Tips to Overcome Migration Challenges

Colgate-Palmolive ran everything through SAP - from factory floors to dentist offices. When I led their migration, one thing was clear: this wasn’t just about moving systems. It was about keeping a global supply chain alive. Back in the early 2000s, I was consulting on one of the most high-stakes SAP migrations I’d ever faced. Colgate’s global operations depended on a single truth: If SAP goes down, so does everything else. Toothpaste doesn’t show up on shelves. Distribution centers stall. Orders to Walmart, Walgreens, and CVS? Delayed. The supply chain goes silent. I remember thinking, “This isn’t about software anymore. This is a logistics problem with a technical disguise.” So before we moved a single bit of data, we did what most teams skip. Here’s what our playbook looked like: 1. Inventory the unknowns We scanned every system — not just for size, but interdependencies. You can’t move System A if B, C, and D are chained to it. 2. Model the risk How much data? How long would each copy take? Where were the bottlenecks — disk IO, network bandwidth, or just legacy bloat? 3. Rank criticality by impact, not size Some “small” systems had outsized business value. Like the one tracking global SKUs. Touch that wrong, and orders get lost in translation. 4. Simulate the move — multiple times We did dry runs. Timed every process. Tweaked our scripts. Even ran scenarios for “What if this breaks mid-flight?” 5. Coordinate like air traffic control Every migration phase was mapped like a flight plan. Timelines, dependencies, failovers. No guesswork. No egos. That project worked. No disruptions. No delays. No headlines (which, in IT, is a win). It also planted the seed for what would eventually become IT-Conductor Inc. Because I realized: Migrations aren’t about tools or timelines. They’re about orchestration. And orchestration starts with a brutally honest assessment. If you're facing a cloud migration and feel unsure where to start — start there. That’s what separates a clean cutover from a career-defining disaster.

Its easy to forget that data migrations are an iterative process. The natural inclination of most people when presented with a list of items to migrate is to systematically go through that entire list. However, the objective of your first iteration of a data migration is usually focused on making sure you have an understanding of the tools, processes, and knowledge required to migrate the data. Migrating the utility network can be a daunting task. Even if you've done data migrations before, there is a very particular way that you need to migrate your data in order to be successful (this process has been covered in detail in some of the webinars on this page https://lnkd.in/e7cr_9JF). So how do I recommend you do your first iteration? 1 - Bring over all your network layers. If its a point, line, or polygon representing a network feature you need to have it in your Utility Network so you can identify and topology issues associated with it. 2 - Don't migrated proposed, retired, or abandoned features in your first iteration. Depending on how you modelled these types of features in your data they may cause topology errors. While you will want to migrate them to your utility network initially, it makes it a lot easier to track down and fix topology errors in your first iteration if you leave these features behind. 3 - Focus on mapping the fields that are absolutely require by the utility network. This will always include Asset Group, Asset Type, and Global ID. You'll also need to include fields required to support tracing, which can vary for each model. Electric models need device status (open/closed) and should include the normal phasing. Pipeline models also need device status (open/closed), and if you have cathodic protection equipment you'll need to include the material of equipment. 4 - If your current system maintains unique identifiers that can assist during the quality assurance process, then you should add them into the target model and bring them along for the conversion. Common example of this include network information (feeder, pressure zone, etc), work order numbers, or any other identifier you can just add into the target database and populate directly without needing any translation or domains. In my next article I will describe what to do once you've got your proof-of-concept migrated. In the mean time, you can access a free tutorial on how to migrate data into a Utility Network using Esri's Data Loading tools in our documentation gallery: https://lnkd.in/eJBDXR9K

