Strategies for Successful Migrations

Before you move a single SAP system, you need to answer 5 questions. Miss even one and your migration might fail before it starts. Most teams skip this part. They jump straight into provisioning cloud resources, copying environments, and trying to meet a go-live deadline. But that’s like building a train schedule without knowing how many trains you’ve got, or where they’re going. Back when I consulted for large SAP migrations - from Colgate to Fortune 100 manufacturers - we never started with tooling. We started with assessment. Because without a clear understanding of what you’re moving, how it’s connected, and what it impacts - you're flying blind. These are the 5 things I always map before touching a single system: 1. System inventory — what exists, and what’s connected You’d be surprised how many environments have orphaned or undocumented dependencies. Miss one? That’s your failure point. 2. Business criticality — what can’t go down, even for a minute Not all systems are equal. Some run background jobs. Others run revenue. You migrate those differently. 3. Resource constraints — who’s available, when, and for how long Most IT teams are already overloaded. You need to know what talent you have before committing to timelines. 4. Downtime thresholds — what’s the business actually willing to tolerate? I’ve seen 80-hour migration estimates get crammed into 24-hour windows. You don’t negotiate after you start. You plan ahead. 5. Migration sequencing — what moves first, and what moves in parallel Dependencies aren’t just technical — they’re operational. Order matters. Or everything stalls. Assessment isn’t overhead. It’s insurance. And the cost of skipping it? Blown deadlines. Missed shipments. Angry execs. And a team stuck in recovery mode for weeks. Every successful migration I’ve ever led had this phase built in from the start. And every failed one I’ve seen? Didn’t.

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

Data conversion is the silent killer of Workday implementations. ☠️ Here’s why. You're ripping out your entire HR/Finance infrastructure and migrating years of mission-critical data into a new system. Yet most organizations severely underestimate this challenge. After dozens of implementations, I've seen firsthand how poor data strategy leads to post-launch chaos and ballooning costs. One client came to us with a broken Benefits implementation causing weekly emergencies. The root issue? Compromised data integrity from the start. 😞 We helped them rebuild their foundation, saving $150K while finally giving them breathing room to focus on strategic initiatives instead of firefighting. The hard truth is that your data quality is probably worse than you think. But there are ways to make it better. My advice is to invest deeply in data preparation before implementation, not emergency fixes after go-live. Before that first load into Workday, you need to: 1️⃣ Determine how much historical data to migrate (hint: less is often more) 2️⃣ Cleanse and validate every data point 3️⃣ Transform everything into Workday-compatible formats 4️⃣ Prioritize integrations that are compliance-critical or essential for day one That's what will give you a stable foundation to build upon so your team can focus on innovation instead of constantly putting fires out. 🔥 What's your biggest data migration concern? Send me a message and let’s solve it together. #Workday #PositionManagement #HRTech #WorkdayConsulting #KandorSolutions #Kinnect

In 2023, Stripe handled $1,000,000,000,000 worth of transactions with 99.999% (5 nines of uptime). Their systems never went offline, even when they were migrating data. Here’s how they designed the system that helped them do it: 1. Choosing the Right Foundation - Stripe created DocDB, a custom database system, on MongoDB Community for flexibility and real-time data handling. 2. Scaling Horizontally with Sharding - Stripe uses thousands of database shards to manage large data volumes, ensuring high availability and low latency. 3. Building the Data Movement Platform - The Data Movement Platform allows for data migration across shards without downtime, maintaining continuous performance and availability. 4. Ensuring Data Consistency and Availability - Asynchronous replication keeps data consistent during migrations by replicating changes to target shards. - Chunk Metadata Service maps data chunks to the correct shards, enabling efficient query routing. 5. Traffic Management During Migrations - Traffic Switch Protocol quickly reroutes data traffic to target shards with minimal disruption, using versioned gating for smooth transitions. 6. Optimizing Data Ingestion - Bulk data import is optimized by arranging insertion orders to leverage B-tree data structures, enhancing write throughput. 7. Maintaining Performance - Oplog Events and CDC Pipeline capture and stream data changes to ensure migrations don't impact performance, logging changes for consistency checks. 8. Automating Database Management - Heat Management System balances data load across shards to avoid hotspots and ensure even performance. - Shard Autoscaling adjusts the number of shards in real-time based on traffic patterns to handle varying data loads. 9. Upgrading Without Downtime - Seamless upgrades are possible with the Data Movement Platform, allowing for database system updates without any downtime, ensuring continuous operation. 10. Continuous Monitoring and Improvement - Custom Proxy Servers manage database queries and enforce reliability, scalability, and access control. - Stripe's infrastructure team constantly addresses complex distributed systems issues to maintain high reliability and performance. -- P.S: If you'd like to learn more, then read over to this blog: https://lnkd.in/gw-fbUEp

