Empowering Clinical Trial Decisions with Data-Driven Decision Management (DDDM)

What is Strategic Portfolio Management (SPM) and why should it matter in life sciences PPM

In life sciences, every portfolio decision shapes the future of innovation affecting R&D timelines, regulatory milestones, and patient impact. Yet many organizations still struggle to connect day-to-day project execution with strategic intent. Disconnected systems, siloed data, and limited visibility into capacity and forecasts make it difficult to see where value is truly being created.Strategic Portfolio Management (SPM) changes that. It elevates traditional project and portfolio management into a strategic capability one that unites scientific, financial, and operational insights to drive better, faster, and more confident decisions. For pharma leaders, SPM is not just about managing portfolios, it’s about steering the organization toward measurable outcomes, optimized investments, and sustainable competitive advantage.In this blog we will unpack the intricacies of SPM, it’s connection with PPM and why life sciences companies need to take SPM seriously. What is Strategic Portfolio Management (SPM)?Strategic portfolio management is a set of business capabilities, processes and supporting portfolio management technologies. It helps pharma leaders create a portfolio of strategic options that focus an organizations resource on portfolios that most align with the strategic goals. This way, key decision-makers can optimize their planning, resource management and budget allocation to ensure every initiative drives a strategic purpose. Why do senior leaders need strategic portfolio management?The #1 challenge that most pharmaceutical companies struggle with, is the silos created by misalignment between strategic objectives and day-to-day execution. Two major gaps causing this issue are: Underlying gap 1: Strategic goals are not clearly defined or communicated to executive teamsConsequence: Siloed decision-making Underlying gap 2: Project-related information, resources data etc. are scattered through multiple disconnected systemsConsequence: Information silos leading to failed PPM efforts Without SPM, functions end up competing for the same resources’ availability, leading to a crisis. On the other hand, leaders lack visibility on high-value/low-risk programs aligned with strategic objectives, and risk-prone/low-value projects to be cancelled – an even greater crisis.The end goal of SPM is implementing a series of processes and tools that facilitate decision making in: Project prioritizations or cancellationsEfficient planning of capacity and resource management so that projects that are of vital importance always have the resources they need.Making informed decisions when it comes to opening or closing investment. How is SPM different from Project portfolio management (PPM)? SPM frameworks are used to make cross-portfolio governance decisions, often with C-suite participation. At i2e Consulting, we approach SPM maturation with an expert-led SPM maturity assessment, where all key stakeholders and decision makers are interviewed.On the other hand, PPM is used to make the granular-level, everyday project/program governance decisions. This involves selecting and prioritizing tasks within the portfolio, allocating resources and streamlining the project lifecycle on a short/mid-term timeline. Here is a brief outline of SPM vs. PPM AspectSPMPPMPrimary focusAre we focusing on the right initiatives?Are we doing the initiatives right? ObjectiveMaximize portfolio value, pipeline success, and strategic impact on organizational goalsOptimize trial timelines, resource utilization, and budget adherence ExecutionTop-Down strategyBottom-Up delivery Decision basisStrategic outcomes: risk-adjusted ROI, probability of success, therapeutic and market alignment Project-level performance: milestones, patient recruitment, cost, schedule VisibilityEnterprise-level view of portfolio alignment, priorities, and value deliveryProject-level reporting and resource tracking End goalEnsure the right portfolio of programs drives pipeline success, commercial value, and long-term growth Deliver projects and trials on time, within budget, and compliant Read more: How PPM can increase operational efficiency for faster drug development SPM vs. PPM- not a competition but a partnership for overall excellence Think of planning a vacation and packing a suitcase. PPM: You carefully pack your clothes and essentials, making sure nothing is forgotten.SPM: You decide where to go, how long to stay, and what activities to do ensuring the vacation is enjoyable, efficient, and meets your goals. Similarly, SPM sets the direction such as outcomes, investment guardrails which makes sure the portfolio is in alignment with the strategic goals. Whereas PPM breaks the direction into portfolios and projects, schedules tasks, allocates resources, and tracks progress.SPM and PPM work hand in hand in a closed loopSPM- Top downPrioritizes investments, and initiatives, and PPM plans and delivers.PPM feeds SPM with progress, risk and feedback to adjust the strategy and the portfolio