Transitioning from SAP ECC to S/4HANA involves several critical steps, each designed to ensure a smooth and successful migration. Here’s a concise overview of the process: 1. Assessment and Planning • Business Case Development: Define the objectives and benefits of moving to S/4HANA. • System Assessment: Evaluate your current ECC environment, including customizations and data volume. • Roadmap Creation: Develop a detailed transition plan, including timelines, budget, and resources. 2. Preparation • Team Formation: Assemble a team of IT professionals, business stakeholders, and external consultants. • Training: Provide education on S/4HANA and the transition process for your project team. • Readiness Check: Utilize SAP Readiness Check to identify compatibility issues and preparatory steps. 3. System Conversion • Data Cleansing: Cleanse and archive data to reduce migration volume. • Custom Code Adaptation: Analyze and adjust custom ABAP code for S/4HANA compatibility. • Add-On Compatibility: Ensure third-party add-ons are compatible with S/4HANA. 4. Technical Migration • Backup Plan: Implement a comprehensive backup and recovery plan. • Infrastructure Preparation: Set up the necessary hardware or cloud infrastructure for S/4HANA. • Database Migration: Transition your database to SAP HANA if not already using it. • Conversion Tools: Use SAP tools like Software Update Manager (SUM) and Database Migration Option (DMO) for the conversion. 5. Data Migration • Mapping and Transformation: Map ECC data structures to S/4HANA and transform data accordingly. • Data Load: Load data into S/4HANA using tools like SAP Data Services or SAP Migration Cockpit. • Validation: Validate and reconcile the migrated data for accuracy. 6. Testing • Unit Testing: Test individual components and customizations. • Integration Testing: Ensure all system components work together seamlessly. • User Acceptance Testing (UAT): Conduct UAT to verify the system meets business requirements. 7. Cutover and Go-Live • Cutover Planning: Develop a detailed cutover plan outlining steps and timeline. • Final Data Load: Perform final data load and reconciliation. • Go-Live Support: Provide hypercare support post go-live to resolve any issues quickly. 8. Post-Go-Live Activities • Performance Monitoring: Continuously monitor system performance and optimize as needed. • User Training: Offer additional training sessions for end-users. • Continuous Improvement: Implement a process for ongoing improvements and leveraging new S/4HANA features. Key Considerations • Change Management: Effectively manage organizational change to ensure smooth transition and user adoption. • Customization Evaluation: Assess the necessity of existing customizations and consider leveraging S/4HANA standard functionalities. • Process Re-engineering: Align business processes with S/4HANA best practices to maximize benefits.

I've been part of 7 significant data migrations throughout my career. I'll teach you the key things to be mindful of in 10 minutes: 1. Data migration > Copying data over to the new system A few factors to consider: * Do you need to move historical data? * Are the data types similar between the new and old systems? * Do you have DDLs defined in your code base? 2. Redirecting input sources > Your new system needs to be able to access the necessary inputs A few factors to consider: * Are the input data sources the same? * Do the input sources in the new system have similar or better SLAs? * Are the input sources of the same quality and schema? 3. Moving code > Does your old code work with the new system If you are moving from a primarily SQL-based code base to a dataframe, you'd need lots of new code. A few factors to consider: * How different are the new and old systems in terms of code interface (e.g., pure SQL v Python)? * Does the new system have all (& ideally more) features than the old one? * Does the scale of the new system satisfy your data SLAs? * The better your code tests, the simpler this step 4. Tools > Your systems probably have non-pipeline tools (e.g., GitHub actions, etc), ensure that they work with the new system A few factors to consider: * Do the tools (e.g., dbt elementary -> Spark?) of the old system work in the new one or have better replacements? * If your new system has "another" tool to do similar things, ensure it can! * If your system interacts with external company-wide tools (e.g., GitHub actions), ensure good integration with the new system 5. Validation period > Run the new and old systems for a switch-over period before switching over users to the new systems A few factors to consider: * Keep the old and new systems running for a switch-over period. * Run frequent (ideally scheduled) validation checks between new and old systems during this period. * After enabling end-user access to the new system, keep the old system on in case of rollbacks 6. Permission patterns > Do the end users have the same permissions as the old system A few factors to consider: * Do your current stakeholders have the same access(read-write-create-delete) in the new system? * If you are changing permissions, ensure you provide the end users sufficient time to adapt. 7. Interface layer for end-users > Will the end users be able to access data with the same data asset name and schemas? A few factors to consider: * Does the new systems require the end user to change any of their code/queries? * If you have used an interface layer (usually a view), this should be simple * Will the new data system have the same or better SLAs? 8. Observability systems > Will your new system's observability system work similarly? What other migration tips do you have? Let me know in the comments below. - Enjoy this? ♻️ Repost it to your network and follow me for more actionable data content. #data #dataengineering

