Iterative Project Management Processes

Ever have 3.5x pipeline coverage and still miss by 20%? Well, here's a potential solution for ya. To be clear, this stuff happens often, and it tends to be a surprise to some leaders. Mainly because lots of folks still think that pipeline VOLUME is the same as pipeline HEALTH. If you're looking at your pipeline and don't really have a clue about what's in it AND you're comfortable with a bit of math, here's a different way to gauge your pipeline health. You can call it something like the "30-Point Quality Score." I'm not a marketing whiz, so feel free to come up with something more creative if you want. Anyway, here's how it works...instead of tracking gross dollar coverage, score each opportunity across six dimensions (0-5 points each, 30 points max): 1. Stage velocity (0-5 pts): - 0 pts = Sitting 3x longer than average cycle. - 3 pts = At average cycle length. - 5 pts = Moving faster than average. 2. Multithreading (0-5 pts): - 0 pts = Single contact. - 3 pts = 2-3 contacts. - 5 pts = 4+ contacts across buying committee. 3. Source quality (0-5 pts): - 0 pts = Cold inbound form fill. - 3 pts = Marketing qualified lead. - 5 pts = Rep-generated with champion. 4. Budget confirmation (0-5 pts): - 0 pts = "We think we have budget." - 3 pts = "Budget approved, waiting on timing." - 5 pts = "Budget allocated with PO number." 5. Intent signals (0-5 pts): - 0 pts = Passive engagement. - 3 pts = Responding to outreach. - 5 pts = Multiple stakeholders actively engaged. 6. Next step commitment (0-5 pts): - 0 pts = Vague "let's reconnect." - 3 pts = Calendar invite scheduled. - 5 pts = MAP with named owners. So your total quality-weighted pipeline = Sum of (deal size x quality score/30). For example: - Deal A: $100K × (25/30) = $83K quality-weighted. - Deal B: $100K × (12/30) = $40K quality-weighted. Now you can start tracking quality-weighted coverage instead of just gross coverage. I mean, you can keep celebrating 500 opportunities at $50M total value if you want. But it might be more effective to start tracking 150 opportunities with validated champions, defined next steps, and budget confirmation. I'd personally recommend the latter, mainly because your board doesn't care how many deals you forecast. They care how many you close. Math doesn't lie. Even when your pipeline does.

🎄 Day 14 of the #AdventOfOR 2025! The single biggest mistake in optimization projects? Engaging stakeholders once. Most teams nail the "Early" part (kickoff, problem framing, initial requirements). But then they disappear into complex code. Weeks later, they return with the perfect solution... but trust has eroded. Engagement isn't a single event. It's a continuous cadence: Early AND Often. Why is this continuous interaction essential? 🤝 Maintains trust: Consistent updates prevent the project from becoming a black box. 🎯 Ensures relevance: Requirements shift; regular check-ins keep your model aligned with business reality (just like we got new requirements on Day 12!). 🪡 Drives adoption: Stakeholders own the solution when they help build it. The secret to making it work is lowering the cost of understanding the model's progress. But you don't need to do heavy presentations; do easy, frequent demos with tools that help: 🔹 GAMS MIRO for interactive apps stakeholders can explore 🔹 Streamlit or Taipy for quick Python dashboards 🔹 Nextmv for comparing runs and sharing scenarios When showing progress becomes easy, you'll do it more often. When you do it more often, trust compounds. 🫵 Your turn: What's the single biggest piece of friction that currently stops you from sharing model progress (work-in-progress, not final results) with your stakeholders more often? (e.g., "It takes too long to clean the output," "We lack visualization tools," "I only share final numbers.")

