Change Management in Knowledge Systems

"How are you rolling out the new system?" I asked the VP. "Email went out Monday," she said. "Documentation attached. We're live next Monday." "Have you scheduled training sessions?" "The instructions are very clear," she said dismissively. "Did you test it with a small group first?" She frowned. "Why would we do that?" "To catch issues before..." She cut me off. "It's been thoroughly vetted." "Have you identified who needs the most support?" "Everyone's smart. They'll figure it out." "Did you ask managers what concerns they're hearing?" "Look," she said, getting impatient. "IT built it. We documented it. People just need to use it." "What's your plan if people struggle?" "They won't." Monday came. Dumpster fire. By noon, nothing worked. Everyone was mad. Fingers pointing everywhere. Pure chaos. The help desk got 200 tickets in three hours. "System won't accept my entries." "Getting error messages." "Lost all morning's work." Emergency meeting at 2 PM. "Why is this failing?" the VP demanded. Tom raised his hand. "The system times out every five minutes." "Nobody told us about the new approval levels," Sarah added. "It deleted my entire team's data," Marcus said quietly. The VP turned to IT. "You said it was tested!" "We tested the code. Not with actual users." She locked eyes with me I said nothing. I didn't have to. I could tell she remembered our conversation. Every question I'd asked. Every shortcut she'd taken. Three months later: Six-figure cleanup cost. Four employees quit. Two clients gone. The VP learned something expensive: Change management isn't about managing change. It's about managing people through change. Skip the people part? You're not implementing anything. You're just documenting your disaster as a cautionary tale.

Shared understanding is fundamental to any change endeavour. But how do we orchestrate a journey towards a shared understanding? This framework - from the fantastic Challenge-led system mapping handbook by Climate-KIC - highlights a structured progression inspired by the DIKW pyramid. I really like the way iterative dialogue is embedded in a way that ensures resources become living documents that evolve with stakeholder insights, reflecting the dynamic nature of the system. "The evolving conversation contributes to the collective understanding of the challenges, the questions, and the mapped system itself." This journey begins with participatory processes and data generation, which lay the foundation for understanding the makeup of the system. These steps involve diverse stakeholders coming together to identify core components and relationships within the system. As the process evolves, we move into harvesting and documentation, where data transitions into manageable sources and is organised into coherent information. This phase involves physical structuring and cognitive processes, framing data into actionable insights and beginning to illuminate system patterns. The next phase—conceptualisation and analysis—builds on this structured base to foster a deeper understanding. Here, information transforms into knowledge through analytical structuring. This stage involves recognising connections, patterns, and dynamics, enabling stakeholders to identify key indicators of progress or change. Finally, the journey culminates in wisdom, where insights are communicated through visualisation and interpretation. This stage bridges the gap between abstract analysis and practical application, enabling informed decision-making and co-produced practices. Wisdom reflects a high level of both structure and understanding, empowering stakeholders to act collaboratively toward systemic change. This iterative and participatory process emphasises the importance of feedback loops and incremental understanding, ensuring that stakeholders grasp the complexities of the system and also feel invested in its transformation. "Knowledge management integrates links between interpretation, analysis, and action, allowing practitioners to move from traditional 'learning to manage' practices to 'management as learning'."

