Project Lifecycle Analysis

How can we ensure that #ArtificialIntelligence projects are not just technically sound but also ethically responsible and organizationally aligned? This paper presents a comprehensive life cycle for the design, development, and deployment of #AI systems, known as the CDAC AI life cycle. It starts with a risk assessment and moves through design, development, and deployment phases. The life cycle consists of 19 constituent stages that address not only the technical aspects but also the ethical and organizational contexts. It aims to fill the gaps in existing methodologies by providing a more holistic approach to AI project management. 1️⃣ The paper introduces the concept of preliminary risk assessment, focusing on privacy, cybersecurity, trust, explainability, robustness, usability, and social implications. This risk assessment is essential for understanding the broader impact of AI projects. 2️⃣ The CDAC AI life cycle is divided into three main phases: design, develop, and deploy. Each phase requires specific expertise, such as AI/data scientists for the design phase, AI/ML scientists for the development phase, and AI/ML engineers for the deployment phase. 3️⃣ The paper emphasizes the importance of ethics and governance in AI projects. It suggests that ethical considerations should be integrated into the AI life cycle, especially in the design and deployment stages. Reading this paper is valuable for anyone involved in AI projects, from developers to decision-makers. It provides a structured approach that ensures ethical and organizational alignment, making it a must-read for responsible AI development. ✍🏻 De Silva, Daswin & Alahakoon, Damminda. (2022). An artificial intelligence life cycle: From conception to production. Patterns 3, 100489. DOI: 10.1016/j.patter.2022.100489 ✅ Sign up for our newsletter to stay updated on the most fascinating studies related to digital health and innovation: https://lnkd.in/eR7qichj

Good news: the awareness and use of empirically-based parametric analysis of systemic project risk is growing. However, given recognition that the level of scope definition (an artifact of phase-gate project systems) is a dominant systemic risk, some ask why the method needs to be applied post-sanction? Here are some key points as to why the method must be used throughout the project life cycle: 1) the parametric model includes a number of uncertainty drivers, of which the level of scope definition is just one (albeit a big one). In particular, the level of complexity, technology, and degree of bias as well as team development. No matter how well defined the scope is, these add uncertainty. Actually, technology is nearly as dominant as definition (granted, most projects do not push any technology buttons). 2) the level of scope definition is optimal the day a project is sanctioned. But experience shows that the project system usually starts to break down on day two (often because the board in their decision made changes) and throughout execution due to team turnover, poor change (scope) management, communication breakdown, unreliable control baselines, forecasts that no longer reflect reality on the ground. The larger and longer the project, the worse it can get (to the point of chaotic behavior and blowouts). 3) a parametric model is not a "contingency tool", it is a QRA tool. It generates a distribution of uncertainty such that even if the mean or p50 is approaching zero, the p90 will not. Those who say it does not matter when scope is well defined are likely not concerned with or do not understand owner investment decision making which must consider the risk profile, not the p50. 4) the method is not only for QRA, but to support project system governance/QA. Using the tool on every project, at every decision gate and at key points during execution will continually challenge and drive home awareness of potential weaknesses and gaps in the project system and organization (as much as some may not want to hear it). This last point focuses on long term capital management risk mitigation; i.e., something a one-off risk analyst may not care much about. I hope this clarifies why one should apply the parametric method throughout the project life cycle, and across all projects in the capital portfolio. It is important because project discipline breaks down. But also because one has to have the multi-dimensioned, long, system and process improvement view which is more common to owners (and lower interest to parties doing one-off hit and run analyses).

Turning AI dreams into real impact isn't an overnight thing; it's a journey. 🛣 Like any new technology, please don't FOMO into it. Just like your low-code and automation investments beforehand, there's a lifecycle an AI project has to go through. Here’s how the lifecycle unfolds: ➡ Ideation: It all starts with a spark. Identifying problems that AI can solve. ➡ Assessment: Not every idea is gold. Rigorous evaluation ensures feasibility and impact. ➡ Design: Blueprinting the solution. This is where creativity meets technology. ➡ Build: Rolling up sleeves to turn designs into working models. ➡ Test: Ironing out the kinks. Ensuring the model behaves as expected. ➡ Deploy: Bringing the model into the real world. The moment of truth. ➡ Measure: The job isn’t done post-deployment. Continuous monitoring to gauge success (both operational and business) and areas for improvement. Each step is a critical piece of the puzzle. Navigating this lifecycle successfully, and at scale, is key to transforming ideas into real, tangible, and high-value solutions. #AI #Innovation #DigitalTransformation #AIGovernance