You can’t call it partnership if stakeholders only hear from you once before launch. True engagement isn’t a courtesy email. It’s about making stakeholders 𝘱𝘢𝘳𝘵𝘯𝘦𝘳𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘱𝘳𝘰𝘤𝘦𝘴𝘴 from day one to follow-through. 4 shifts that make the difference: 1. Map before you move Not all stakeholders need the same level of attention. Use mapping tools to identify who has influence, what they care about, and how they prefer to engage. 2. Align objectives early Don’t wait until the end to prove impact. Bring stakeholders into planning to set KPIs, success metrics, and business outcomes together. 3. Keep communication alive Use clear, jargon-free updates. Share progress, invite feedback, and celebrate wins. Trust grows when stakeholders feel part of the journey. 4. Champion transfer, not just learning Make managers and sponsors active player, e.g. mentors, accountability partners, and reinforcement leaders. Because learning in the classroom means nothing if it doesn’t show up on the job. When engagement is tailored this way, L&D stops being a service provider… and starts being a strategic driver of business results. A question for you: What’s worked best in your experience: mapping, alignment, communication, or transfer support? _____________ High functioning ≠ high capacity. I consult with L&D teams to turn busyness into business impact.

Are you optimizing for traction… or just counting trophies? Trophies (or outcomes) are the obvious metrics: followers, subscribers, sales, launches, “big wins.” 🏆 Traction (or velocity) is the quieter stuff: the movement in behaviour that tells you your message is landing. 🏃 Most marketing metrics fall into those two buckets: Velocity and Outcomes. We tend to obsess over outcomes. But velocity metrics often tell you sooner if you’re on the right track. Outcomes = “Did it work?” These are the scoreboard metrics: ▪️ Revenue ▪️ Qualified leads ▪️ Booked calls ▪️ Conversions (trial → paid, cart → checkout, etc.) ▪️ Followers They matter because they’re the point of the work. If outcomes don’t move, the marketing isn’t doing its job (or the offer/sales process needs fixing). The catch: outcomes are lagging indicators. They show up after a bunch of things go right. Velocity = “Is it starting to work?” Velocity metrics measure movement in behaviour — signals that people are getting closer: ▪️ Click-through rate changes (especially to “money pages”) ▪️ Save rate (people want to keep it) ▪️ Share rate (people want to be associated with it) ▪️ DM/inbound mentions (“saw your post about X…”) ▪️ Return visitors/time on page shifts ▪️ Conversion rate improvements even if volume is flat (less leakiness) These matter because they let you diagnose and iterate before you wait 60–90 days for revenue to tell you what happened. Velocity metrics are directional, not definitive. They answer: Are we earning attention? Building intent? Reducing friction? Here’s why you need both: ▪️ If you track outcomes only, you’ll change strategy too late (or give up too early). ▪️ If you track velocity only, you can accidentally optimize for “interesting” instead of “effective.” And here’s a guideline on how you might implement this: ▪️ Determine one outcome metric (the business result) ▪️ Determine two to three velocity metrics (the leading indicators) ▪️ A timeframe (when you expect each to move) Example: ▪️ Outcome: booked calls ▪️ Velocity: CTR to services page, DM volume referencing the post, landing page conversion rate That’s it. Simple. Trackable. Actionable.

How do we approach stakeholders - and how do we generate meaninful value for them? Over the last years, working across multiple Horizon Europe projects (e.g. Soil Health Benchmarks, LILAS4SOILS, Project CAFAMORE, TRAILS4SOIL), I’ve spent a lot of time reflecting on how we design and run stakeholder engagement. Across projects and organisations alike, I keep encountering a familiar pattern. We design engagement frameworks. We create checklists. We define participation moments. And still, something often doesn’t quite land. Not because stakeholders are unwilling to engage — but because we often misread or interpret from our perspective what we’re actually hearing. And foremost, projects engage motivated by checklists. Lately, I’ve been exploring this challenge through the lens of epistemic justice (very much as a learner) - not as a theory to apply, but as a practical question: How do we recognise, work with, and value or enable different ways of knowing in stakeholder (needs, expectations, wishes, etc.)? One of the risks, when we don’t, is what is often described as epistemic injustice. The image below captures this quite simply: someone shares experience A, but what gets heard - and acted upon - is B. Not out of bad intent, but because interpretation is guided by existing knowledge structures and decision-making power. For example: in a workshop on regenerative agriculture, a farmer is asked to reflect on “barriers to adoption” using predefined indicators. When he explains that the real challenge is yield volatility, financial risk, and the inability to absorb a bad season, this is translated into labels like “risk aversion” or “lack of incentives”. The farmer is heard - but his framing is reshaped to fit project categories, rather than allowing those categories to adapt. What I’m learning is that this isn’t about adding more empathy workshops or slowing projects down. It’s about epistemic fluency: integrating different kinds of knowledge, coordinating different ways of knowing, and designing engagement processes that adapt with stakeholders, not just to them. In my role advising the Mission Soil Cluster on Stakeholder Engagement and Communication, working with 55+ projects,  I want to explore this more deliberately over the coming year - and I’d genuinely welcome critique or pushback from those who’ve thought about this far longer than I have. Alexandra Robinson Dave Snowden Adrian Wagner Anne Caspari Joshua Stehr How do you see the balance between structured project delivery and epistemic justice e.g. in EU-funded projects that aim to engage stakeholders around diverse understandings of challenges and objectives (e.g. soil health across different regions)?