Change Management is the missing piece in most data ecosystems. I often repeat a quote: "If nothing ever changes, nothing ever breaks." In other words, quality issues are almost a result of change occurring at some point in the data pipeline. For example: 1. Application code can change by adding features, removing features, updating business logic, or changing data ingestion points 2. Application events can change by creating or removing events, updating event schemas, modifying how frequently events are emitted, or changing the conditions under which events are emitted 3. Database code could change by altering schemas, creating new databases/tables, adding columns that may or may not be versions of the previous columns, making fields Nullable, and much more 3. Pipelines/processing steps can change by modifying transformations, adjusting dependencies, adding or removing filtering logic, restructuring aggregations, or altering orchestration logic. 4. Stream processing jobs can change by updating windowing strategies, modifying partitioning keys, adjusting stateful processing logic, or changing the underlying infrastructure. 5. File storage structures can change by restructuring folder hierarchies, modifying file formats (for example, JSON to Parquet), adjusting partitioning schemes, or changing retention policies. 6. Medallion architecture layers can change by altering how data progresses from raw to refined layers, introducing new validation checks, enforcing stricter governance, or restructuring tables. 7. Data warehouse tables & transformations can change by modifying joins, adjusting business logic in transformations, deprecating or renaming tables, or changing partitioning strategies. 8. Data products can change by evolving definitions, updating metrics, shifting ownership, or integrating new data sources. Now consider that the above list is merely a fraction of the many changes that can and do emerge daily in a data ecosystem. These changes are usually untracked and invisible to all other participants in the data supply chain. They are not audited, which makes root-causing failures incredibly difficult. Most importantly, the more data debt accumulates in a downstream system, the more ripple effects even a single change will have. My recommendation? Begin with visibility first. Start by creating the systems to track changes over time at every layer of technology in your pipeline. Capture information such as: 1. What data object was changed? 2. Who changed it? 3. What was the diff between version 1 & 2? 4. What were the expectations of the consumers of this object? 5. Was this change communicated in advance? Once you have this information, you can begin to correlate changes with outages, governance issues, failed compliance audits, untrustworthy data, and other instances of low quality. Good luck!

𝗧𝗵𝗲 𝗙𝗼𝗿𝗴𝗼𝘁𝘁𝗲𝗻 𝗦𝘁𝗲𝗽 𝗧𝗵𝗮𝘁 𝗞𝗶𝗹𝗹𝘀 𝗚𝗿𝗲𝗮𝘁 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 If there's one phrase that should send shivers down every project manager's spine, it's: "We'll handle change management internally." Translation: 𝗪𝗲'𝗹𝗹 𝘄𝗶𝗻𝗴 𝗶𝘁 𝗮𝗻𝗱 𝗵𝗼𝗽𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗺𝗮𝗴𝗶𝗰𝗮𝗹𝗹𝘆 𝗮𝗱𝗼𝗽𝘁 𝘁𝗵𝗶𝘀 𝗻𝗲𝘄 𝘀𝘆𝘀𝘁𝗲𝗺. Here's the uncomfortable reality 👇 Many organizations treat change management like it's optional. Something fluffy. A nice-to-have. A checkbox to tick before launch. But here's what the data actually shows: 𝟳𝟬% 𝗼𝗳 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗮𝗶𝗹. And the culprit isn't usually bad code or flawed architecture. It's people who weren't ready, weren't trained, or weren't brought along for the ride. We pour millions into new technology. Endless hours into project design. Countless meetings into governance structures and data models. But we forget that 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗶𝘀𝗻'𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝘁𝗵𝗶𝗻𝗴 — 𝗶𝘁'𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁. I've watched talented teams build beautiful data platforms and AI models that check every technical box — only to see them gather dust post-launch. Not because of bad execution. Because the humans on the receiving end weren't part of the journey. 🚶♂️ They weren't consulted during design. They weren't trained effectively. They weren't given a reason to change workflows that worked (even if held together with Excel and prayer). Here's what I've learned the hard way 💡 #ChangeManagement isn't a side quest. It's the main storyline. And like any good story, it needs dedicated storytellers — specialists who understand: • Communication strategies that actually resonate • Behavioral psychology and how adults learn • Adoption metrics that go beyond "number of logins." • Resistance patterns and how to address them with empathy 𝗖𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝗱𝗴𝗲𝘁 𝗹𝗶𝗻𝗲. It should have dedicated resources. It should start on Day 1 of the project, not three weeks before go-live, when someone panics about user adoption. If your #DataStrategy, #DigitalTransformation, or #AI projects keep stalling after launch, don't just audit your tech stack or review your backlog. 𝗟𝗼𝗼𝗸 𝗮𝘁 𝘆𝗼𝘂𝗿 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗴𝗮𝗽. Because the best tool in the world means nothing if no one changes how they work. 🎯 The fanciest dashboard is worthless if people still make decisions in spreadsheets. The most sophisticated AI model won't deliver ROI if your team doesn't trust it. Technology is the easy part. Humans are the hard part. That's why we need to stop treating change management like an afterthought. #ChangeManagement #Leadership #DigitalTransformation #AI #DataStrategy #ProjectManagement #OrganizationalChange #Teamwork #Innovation #TechLeadership