mix. This is a closed loop, and for organizations who can achieve this seamless data transfer between SPM and PPM, they will avoidGreat execution of the wrong projectGreat strategy, but poor execution Why should SPM matter more in Life Sciences PPM? Life sciences projects have some unique challenges. These make SPM especially valuable when layered on top of traditional PPM practices.High uncertainty and long timelines:The drug or medical device development process commonly takes several years and even decades. The uncertainties are significant scientifically, clinically, regulatorily, and in the market. A project that was once a good candidate to meet value or data objectives 5 years ago could now be irrelevant or at least be at risk of being displaced by competitive options and lack of relevance. SPM allows you to course correct, move resources around or stop what once was a valid strategic project. Cross-functional and cross-discipline dependencies:Life sciences portfolios can include discovery science, clinical trials, CMC, device development, regulatory, commercial planning, etc. All these components are highly interactive/connected. SPM can promote discussion about the interrelationships that bind so many of the disciplines together and prevent interdependent support functions from being left behind (e.g., manufacturing scale-up to manufacture). SPM is also a precursor to advanced cross-silo coordination. Scarcity of resources and cost pressure: There are multiple constraints for any portfolio: skill set capabilities, trial site capacity, R&D budgets. SPM can allow you best allocate these resources to the projects that are most important to overall strategy and business objectives. This is critical to achieving the organizational results, rather than prioritizing the flashiest program in the portfolio.”Real-time visibility and scenario planning:Because the environment (science, regulation, markets) changes, decision-makers need up-to-date dashboards and “what-if” modelling. SPM platforms support scenario analysis (e.g., “If we delay Project A, what happens to cash and resources?”) and real-time insight across all projects.Regulatory and compliance constraints:In life sciences, you have multiple stages in a project that must be regulated (e.g., clinical phases, submissions). Many of the decisions made on a project must have legitimate audit trails, documentation of justification and regulatory follow rules. There are defined best practices to help organizations think through these challenges. Take your first step towards SPM maturation with i2eWhether you’re managing a few regional projects or working on global-scale pharma portfolios, we bring 15+ years of life sciences PPM success and SME expertise to help you reach a stronger future. With the APEX framework – our life sciences-specialized SPM maturity framework – we help you spot hidden inefficiencies restricting your growth potential and guide you towards the highest maturity level with a strategic roadmap and expert guidance. Book a 1:1 SPM assessment today to get started. Article by: .profile-image img{ width: 200px !important; height: 200px !important }

Microsoft Project Online is retiring: What’s next for organizations?

Microsoft Project Online RetirementMicrosoft has officially announced the retirement of Project Online, marking a major shift in how organizations manage projects and portfolios in the Microsoft ecosystem. While this move may seem disruptive, it’s also an opportunity to modernize your project management landscape with more agile, connected, and scalable solutions.What is retiring and what is not within the MS Project Management ecosystem CategoryProduct / Component StatusProject OnlineMicrosoft Project Online (part of Project for Web and Project Online Plans 1–5) Retiring (officially retiring on September 30, 2026)Project ServerProject Server Subscription Edition (on-premises)Not retiring (Microsoft has committed to supporting it through at least July 14, 2031)Project Server 2019 / 2016 / 2013Legacy on-prem versionsRetiring / Out of mainstream supportPlanner PremiumMicrosoft Planner (and Planner Premium)Active / ExpandingProject Desktop ClientMicrosoft Project Professional (Desktop app)Still available but staticWhat should be your next steps?Our PPM experts identified four key paths forward, some cover Microsoft project alternatives within the Microsoft ecosystems, where are some options go outside Microsoft. Here is a detailed look at their pros, cons, and technical implications to help you make an informed choice. 