As CCS technologies continue to scale up, it's critical to accurately quantify the carbon footprint and emissions reduction potential of these projects. A new report from IOGP provides an overview of the methodologies, tools, and best practices for conducting lifecycle assessments (LCAs) of CCS projects. Key takeaways: 📢 1. LCAs for CCS projects should follow established ISO standards like ISO 14040, 14044, and 14064 to ensure a robust, consistent approach. 📢 2. Defining the appropriate system boundaries is crucial - this includes accounting for emissions from capture, transport, and storage operations. 📢 3. Establishing a baseline scenario is important to demonstrate the "CO2 avoided" through the CCS project. 📢 4. Shared CO2 transport and storage networks between multiple emitters add complexity to the LCA - allocation approaches like proportional or Scope 3 accounting should be considered. 📢 5. LCAs should be conducted throughout the lifecycle of a CCS project - from planning and development to operations and decommissioning. 📢 6. Various software tools and emissions factor databases are available to support the LCA quantification process. Careful LCA accounting is essential for demonstrating the true emissions reduction benefits of CCS technologies. This report provides a helpful overview for CCS project developers, policymakers, and other stakeholders. #CCS #CCUS #LCA #CarbonBaseline #CO2 #Scope3 #IOGP #Decarbonization

Almost everything we do in data follows a lifecycle. Below are the main ones in data management. Knowing them is very helpful when designing new processes, frameworks, and operating models. 1. Data Lifecycle - Describes the journey of data from creation to destruction. Stages: ~Creation. Data is created at the source. ~Collection. Data is captured and gathered. ~Processing. Data is transformed and prepared. ~Storage. Data is persisted in systems. ~Analysis. Data is analyzed to generate insights. ~Sharing. Data is distributed to consumers. ~Archiving. Data is retained for long-term or compliance needs. ~Destruction . Data is securely deleted at end of life. 2. Metadata Lifecycle: ~Metadata Collection. Metadata is captured from systems and processes. ~Metadata Storage. Metadata is stored in repositories or catalogs. ~Metadata Access. Metadata is made discoverable. ~Metadata Consumption. Metadata is used for governance and decision-making. ~Metadata Aging. Metadata becomes obsolete and retired. 3. Data Engineering Lifecycle Focuses on building and operating data pipelines and platforms. ~Generation. Data is produced by source systems. ~Storage. Data is stored in appropriate platforms. ~Ingestion. Data is moved into processing environments. ~Transformation. Data is cleaned, enriched, and structured. ~Serving. Data is delivered for analytics, BI, or applications. 4. Data Analytics Lifecycle Describes how data is transformed into insights and actions. ~Problem Definition. Define the problem. ~Data Requirement, Collection & Access. Gather and access relevant data. ~Data Cleaning & Preparation. Prepare data for analysis. ~Exploratory Data Analysis. Explore patterns and anomalies. ~Advanced Analysis & Modeling. Generate insights using models. ~Visualization & Communication. Communicate insights visually. ~Implementation & Monitoring. Operationalize and track results. 5. Data Product Lifecycle Describes how data assets are built and managed as reusable products. ~Design. Define the product vision, users, and requirements. ~Develop. Build pipelines, models, and supporting components. ~Deploy. Release the product for consumption. ~Evolve. Improve and adapt the product based on feedback and usage. Few of my favorites: 6. Continuous Improvement Lifecycle (PDCA) Widely used in data quality management and operational improvement. ~Plan. Identify the problem, define goals, and create a plan. ~Do. Implement the plan on a small scale. ~Check. Analyze results and compare them with goals. ~Act. Standardize improvements or adjust and repeat the cycle. 7. Six Sigma Lifecycle (DMAIC) A data-driven approach to improving existing processes. ~Define. Clarify the problem and goals. ~Measure. Understand current performance. ~Analyze. Identify root causes of defects. ~Improve. Implement solutions. ~Control. Sustain improvements over time. What other major ones did I miss?