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 have no idea what I'm doing... but I'll figure it out." This is basically my everyday life working in a seed-stage startup, and I often rely on applying my data best practices to "non-data" business problems to unblock myself. 🚀 Most recently, I've been working on a migration from Hubspot to Salesforce, where I have limited experience in sales and these tools. But by reframing it into a data engineering problem, I all of a sudden have a wealth of knowledge to make this migration happen. 👇🏽 Here's how I approached it: 1. Determine what's the business use case and expectations from my business stakeholders? 2. Create a flow chart that logically maps out the process of going from "lead capture" to "discovery call" and how a lead's "status" changes throughout the workflow. 3. Map the workflow to an underlying architecture of our various tools and integrations to make this process happen, AND determine which data fields are being changed. 4. Determine all the data fields being used in our current system (Hubspot), then map them to the fields in the new system (Salesforce)-- it's unlikely these fields map 1:1, and thus, be sure to document all of your decisions as you are updating business logic. 5. Measure your baseline counts (e.g. lead counts by "lead stage") in your current (Hubspot) and new (Salesforce) systems. 6. Begin unhooking third-party integrations from the old system and move the integration to the new system so new "lead events" are not interrupted. 7. Test the updated integrations with known values-- for me, I went through the entire "sales journey" as if I were a "lead" by filling out our lead form with a test account, scheduling test meetings, etc., and ensuring the expected data shows up in Salesforce. 8. Begin backfilling data and iterating until your expected counts match in the old and new systems. Bonus: Create a doc that details the entire process and your decisions, as well as create a Slack channel to give real-time updates to ensure your business stakeholders are in the loop. 💯 With this reframe, I went from "How do I migrate from Hubspot to Salesforce!?" to instead, "I've done a database migration before, so let's apply it to Hubspot and Salesforce!" 👀 Check the comments below to see the impact already made to for one of my business stakeholders! #data #dataengineering #sales #salesforce

Planning your CRM migration? Here’s a quick guide to ensuring a seamless transition. ✅ Do's: [1] Plan thoroughly: Develop a comprehensive migration plan with clear goals, timelines, and assigned responsibilities. [2] Clean your data: Remove duplicates and outdated info to ensure only high-quality data enters your new CRM. [3] Test extensively: Check data accuracy and system functionalities to iron out issues before going live. [4] Train your team: Conduct in-depth training sessions to familiarize your team with the new CRM features. [5] Keep communication open: Regular updates and clear communication can help manage expectations and reduce resistance. ⚠️ Don'ts: [1] Rush the process: Take the necessary time for each step to avoid costly mistakes. [2] Overlook user input: User insights are crucial for a system that meets daily operational needs. [3] Neglect data security: Ensure all data transfers are secure and compliant with data protection laws. [4] Migrate unnecessary data: Avoid clutter by only transferring relevant data. [5] Ignore post-migration support: Continue to provide support and monitor the system to resolve any issues that arise. Leaders and executives, what challenges have you faced during CRM migration, and how did you overcome them? Let's share insights below! [👍 Like | 💬 Comment | 🔁 Share] #crm #data #migration

📌Companies are shifting from old and inefficient infrastructures to the cloud for better performance. This includes those wanting to replace: ↳ Aging servers,  ↳ Unreliable firewalls, or  ↳ Sub-optimal hardware and software. But it comes with its hurdles: Technical challenges Potential data loss Business continuity concerns Whether you’re migrating between providers or to the cloud for the first time, you’ll want to follow these best practices. Let's discuss here: 1️⃣Identify your business goals and objectives. Outline the specific benefits you aim to achieve such as: ↳ Cost savings ↳ Flexibility ↳ Improved security 2️⃣Evaluate and select the best cloud provider considering factors like: ↳ Compute options ↳ Connectivity ↳ Security ↳ Scalability, etc. 3️⃣ Develop a migration plan including: ↳ Documentation of current infrastructure  ↳ Migration scope, and timeline ↳ Cloud roles, &  responsibilities 4️⃣ Conduct a migration dry run to test your strategy and troubleshoot issues before going live. → Verify the readiness of the new environment before full migration. Confirm DNS changes, code deployment, and database readiness. → Adjust application configurations to point to the new cloud host before migrating data for a smooth transition. → Migrate stateful data carefully after disabling writes to avoid inconsistencies. → Perform extensive testing and cut over DNS to direct traffic to new infrastructure once the migration is complete and verified. Monitor closely. → Consider leveraging migration experts for knowledge transfer, efficiency gains, and ongoing support. What KPIs or metrics did you use to measure migration success? (Comment Below) #cloudmigration #itleaders #technologytrends

When our customers add more PSPs, migrate to new one, adjust their routing logic, or just reconfigure their setup, they trust Pagos to take on the data aggregation, research, and monitoring work. With Pagos in their corner, this once difficult work is now simple and repeatable! What we've learnt from assisting them is that once the data is harmonized, we see the following steps as critical to any migration journey: Step 1: Determine what metrics you're most concerned about for each processor. This obviously includes cost and approval rate, but can also include contractual obligations, SLA/uptime, fraud tools (and their efficency), chargeback dispute tools and so much more. . Step 2: Establish baseline metrics over time for each processor. With Peacock by Pagos, we ingest your payments data from every processor and normalize it, thereby allowing you to compare processors with different settings and functionality side by side. Step 3: If one processor demonstrates lagging performance, reassess your relationship with them - for parts of your business or all! It's entirely possible you set up a new PSP using configuration settings typical of your more established PSP; since each processor has their own unique way of handling payments, there may be opportunities for improvement purely in your setup. Step 4: Now that you have an easy way to compare key performance indicators for multiple PSPs in Peacock, you can monitor data over time for any unexpected changes which is also important in order to be able to hold your partners accountable. You can even run A/B tests for migrating specific customer segments to different PSPs. Step 5: Payments optimization! They could do all this without having to rip out their current infrastructure as Pagos does not require you to change your current processing flow as we can help maximize your performance without having to actually process your transactions..With our no-code integrations to most major PSPs, you could be live in minutes. Having said all that, we have also found that a lot of the opportunities to do better sits on the merchants side. What is Your performance? Have you optimize that part of the equitation? With detailed data metrics? If not, how do you know if your current partners are your problem and how would you determine success with a new partner..? It should also be noted that other types of partners like payment orchestration platforms (or gateways), fraud providers, subscription management platforms also impact your performance with your PSPs.