1. Move to Microsoft Planner with premium capabilities / Power Platform extensionsMicrosoft Planner has evolved beyond a simple task board. With Planner Premium (built on Microsoft Project for the web) and Power Platform integration, organizations can create scalable, low-code project management environments that automate workflows, connect to data sources, and deliver analytics. They can easily recreate their MS Project plans within Planner Premium or extend them using Power Platform components for automation and reporting.Pros:Modern UI and simplicity: Intuitive, cloud-native experience with integration into Teams and Microsoft 365.Automation and customization: Power Automate, Power Apps, and Dataverse enable custom workflows and reporting.Scalable and future-ready: Microsoft’s strategic focus is clearly on the Power Platform–Planner stack, ensuring continued innovation.Unified data model: Leverages Dataverse for consistent data handling and analytics via Power BI.Cons:Migration complexity: Data structures in Project Online differ from Planner/Dataverse, requiring careful mapping and reconfiguration.Feature gaps: Advanced portfolio-level functions (like EVM or multi-dimensional resource planning) require custom builds or add-ons.Change management effort: End users need to adapt to new workflows and interfaces.Cost implications:Low to moderate initial cost: Most Planner Premium and Power Platform capabilities come under existing Microsoft 365 or Power Platform licenses.Implementation costs vary: Custom app development, workflow setup, and Power BI dashboarding can add moderate consulting expenses.Ongoing savings: Reduced infrastructure costs and seamless integration minimize total cost of ownership (TCO). 2. Move to Project Server Subscription Edition (On-Premises)For organizations not ready to go fully cloud-native, Microsoft Project Professional and Project Server Subscription Edition offers a supported, on-premises continuation of Project Online capabilities.Pros:Continuity with existing processes: Familiar interface, enterprise resource planning, and enterprise custom fields, and project detail pages remain intact.Control and compliance: Data stays on-premise—ideal for regulated industries with strict data residency requirements.Integration consistency: Existing add-ins, reports, and integrations can often be retained with minimal rework.Cons:Limited innovation: Microsoft’s development focus has shifted to the cloud; few routine updates are expected.Higher maintenance overhead: Infrastructure, patching, and scalability remain your responsibility.Scalability constraints: Not ideal for distributed or hybrid teams needing mobile/cloud access.Cost implications:High capital cost: Requires on-prem servers, SQL licensing, and ongoing hardware maintenance.Lower migration cost: Minimal configuration changes compared to cloud migration.Higher long-term cost: IT resource overhead, patching, and version upgrades add recurring expenses. 3. Hybrid or mixed approachMany enterprises choose a hybrid setup, using Planner and Power Platform for agile, team-level project tracking while retaining Project Server for enterprise-level program management.Pros:Balanced modernization: Gradual migration minimizes disruption.Best of both worlds: Agile teams get flexibility while PMOs retain robust governance tools.Phased adoption: Allows time to retrain teams and adjust processes.Cons:Integration complexity: Requires connectors or middleware to keep systems in sync.Dual administration: Managing both environments increases oversight effort.Data consistency risks: Without clear governance, data integrity may be affected.Cost implications:Moderate setup cost: Investment in integration tools and Power Platform customization.Reduced upfront burden: Avoids full migration costs by spreading transformation over phases.Higher operational cost: Running and maintaining two environments can increase ongoing spend. 4. Switch to third-party enterprise PPM toolsFor organizations looking for end-to-end project and portfolio management with built-in financials, resource planning, and risk management, third-party tools like Planisware, Clarity, Smartsheet, Monday.com, Planview, OnePlan or Wrike offer comprehensive alternatives.Pros:Rich PPM functionality: Mature features for scenario planning, capacity management, and financial tracking.Industry-specific capabilities: Tailored solutions for pharma, engineering, or R&D.Dedicated vendor innovation: Regular updates and roadmap-driven enhancements.Embedded AI support: Built-in AI agents to streamline everyday project management activities and decision-making.Cons:High licensing cost: Enterprise-level subscriptions can be significant.Complex migration: Requires data mapping, validation, and process reengineering.Reduced Microsoft integration: Some features may require additional connectors or third-party middleware.Cost implications:High upfront investment: Licensing, implementation, and integration costs can be substantial.Predictable recurring costs: Annual subscriptions and vendor-managed support simplify budgeting.Potential savings in efficiency: Rich automation and portfolio analytics can deliver ROI over time. Make the right choice with i2eAt i2e, we help organizations evaluate their Project Online footprint, assess migration complexity, and select the right modernization path—balancing functionality, cost, and long-term strategy. Check out our 7 steps migration roadmap.Our consultants specialize in Microsoft PPM modernization, Power Platform automation, and data integration, ensuring a smooth transition with minimal downtime. Whether your goal is cost optimization, enhanced agility, or future scalability, we design a roadmap that aligns with your business priorities.