I've ensured 100+ AWS migration projects succeed. Found key reasons why migrations could fail. (This is how we solved it, and you can too) 1. Ever-changing migration plans Constantly changing your migration plan, like 'Lift and Shift', 'Re-platforming', 'Re-hosting' etc., is a red flag. This inconsistency can lead to unforeseen dependencies and legacy system issues. To mitigate this, conduct thorough application dependency mapping and discovery before planning migration phases. 2. Inconsistent migration methods In a multi-tier web application migration project, using different methods like 'Re-hosting', 'Re-platforming', and 'Refactoring' for different applications will prove inefficient. It can lead you to integration issues and performance bottlenecks. Avoid it by proper standardization, defining clear target architectures, and grouping similar applications together. 3. Ineffective escalation process In a large data warehouse migration project, you can face issues with data consistency and integrity. These technical issues need to be promptly escalated to the right team for quick resolution. As a solution, establish a strict governance structure and communication plan to ensure blockers reach the right teams promptly. 4. Late emerging migration issues While doing CRM system migration, unforeseen data migration complexities can surface late, causing delays and significant rework. To address this, implement mechanisms like early design processes, tools, and escalation paths to identify issues sooner and maintain project momentum. 5. Lack of stakeholder alignment This can usually be faced while undergoing an ERP system migration. Stakeholder buy-in can prove to be critical. Without alignment, miscommunication between the migration team and business stakeholders can lead to roadblocks. Ensure alignment early by highlighting how AWS benefits specific objectives, fostering strong support throughout the migration process. Just remember that the future is unpredictable. But if planned well, then things are manageable! In the same way, Murat Yanar, Director at Amazon Web Services (AWS), once said, “You may not be able to predict the future needs of your business precisely. But the AWS cloud provides services to meet these ever-changing demands and help you innovate flexibly and securely.” Curious to know: What’s your biggest challenge when it comes to AWS migration? #aws #database #scalability #softwareengineering #simform

Transitioning to new technology like Workday Student requires re-thinking everything. Many believe it's a straightforward software swap. But that's like thinking that moving from the United States to another country in the world and expecting everything to work the same. Sure, the basic functions remain: eat, sleep, work, play. But the language is different. The rules are different. If you only had someone from that other country helping you navigate things they might not think of things to tell you because their experience is from that other country. When someone works as an implementation partner/system implementor, they live and breathe that other product's software. The more they gain the expertise you need them to have to be that expert, the more they move away from any prior experience they had in your shoes in higher education. So what do you do? 1) Talk to peer schools. This is CRITICAL to do. Talk to ones that both went live recently with Workday Student and were on the same legacy SIS you were. But also talk with ones that have been live for a few years. Each one has very valuable perspectives to share. 2) Ask questions. Too often, I see people at institutions hesitate to ask questions, worrying they’re being a bother. They preface with, “This is probably a stupid question,” when in reality, it’s a great question. Remember, the experts from the implementation partner aren’t in your shoes every day—you are the expert in what your institution needs to function. Keep asking questions until you feel confident you can do your job within Workday Student. 3) Seek advice from individuals with a practitioner level perspective. At Legato Strategic, we serve as both translators and transition managers, bridging the gap between your institution and implementation partners. As Workday Student experts, we also take on tasks that fall to the institution. We've observed that implementation recommendations can be both effective and ineffective, often overlooking the complexities of live transactional data. By working alongside your staff, we provide on-call support to ensure your transition fully considers these data nuances. Our support is collegial and nimble, without rigid commitments or ticketing systems. You get the help you need when you need it. To have a free one hour consultation or just advice on what you should do next with your SIS assessment or Workday Student implementation, fill out our contact form: https://lnkd.in/gcfv3Uxw. I promise there will be no jargon or slide decks filled with text in tiny font. We can just have an informal conversation about your institution's specific situation. To make sure you don't miss out on tips like these, subscribe to our newsletter: https://lnkd.in/gZ-smcGc

We recently found out we weren’t down-selected by a prospect that was evaluating CLM tools because we asked too many questions. Let me explain… They had a large volume of legacy contracts they planned to migrate into a new CLM. During our discussion, we asked questions that, in our view, are non-negotiable for a successful implementation: ✅ Where exactly is the legacy data stored—shared drive, legacy CLM, contract folders, or inbox archives? ✅ How far back do the contracts go, and are they all still relevant or in scope for migration? ✅ What specific metadata is needed—and how will that data actually be used downstream in workflows, approvals, and reporting? ✅ Does the team expect perfect metadata, or is lower accuracy acceptable with fallback manual review? The prospect told us no other vendor had even scratched the surface of these questions. This isn’t about overcomplicating things—it’s about doing the foundational work up front to avoid migration disasters later. If you’re evaluating CLM tools and planning a legacy data migration, here are the questions your vendor should be asking: 🔍 What role will AI play—and is anyone manually validating extracted metadata for accuracy? CLM vendors often tout AI-powered extraction, but rarely clarify if the output is being checked by humans. That distinction matters—especially for renewal dates, obligations, and clause-level data that trigger automated workflows. 📂 Are we cleaning and enriching data—or just moving bad data from one system to another? Dumping PDFs into a new CLM without organizing them or applying meaningful metadata is a glorified file transfer, not a migration. ⚠️ Who owns data validation and upload and how much support will the vendor provide during that process? If your team is expected to fill out templates or map metadata fields without help, be ready for delays and rework. 🧠 Is your vendor aligning the metadata strategy to your actual business use cases? We’ve seen teams extract 50 fields, only to use 5. Good scoping aligns data capture with decision-making, not just data storage. A successful CLM implementation starts long before kickoff. It starts with asking the right questions, especially about your legacy data. #CLM #ContractManagement #LegalTech #DataMigration #DigitalTransformation #LegalOps #CLMStrategy