If I've learned nothing else from all of the systems I've migrated, installed, and implemented, it is this: It is never just about the platform or technology. It is about the change that those systems bring. Opportunities for new workflows, opportunities for new functionalities, and on the downside, sometimes lost functionality or more complex workflows. Not every system/platform is going to equally increase functionality and efficiency across all functional areas. Some work will much easier, some may be exactly the same, and other work may take more time OR even another technology to fill in the gap (a plugin, a module, a 3rd party software or platform, etc.) Part of prepping for new technology is not only clearly identifying needs (including any restrictions) and requirements to get the most appropriate technology, but also figuring out how the new software/platform will be used. How is the old system used? How will that work migrate to a new system? Don't just transfer data and workflows -- use the system change to recalibrate and build a new way forward, keeping what works, reducing or eliminating what doesn't, to the extent possible. Involve your staff and get everyone on board. Everyone who works in a system or with a system has valuable insights! Leverage collective knowledge to build the future. #systemmigrations #tips #changemanagement #bethechange #systemigration #libraries #archives #softwaremigration

Your organisation is losing critical knowledge every day... and your reports and database won’t save it. This knowledge drain isn’t just about efficiency. It’s eroding institutional memory which slows down decision-making, and weakens programme impact. This guide uncovers the hidden knowledge gaps that keep development organisations reinventing the wheel and shows how to turn scattered insights into a lasting, shared asset. Key Takeaways from the guide include: ➤ Beyond Reports The most valuable lessons from projects and programs aren’t always written down. How do you capture insights from field staff, communities, and partners before they’re lost? ➤ From Information Overload to Strategic Knowledge Development organisations generate countless reports, evaluations, and studies. But are they used for decision-making or just archived? This guide shows how to turn data into actionable knowledge. ➤ Preventing Knowledge Loss During Transitions What happens when key staff leave? Without structured knowledge management, lessons, partnerships, and institutional memory disappear. Learn strategies to retain and transfer knowledge across teams and leadership changes. ➤ Breaking Down Silos Between Teams and Programmes Lessons from one project could be game-changing for another—but only if they are shared. This guide outlines how to bridge gaps between departments, sectors, and country offices. ➤ Leveraging Technology Without Losing Human Insights Digital platforms are essential, but technology alone isn’t enough. Learn how to balance tech-driven knowledge systems with people-centered learning. Impact relies on more than funding. It depends on how well we manage, share, and apply knowledge. Ready to stop the drain? Start here. #KnowledgementManagement 🔔 Follow Me ♻️ Sharing is Caring

Tired of the same old rigid and ineffective change management plans? Today, change in organizations is increasingly like sailing: the wind (market, technology, regulations) can shift direction suddenly, and those at the helm must know how to adapt quickly. The organizations that thrive are not those that follow a predetermined plan, but those that embrace dynamic change management: an agile and flexible approach that uses evolving tools to understand in real time whether change is working or if a course correction is needed. In this article, we’ll explore together: How to shift from a “train” mindset to a “sailing” mindset Practical tools to measure change readiness and make data-driven decisions A concrete example of how dynamic change management can make a real difference in your organization.