Is your data ready for generative AI: a guide for life sciences organizations  

Generative AI (Artificial Intelligence) comes with a promise of offering unparalleled opportunities to life sciences organizations. Yet, the success of the journey grips on how data ready is your company for gen AI. From improving drug discovery to enhancing trials and devising marketing strategies, there is a vast potential to take benefit from the use of gen AI applications. However, the hurdle most Chief Data Officers (CDOs) and data leaders in the life sciences domain are facing is managing data and scaling AI use cases. Now, they need to focus on making changes within the data and the architecture for gen AI to produce meaningful results for the business. In this blog, we explore the importance of making data ready for generative AI and actionable insights for life science companies to navigate the generative AI data with confidence. Importance Of Data Quality for Gen AI Applications Data quality affects the accuracy, dependability, and consistency of algorithmic patterns and results of gen AI applications. To ensure its standards, organizations should build strategies comprising of data validation and data cleansing methods. Data validation refers to authenticating the accuracy of information through different facets. It includes verifying the data for errors, patterns, and inconsistencies and ensuring it runs parallel to the organizations' standards. While the data cleansing process is implemented to fix the errors found during validation, it involves eliminating duplication, correcting errors, and standardizing the data for overall consistency. Data validation is decisive for Gen AI applications as it makes sure the data presented to AI models is reliable, consistent, and precise. Without validation, the input data could have inconsistencies, biases, and errors, leading to variable and unreliable AI-led output. These make sure that AI models are trained to offer reliable and high-quality data for organizations to lay their problem-solving decisions. What Constitutes Data Readiness for Generative AI Data readiness for gen AI involves multi-layered tactics with a few components that are critical for organizations. Next, let us look at the steps involved in preparing data for gen AI usage. Steps to Prepare Data for Gen AI To leverage the power of Gen AI, the data should be prepared well. Here are the four critical steps to prepare life sciences data for gen AI. Data acquisition and creation The fundamental practice of preparing data for gen AI starts with acquiring data from diverse datasets and curating relevant data. The data should consist of all the critical components which are essential to generate the right response. For example, while acquiring data for drug development care should be taken to include chemical structures, target proteins, biological assays, drug reactions, and trials. Data can be obtained from academic literature, internal records, public repositories, and proprietary databases. The next focus should be on the creation of data by cleaning the acquired data and standardizing it to maintain quality and consistency. At the same time, the steps should involve correcting errors, eliminating duplication, and regulating data formats. Additional data including patient demographics, assay conditions, and molecule identifiers should also be analyzed and cleared for further data interpretation and training models. Data cleansing and preprocessing This step involves improving the available data, particularly when it is disorganized or limited. For generating superior results from gen AI cleansing and preprocessing methods must be applied. Data synthesis is the method implemented, which involves creating new data samples based on the available data. A few generative AI techniques at this stage include interpolation and extrapolation, which means creating synthetic data as per the statistic models. Data synthesis is a broad concept that constitutes methods to create new data and is not limited to merely resampling. Gen AI models like generative adversarial networks and variational autoencoders can synthesize data samples from the curated data. Nonetheless, it should be ensured that the data reflects the real-world annotations. Feature engineering and selection This is a critical stage as the data collected must go through sifting, where the raw data transforms into a standard format appropriate for training gen AI models and contribute to visionary performance. For example, the data for drug development should undergo changing biological sequences for numerical embeddings, encode chemical structures, and extract information from clinical data. Some of the techniques involved at this stage are normalization, dimension reduction, and selection for computational efficiency. Moderation and model building Life sciences data for gen AI should be validated and facilitated for model training. This step involves adhering to quality based on accuracy and reliability for AI models. Conducting experiments, validating datasets, and checking for model robustness are a few more steps to assess the performance of gen AI models. The approach begins with a base model and then passes through layers of SFT (Supervised Dine Tuning), RLHF (Reinforcement Learning from Human Feedback), and Proximal Policy Optimizations. Another crucial aspect of model building is moderation, which helps to generate relevant data by eliminating socially irresponsible answers. SME verification Finally, Subject Matter Experts (SMEs) are required to verify the final data samples and ensure it aligns with drug discovery and biological plausibility. Adding a human element is necessary to validate the gen AI responses and test the data quality. Some other measures like implementing control mechanisms and data governance are critical to maintain reliability and integrity. Conclusion In the era of AI-driven world, the potential of data readiness in leveraging pharma organizations should not be overlooked. From enhancing drug discovery processes to clinical trials, and coming up with unparalleled marketing strategies, gen AI applications have the potential to energize the pharma organizations. Adhering to meticulous data preparation through advanced practices and accelerating pharma organizations to use gen AI’s full potential and result in breakthroughs and innovation in the healthcare and drug development industry. The future is gen AI and i2e Consulting can help you prepare for it. Our data scientists are experts in preparing data for gen AI models. We can also advise on implementing control mechanisms and data governance practices.