In many projects, stakeholders know they have a problem but aren’t clear about the solution. As Business Analysts, it’s our job to turn that uncertainty into clarity. Here’s how I approached it in a report automation project: 🎯 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: The organization manually prepared monthly financial and operational reports using Excel. The process was tedious, error-prone, and delayed decision-making. Leadership knew they wanted “automation” but couldn't articulate what exactly they needed. 🛠️ 𝐇𝐨𝐰 𝐈 𝐇𝐞𝐥𝐩𝐞𝐝 𝐓𝐡𝐞𝐦 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫 𝐓𝐡𝐞𝐢𝐫 𝐍𝐞𝐞𝐝𝐬: Start with Business Outcomes, Not Solutions → I asked, "What decisions are delayed today due to slow reporting?" and "What’s the impact of late or incorrect reports?" → This shifted the discussion from "build a dashboard" to "we need accurate reports within 3 days after month-end to improve decision speed." 𝐂𝐨𝐧𝐝𝐮𝐜𝐭 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐖𝐚𝐥𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡𝐬 → I organized sessions where stakeholders walked me through the current report generation steps. → Outcome: Identified bottlenecks like manual data consolidation from multiple systems, version control issues, and formula errors. 𝐔𝐬𝐞 𝐕𝐢𝐬𝐮𝐚𝐥 𝐀𝐢𝐝𝐬 → I mapped the As-Is report preparation process on a whiteboard: data sources → manual steps → approvals → final report. → Stakeholders immediately saw inefficiencies they hadn’t verbalized before. 𝐄𝐥𝐢𝐜𝐢𝐭 𝐏𝐚𝐢𝐧 𝐏𝐨𝐢𝐧𝐭𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐎𝐩𝐞𝐧-𝐄𝐧𝐝𝐞𝐝 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 → Instead of asking, "What features do you want?", I asked: "What’s the most frustrating part of preparing these reports?" "What do you wish was faster or easier?" → Answers revealed that data reconciliation and last-minute formatting were major pain points. 𝐏𝐫𝐨𝐩𝐨𝐬𝐞 𝐒𝐦𝐚𝐥𝐥 𝐏𝐫𝐨𝐭𝐨𝐭𝐲𝐩𝐞𝐬 → I created quick mockups (even in Excel or Power BI) of how an automated report could look. → This gave stakeholders something tangible to react to, sparking more specific feedback and helping refine the requirements iteratively. Facilitate Prioritization Workshops → Stakeholders often have a wishlist once they start seeing possibilities. I conducted MoSCoW prioritization sessions to separate “must-have” automation (data refresh, error checks) from “nice-to-haves” (fancy dashboards). 𝐓𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐞 𝐔𝐧𝐜𝐥𝐞𝐚𝐫 𝐖𝐚𝐧𝐭𝐬 𝐢𝐧𝐭𝐨 𝐂𝐥𝐞𝐚𝐫 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬 → Statements like, "We need to make reports faster" were converted into clear specs: Data from 3 systems consolidated automatically. Standardized templates in Power BI. Report availability by the 3rd business day. 💡 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 𝐟𝐨𝐫 𝐁𝐀𝐬: When stakeholders are unclear, they don't need immediate solutions — they need discovery. Our role is to: ✔️ Focus on outcomes. ✔️ Walk the current journey. ✔️ Ask powerful open-ended questions. ✔️ Show possibilities visually. ✔️ Translate pain points into structured requirements. BA Helpline