3 years ago, we hit a wall that changed us. Our client's site migration to Webflow failed. Traffic dropped. Rankings fell. Our team felt lost. Here's what went wrong: → No pre-migration content audit → Missing 301 redirects → Incomplete sitemap planning → No planning where pages will move → No speed tests Simple truth: Moving a site to webflow needs a clear plan. Here's how we made it work: → Made a comprehensive sitemap strategy → Built a phased content migration system → Planned Webflow CMS structure based on sitemap before dev phase → Set up page tracking tools → Watched loading speeds → A post migration checkup What changed: → Every page moved safely → Search rankings stayed up → Sites loaded faster Here's the big lesson, Moving your site isn't just copying files. It's moving your whole business safely. This fail taught us to build better steps. Every failed launch teaches something valuable. This one made us build better systems. What's your biggest migration challenge? #webflow #marketing #tech

“We need to move off SAP ECC, but we can’t justify the cost.” My CEO friend from Harvard Business School told me this last week. And she’s not wrong. Migration quotes often look scary because many SAP projects: ⚠️ Take years with runaway budgets. ⚠️ Have consultants billing endlessly without clear outcomes. ⚠️ Burn out teams testing the same processes on repeat. But why do SAP migrations become so expensive? Here’s the truth: Most SAP migrations fail the moment they’re scoped. 🛑 They scope everything instead of what matters. ➡️ Every custom report, even if no one uses it. ➡️ Every process variant, even if it’s an edge case. ➡️ Every piece of dirty data, without cleaning it first. This isn’t transformation. It’s expensive duplication. A smart SAP migration is different. It’s a business simplification project disguised as a technical upgrade. If you want to control your migration costs, here’s how: ✅ 1️⃣ Migrate only what you need. Your ECC likely has 20 years of custom code, unused reports, and workarounds that no longer serve you. S/4HANA is your chance to reset, not replicate. ✅ 2️⃣ Fix your data before you migrate. Dirty data multiplies your testing cycles and post-go-live headaches. Good data shrinks timelines, reduces consultant hours, and improves user trust. ✅ 3️⃣ Prioritize the 20% that runs 80% of your business. You don’t need to perfect every exception on day one. Get your core revenue-driving processes live, then iterate. ✅ 4️⃣ Pick a partner who says ‘no’. You need a partner who challenges scope bloat, not one who says yes to everything to grow billable hours. 🚩 Here’s what most never calculate: the cost of staying stuck. – The revenue lost because quotes take days, not hours. – The manual reconciliations your team does every month. – The friction your customers feel because your processes can’t keep up. You’re already paying a hidden cost every day you stay on ECC. You just don’t see the invoice. The difference between an expensive SAP migration and a smart one isn’t technology. It’s strategy. If migration costs are holding you back, maybe it’s time to ask: “Are we planning a migration, or are we copying our problems into a new system?” How are you thinking about controlling cost when you move off SAP ECC? #SAP #S4HANA #SAPMigration #DigitalTransformation #Leadership #CIO #CEO #EnterpriseIT #CloudERP #BusinessTransformation #SAPCommunity #ASARDigital #ERP

𝗟𝗲𝘀𝘀 𝘁𝗵𝗮𝗻 𝗵𝗮𝗹𝗳 𝗼𝗳 𝗖𝗥𝗠 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝘀𝘂𝗰𝗰𝗲𝗲𝗱. I have participated in a few of them - they good ones and the one that fail. I've created a mini CRM Migration checklist to cover 4 key areas that you need to consider. ☑ Comprehensive Planning: Define objectives: Know your goals. Stakeholder engagement: Get everyone on board. ☑ Data Management: Data cleansing: Ensure accuracy. Data mapping: Align old and new systems. Know your ETL tool. Roll-out plan, go small, in waves until you do a full migration. ☑ System Integration: API configurations: Connect smoothly. Focus in details Testing: Validate every step. Test, and test, and test again. ☑ User Training: Training programs: Empower your team to help others. Support resources: Provide ongoing help. Every step, process, and tool is clear and straightforward. No jargon. Just actionable steps. What else could be part of this checklist?