operations.Here’s how Alloc8 helps manage resources across teams:Speeds up decision-making and improve alignment—making sure the right people are working on the right projects at the right time.Unifies siloed data across HR, PMO, and line operations into a single, real-time platform with live dashboards and alerts—giving teams a shared, accurate view of resources, skills, and timelines.A connected resource management system that integrates seamlessly into any existing portfolio ecosystem.The result?Clearer communication, faster decisions, and real collaboration—because everyone is working from the same up-to-date information. 2. What are the benefits of dedicated digital solutions like Alloc8 for named resource management?Managers often stick with Excel for resource management because it's familiar. Excel works for one-off analysis, especially in ad hoc instances, for teams of fewer than 10 people. But in large, complex R&D environments managing tens of millions $s in demand across diverse skill sets, it breaks down fast.When the workforce needs shift weekly, not quarterly, and capacity planning is based on broad skill categories, a dedicated resource management system brings critical transparency. It enables faster, data-driven decisions on what can be delivered, what needs funding, and where trade-offs are required.But the impact is not just operational, it's also qualitative. In R&D, where success depends on collaboration, a shared resource framework structure & processes reduces stress, clarifies priorities, and supports team well-being.When tools like Alloc8 genuinely makes work easier for managers and teams, adoption isn’t a battle—it happens naturally because at its heart Alloc8 improves colleague well-being - through communication and expectations supporting the individual’s role in the team sport of drug R&D. Why is this?The visibility of allocation drives meaningful & quantifiable dialogue between colleagues, managers, and teams. When an employee sees they’re assigned 40% to Project A, 30% to B, and 30% to C—and understands why Project B is the priority and who else is involved—it brings clarity and focus. This alignment drives engagement, improves delivery, and makes employees feel empowered, valued, and connected to the bigger picture. The right dialogue on resourcing between managers and employees enables trust and engagement to develop in projects. Once resources are assigned to projects, identifying skill or knowledge gaps is the next priority—something Alloc8 addresses head-on. Alloc8 identifies skill and knowledge gaps by integrating with enterprise systems and using advanced LLMs can automatically recommend targeted training and surface relevant internal knowledge. This transforms traditional scheduling into real-time workforce enablement—offering a futuristic, AI-driven way to align people with projects and goals instantly. It also uncovers cross-divisional shadowing or delegation opportunities, matching it to individual growth plans. As a result of this smarter alignment, faster upskilling employee retention improves, colleagues stay where they feel supported and developed.Refer to our white paper called “Solving the mystery of named resource management in life sciences project management for organizational effectiveness” for more. 3. What roadblocks and barriers do organizations face with digital solutions for resource management?Factors that deter life sciences organizations from doing named resources.Disparate data: Life sciences organizations often hesitate on named resources due to fragmented data across unconnected systems, overlooking how integration can unlock new insights.Manual effort: The perception that named resource management requires time-consuming manual data gathering and updates—especially in tools like Excel—can hamper adoption.Lack of automation: Without automated notifications, critical updates on resource availability and task changes are missed, causing delays and misallocations.Regulatory compliance: Concerns about compliance with country-specific labor laws, particularly in the EU, are addressed by Alloc8’s configurable framework that ensures both efficiency and regulatory alignment. 4. What are the challenges and opportunities when integrating FTE forecasting, resource allocation and time carding data for resource decisions?Challenges in cross-functional data integrationDisconnected data systems: When your PPM, HR, and contractor capacity databases don’t talk to each other, comparing forecasted demand to actual capacity becomes manual hard work. It gets worse when insourced contractors are tracked separately, making total workforce visibility nearly impossible.Mismatch in forecast vs actuals: In theory, matching forecasts to actuals is simple, but any inconsistencies in naming and activity labels across systems make it difficult. For instance, a PPM forecast may list “chemist” for Project AB-234,777, Workflow 2, Activity A, while the timecard logs “chemist” under a different activity—causing misalignment when attempting forecast variance analysis. Without governance & standardization, linking forecast, allocation, and timecard data, data analytics are slow and error prone.Challenges in data maintenance: Scientists and teams often deprioritize forecast data updates, preferring to focus on their core science work rather than business forecasts. Many distrust forecasts and view manual updates as unproductive. But with custom digital resource frameworks, much of this can be automated—named allocations can update forecasts, calendar events can feed into timecards, and AI can refine future forecasts with human-in-the-loop approvals.Opportunities in cross-functional data integrationConnected forecasting: Data integration across functions is no longer optional—it’s now technically possible and essential. As digital transformation accelerates across every part of the organization, static forecasting templates quickly become outdated.Seamless data flow: Leaders need up-to-date, connected insights to compare forecasts, allocations, and actuals in real time and to accurately measure the ROI of digital investments. Without seamless data flow between systems like HR, PPM, finance, and operations, organizations risk slow or misinformed decisions, resource misalignment, and missed opportunities.Enhancing workforce administration with Alloc8: Resource management tools like Alloc8 are designed to work with—not against—the workforce. When integrated into the broader tech ecosystem, they help reduce manual admin, surface actionable insights, and identify training, knowledge or resourcing gaps early. This frees scientists, managers, and leaders to focus on higher-value innovative work, advancing science, solving critical problems, and driving innovation all the while letting technology handle the complexity behind the scenes.5. What about resource constraints and how can life sciences companies handle them?Yes, resource constraints are BAU for most Pharma companies, it’s a sign of a healthy growing organization to have a book of work larger than the capacity. Resource constraints are a normal part of any growing life sciences organization—but they only become a problem when there's no clear process to address them. Handling resource constraints with rapid, structured action involves:Verifying if the forecasted demand truly exceeds capacity by analyzing gaps in FTE or budget by skill set. Using named resource data to pinpoint which projects are under- or over-resourced relative to priorities, enabling faster and more accurate decision-making.Resolving gaps requires aligning existing talent with strategic priorities, securing additional budget for outsourcing or hiring, or making tough trade-offs on lower-priority projects.Alloc8 makes this decision-making process real-time by flagging skill shortages, overbookings, and workload bottlenecks. When teams see that managers are actively identifying and addressing resourcing issues, it builds trust, reinforces a team-first culture, and supports both performance and well-being. As a result, organizations can manage workload demands to balance productivity and colleague well-being. 6. Explain the 4Cs to optimize named resource allocation Organizations have the best interest of business and employees when they use 4Cs to optimize named resources. Culture: When employees trust the governance process and the leadership backing is strong, named resource allocation is seen as fair and beneficial.Communication: Regular one-on-ones, face-to-face updates, and AI-powered task automation ensure everyone stays aligned and knows their priorities.Connected: Linking resource plans to business strategy, OKRs, and KPIs ensures every allocation supports high-impact goals first.Change Management: For named resources to deliver as much time needs to be invested in stakeholder buy-in and training as the digital tech solution to ensure lasting adoption and effective implementation. Forget this last step at your peril!7.How can organizations solve demand forecasting disagreements?Purist vs directional, correct?Forecasting demand is never 100% precise because it’s based on general templates and estimates, often includes extra buffer time, often forgets to account for tasks like capturing lessons learned—so the over- and under-estimations tend to even out in the end. Managers recognizing this can usually reach forecasting agreements.Skill effectiveness varies too. For instance, a newer employee may deliver at 50% efficiency compared to someone experienced, making named allocation essential for accuracy. Historical data showing consistent over-forecasting—like deliverables met despite named resources being 20% below forecast—helps refine future predictions and find the true average algorithm.When disagreements arise, resource managers can use data from named allocations and timecards to show how work was actually delivered guiding more accurate, AI-supported future forecasting.