🔹 Velocity and Acceleration Analysis in Linkages – A Step-by-Step Guide In mechanical engineering, linkages are the heart of mechanisms. They transmit motion and force in machines like engines, presses, and robotic arms. To design them effectively, we need to understand not just positions, but also velocity and acceleration of each link. Let’s break it down step by step 👇 1️⃣ Position Analysis (Foundation) Before moving to velocity and acceleration, we first solve the position analysis – finding the location of every link using geometry and vector loop equations. This gives us the baseline configuration. 2️⃣ Velocity Analysis Concept: Velocity tells us how fast a point or link moves. Method: Differentiate the position equations with respect to time. Approach: Use the relative velocity method: V_B = V_A + ω × r_B/A Or apply the instantaneous center of velocity method for planar linkages. Goal: Find angular velocities of all links once the input speed is known. 3️⃣ Acceleration Analysis Concept: Acceleration shows how velocity is changing – crucial for forces and stresses in design. Method: Differentiate the velocity equations. Approach: Use relative acceleration equations: a_B = a_A + α × r_B/A – ω²r_B/A Split into tangential (due to angular acceleration) and centripetal (due to angular velocity) parts. Goal: Determine angular accelerations and linear accelerations of all points. 4️⃣ Why It Matters ✔ Helps in designing smooth and efficient machines. ✔ Ensures components can handle dynamic forces. ✔ Used in cam-follower systems, automotive suspensions, and robotic mechanisms. 🔧 In short, velocity and acceleration analysis bridge the gap between geometry (where things are) and dynamics (how forces act). --- 💡 What do you think: should I make a step-by-step YouTube tutorial showing a four-bar linkage example with velocity and acceleration analysis?

𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐕𝐞𝐥𝐨𝐜𝐢𝐭𝐲 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐌𝐕𝐀) #Migration requires an accurate #velocity_model to effectively focus reflections and correctly position subsurface reflectors. Determining the correct migration velocity is a crucial step in #seismic_imaging. While #geological knowledge can provide insights into the spatial distribution of propagation velocities, detailed information must be derived from the seismic data itself. #Velocity estimation relies on the redundant illumination of reflectors provided by multi-offset #seismic_data. The fundamental principle is that the correct velocity must accurately account for the relative time delays between reflections originating from the same subsurface interface but reflected at different aperture angles. Most #velocity_estimation_methods_analyze the kinematics of reflections. In simpler geological structures, kinematics are analyzed directly in the data domain by measuring the relative moveout of reflections as a function of offsets through velocity spectra. However, in complex geometries or when wavefronts are distorted by rapidly varying velocity functions, kinematic analysis is more effectively performed in the image domain after migration. 𝐌𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐕𝐞𝐥𝐨𝐜𝐢𝐭𝐲 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (#MVA) is a systematic process for estimating interval velocity in the image domain through several iterations of a three-step process: 1️⃣ Migrating the data with the current best estimate of interval velocity. 2️⃣ Analyzing the pre-stack images for kinematic errors. 3️⃣ Inverting the measured kinematic errors into interval velocity updates using a tomographic approach. #MVA exploits data redundancy in Common Image Gathers (#CIG) by measuring coherency across images obtained from different data offsets or varying reflection-aperture angles. 𝐓𝐡𝐞 𝐭𝐨𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢 inversion problem is often underdetermined, as ray coverage typically has a #limited_angular_range. Therefore, the inversion process must be constrained to prevent divergence. Geological knowledge of subsurface structures and the behavior of the velocity function should inform the components of the velocity model that cannot be sufficiently determined from the data alone. Incorporating geological insights into the regularization terms can lead to more #accurate_models, favoring smoother velocity functions parallel to reflectors while allowing for discontinuities at layer boundaries. Understanding these principles is essential for improving seismic imaging and ensuring accurate subsurface interpretations. #SeismicImaging #Geophysics #VelocityModeling #MigrationVelocityAnalysis #GeologicalInsights