🔐 Integrating OT Cybersecurity Throughout the Project Lifecycle: A Comprehensive Approach In today’s interconnected landscape, ensuring the security of Operational Technology (OT) systems is no longer optional—it’s a critical imperative. From risk assessments to network architectures, the importance of embedding OT security into every phase of a project cannot be overstated. 🌐 The recently published paper by Wintershall Dea highlights a case study of subsea gas production facilities, showcasing how integrating OT security from the very beginning leads to operational resilience and compliance with industry standards. 🛡️ This approach not only minimizes vulnerabilities but also fosters success across the project lifecycle. Key Takeaways: - Incorporate OT cybersecurity considerations in conceptual design and feasibility studies. - Conduct comprehensive risk and vulnerability assessments during execution phases. - Leverage virtualization technology to improve control and security over third-party access. For those in critical infrastructure, this paper is a must-read to understand the practical application of OT security measures that ensure robust and secure operations. 💡 What proactive OT cybersecurity strategies are you employing in your projects? #Cybersecurity #OTSecurity #CriticalInfrastructure #RiskManagement #ProjectLifecycle #IEC62443

🚀 𝗧𝗵𝗲 𝗣𝟴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 A modern framework for a complex world. Every plan looks perfect, until reality shows up. In today’s shifting business landscape, planning once and executing perfectly no longer exists. The best Project Managers don’t just deliver, they adapt. That’s why we built the 𝗣𝟴 𝗣𝗠 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲, a pragmatic, eight-step model that helps leaders turn complexity into clarity and keep outcomes aligned with evolving priorities. 𝗧𝗵𝗲 𝟴 𝗦𝘁𝗲𝗽𝘀 1️⃣ Define Scope 2️⃣ Establish Strategy & Governance 3️⃣ Create Initial Plan 4️⃣ Execute Plan 5️⃣ Monitor & Report Status 6️⃣ Replan & Redesign 7️⃣ Support & Business Stabilization 8️⃣ Close Project Unlike traditional models. 𝗦𝘁𝗲𝗽𝘀 𝟰–𝟲 𝗳𝗼𝗿𝗺 𝗮𝗻 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗰𝘆𝗰𝗹𝗲, Continuously refining direction as conditions change, enabling real-time responsiveness and resilience. 𝗚𝗿𝗼𝘂𝗻𝗱𝗲𝗱 𝗶𝗻 𝗺𝗼𝗱𝗲𝗿𝗻 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀: • 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Refine, learn, and realign as context shifts. • 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Enable clear, timely decisions amid changing priorities. • 𝗩𝗮𝗹𝘂𝗲-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗙𝗼𝗰𝘂𝘀: Measure success by business impact, not activity. • 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗔𝗴𝗻𝗼𝘀𝘁𝗶𝗰: Seamlessly fits Scrum, Waterfall, or hybrid. • 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻-𝗗𝗿𝗶𝘃𝗲𝗻 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲: Lead with logic, transparency, and data. • 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗘𝗺𝗽𝗵𝗮𝘀𝗶𝘀: Plan for the phase beyond “go-live.” In modern project management, 𝘁𝗶𝗺𝗲𝗹𝘆 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗼𝗳 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲. 🧭 𝗧𝗵𝗲 𝗣𝟴 𝗣𝗠 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: 𝗕𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘄𝗵𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗰𝗹𝗮𝗿𝗶𝘁𝘆 𝗮𝗺𝗶𝗱 𝗰𝗵𝗮𝗻𝗴𝗲. How does your team adapt when the plan shifts? Follow Pragintion PM to learn how to become the 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗱𝘃𝗼𝗰𝗮𝘁𝗲 senior leadership needs to capture intended business value.

💡 Project Life Cycle of a Business Analyst – More Than Just Gathering Requirements! Behind every successful project is a Business Analyst connecting the dots between business needs and technology solutions. Ever wondered what a BA's journey looks like across the project lifecycle? Let's breaks it down into 6 key stages: 1️⃣ Initiation • Identify business problems/opportunities • Engage stakeholders • Conduct feasibility analysis • Define high-level business requirements 2️⃣ Requirement Gathering & Analysis • Organize workshops, interviews, or surveys • Document Business Requirements (BRD) • Create Process Flow Diagrams • Prioritize requirements 3️⃣ Requirement Documentation & Validation • Draft Functional Specifications • Create Use Cases & User Stories • Obtain stakeholder sign-off • Perform Gap Analysis 4️⃣ Design & Collaboration • Collaborate with Developers & Architects • Clarify requirements • Update documentation (if needed) • Participate in Sprint Planning or Design Reviews 5️⃣ Testing & UAT Coordination • Define Test Scenarios • Support QA teams • Conduct UAT sessions • Log and track issues 6️⃣ Go-Live & Post-Implementation Support • Assist with Go-Live activities • Monitor production issues • Create User Manuals & Training Documents • Conduct Post-Implementation Review ✅ But here's the secret – the BA's job doesn't end at delivery... Change Requests, Impact Analysis, and Continuous Improvement keep the loop going! This visualization represents how BAs drive projects forward, align stakeholders, and turn business problems into solutions 😊 #BusinessAnalysis #ProjectLifeCycle #LifeOfABA #BusinessAnalyst