🚀 Part 4 of my Rethinking Change Management: Collaborative Workspaces for Product Data (Sample Technology Stack and Workflow) In Parts 1, 2, and 3 of my recent series on modern change management, we explored how to evolve beyond traditional methods to tackle the complexities of managing product data across systems and disciplines. In Part 1, I introduced the concept of a collaborative workspace—a dynamic environment enabling real-time collaboration and change tracking (https://lnkd.in/e9V9phpE) In Part 2, I outlined the transition from traditional check-in/check-out methods to a single source of change, paving the way for better traceability and control. (https://lnkd.in/eRwFkXGD) In Part 3, (https://lnkd.in/eCCAbn23) I explore the coexistence of this new collaborative change management architecture with legacy file-based PDM and PLM systems. In the fourth part of my “Rethinking Change Management” series, I explore the technical architecture and workflows behind modern collaborative workspaces. This new approach moves beyond traditional PDM/PLM check-in/out models, enabling real-time, multi-user collaboration and smarter workflows. https://lnkd.in/eSTR4jcj ✨ Here are key highlights: Leverages Product Knowledge Graphs, API integrations, and polyglot persistence 📊 Scalable, traceable workflows for improved collaboration 🤝 and decision-making ✅ Real-time collaboration, like a “Google Doc” 📝 for product data 💡 I draw a sample workflow to demonstrate how it can work: 1️⃣ Retrieve Up-to-Date Data 📁 from CAD, PDM, or cloud systems. 2️⃣ Structure Data into a Knowledge Graph 🔗 capturing dependencies and metadata. 3️⃣ Collaborate in Real-Time ⏱️ without locking others out. 4️⃣ Approve Changes ✅ via transparent workflows. 5️⃣ Save Immutable Baselines 🛠️ of product revisions. 6️⃣ Browse Revision History 📜 for better traceability. The future of PLM is cloud-native ☁️, collaborative 🤝, and data-driven 📈, enabling companies to accelerate change management while ensuring governance and scalability. What are your thoughts on evolving change management in PLM? Let’s discuss! 💬 Looking forward to your comments Dr. Yousef Hooshmand Martijn Dullaart Jos Voskuil Matthias Ahrens Alex Bruskin Michael Finocchiaro Adam Keating Kevin Schneider Martin Eigner Prof. Dr. Jörg W. Fischer Ismail Serin Peter Bilello [sorry, I cannot put everyone here...] #PLM #ChangeManagement #Collaboration #DigitalThread #OpenBOM

“Just train them.” That’s what I hear when companies talk about ERP change. As if training alone will make people adopt the system. But here’s the truth: Training ≠ Change Management. Training shows someone how to use the system. Change management helps them want to use it. And that shift starts long before UAT and go-live. Because by the time you’re booking training sessions? The resistance is already 'business as usual'. What’s missing? → Early engagement with the people who’ll use the system. → Clear, ongoing communication about why the change matters. → Leadership buy-in from day one, not just when problems arise. Change management isn't a last-minute training checklist. It’s a strategy that starts at the very beginning. Actually before the beginning. Long before implementation kicks off. Because if you wait until go-live to get people on board... It’s already too late. PS. What’s the biggest myth you’ve heard about change management? ♻️ Repost if you’ve seen companies confuse training with change. 👋 Follow Mariya Koteva for more insights on ERP & Change Management.

Change Management is too often overlooked in #CLM implementations, but it’s the difference between a system that’s adopted and one that’s abandoned. Launching a new system is good news, but it is not THE news. Adoption and actual value are the success measures everyone should monitor. What make change management the )not so secret sauce to successful CLMS implementations? ✅ Stakeholder Alignment It ensures everyone understands the goals, the process, and their role in it. It also gives teams a chance to voice concerns early—before they turn into roadblocks. ✅Overcoming Resistance People naturally resist new systems. Change management helps address that resistance with clear communication, training, and support—so adoption isn’t left to chance. ✅Communicating Policies It ensures that new processes and expectations are clearly conveyed and understood, reducing confusion and increasing compliance. ✅Avoiding Failure Many CLM projects fail not because of the technology, but because people weren’t prepared for the change. Change management helps bridge that gap. Planning isn't sexy or exciting. Everyone rushes to by tech, but without effective change management - there is VERY low likelihood of success. That is the cold, hard truth. #legaltech #legalops