What metrics do you use to track sprint or release progress? Tracking progress is one of the most critical responsibilities for a Business Analyst or Scrum Team during a sprint or release. Metrics help teams measure whether their efforts are translating into value, not just activity. Without measurable data, sprint reviews become opinion-based rather than outcome-driven. Definition Sprint or release metrics are measurable indicators used to assess a team’s performance, predict delivery success, and identify improvement areas within Agile projects. They turn abstract progress into tangible evidence of delivery health. Purpose These metrics are not meant to micromanage but to ensure alignment between commitments, delivery, and business value. They support transparency, predictability, and continuous improvement. 1. Velocity Measures the total story points completed per sprint. Helps forecast future sprint capacity and identify if the team is overcommitting or under-delivering. 2. Burndown Chart Shows the remaining work against time. A healthy burndown line reflects steady progress toward sprint goals. Any plateau or sudden drop highlights blockers or unrealistic estimates. 3. Sprint Goal Success Rate Indicates how effectively the team meets the intended sprint goal. Even if all stories are completed, missing the sprint goal often signals misalignment between business objectives and backlog priorities. 4. Defect Density Tracks the number of defects per story point or per sprint. This helps assess code quality and efficiency of testing and analysis during the sprint. 5. Lead Time and Cycle Time Measures how long it takes for a story to move from backlog to completion. Shorter, predictable times indicate a mature, self-organizing team. Practical Application For example, during a financial software release, a team observed fluctuating velocity and high defect counts. By tracking cycle time and defect density together, they discovered delays in peer review and testing. Streamlining reviews reduced defect rates and stabilized sprint velocity. Step-by-Step Framework to Track Progress 1. Define 3 to 5 key metrics relevant to your project goals. 2. Set a baseline from previous sprints or releases. 3. Visualise metrics using tools like Jira dashboards or Power BI. 4. Review trends during sprint retrospectives, not just numbers. 5. Identify actions to improve weak metrics. 6. Re-evaluate metrics periodically as the team evolves. Key Takeaway Metrics must drive improvement, not inspection. As a Business Analyst, your role is to ensure metrics reflect business value, not just task completion. What metric has been most valuable in your projects to predict delivery success?

You don’t need a report to know a sprint is slipping. You just need to read this BURNDOWN chart. A sprint that started the same... led to two very different conclusions. Let’s see how 👇 In Agile, burndown charts and velocity are more than tracking tools, they’re decision enablers. A burndown chart maps remaining effort across time. Velocity shows your team’s average output per sprint or day. Together, they tell the real story behind delivery. Now imagine this: 📌 Two teams. 📌 Same 10-day sprint. 📌 Same 100 story points. 📌 Same commitment. But two completely different outcomes. 🟩 Team Green Delivered consistently Maintained velocity: 10 pts/day Burned down to 0 by Day 10 ✅ Sprint goal achieved ✅ Value delivered on time 🟥 Team Red Flat progress early on Velocity dropped to 3.5 pts/day Finished with ~65 points still pending ❌ Sprint failed ❌ Replanning required So what happened? Team Green stayed aligned with their plan. Team Red? Missed early signals, didn’t adapt. One burndown showed control. The other exposed risk. What Should Every PMP Learn Here? Burndown shows what’s happening. Velocity explains why it’s happening. Flat progress early? That’s not a delay, it’s a decision point. Ignored trends = missed delivery. Consistent burn = disciplined execution. Same scope. Same sprint. But only one team delivered predictably. As Project Managers, we don’t just track work. We interpret patterns. We act before the curve flattens. And we lead teams toward VALUE not just effort. Let your burndown chart speak before your stakeholders ask.