Impact of Poor Data on SAP Planning

If You Haven't Fixed Your Master Data, Don't Build AI on SAP Everyone's rushing to build intelligent systems on SAP but most are building on quicksand. Your master data is the foundation of every AI insight, and if it's fragmented, duplicated, or inconsistent, your algorithms will amplify those flaws at enterprise scale. I've seen companies spend millions on machine learning models that failed because their material masters had 47 different ways to spell "aluminum." The problem isn't technical it's organizational. Most SAP environments evolved over decades through acquisitions, system migrations, and business unit independence. Your vendor files might have duplicate entries for the same supplier across different purchasing organizations. Your customer records could be scattered across multiple company codes with conflicting addresses. When AI tries to analyze this chaos, it doesn't see patterns it sees noise. The solution isn't perfect data it's disciplined data stewardship. Start with your most business-critical master data domains: materials, vendors, and customers. Establish clear ownership, standardize naming conventions, and build data quality checks into your processes. Create cross-functional teams that understand both the business context and the technical implications. Make data governance a business priority, not an IT afterthought. Before you chase AI-powered insights, audit your master data foundation. Run duplicate analyses, map data flows between systems, and identify the gaps that will derail your intelligent automation efforts. The companies winning with SAP AI aren't the ones with the fanciest algorithms they're the ones who did the unglamorous work of cleaning their data house first. What's your experience with master data challenges in AI projects? Have you seen promising initiatives fail due to poor data quality? #MasterData #SAPData #DataGovernance #ERPStrategy #AIReadiness #EnterpriseData #DataStewardship #DigitalTransformation

The “Before & After” Data Transformation Story In the lead-up to our SAP migration, we weren’t just preparing systems — we were unearthing years of neglected, inconsistent, and chaotic data. If we are honest, most of the time, it felt less like digital transformation and more like an archaeological excavation. We were buried in layers of spreadsheets, conflicting legacy reports, and systems that hadn’t seen a clean-up in over a decade. Each click revealed more clutter: customer names spelled five different ways, address fields mixing “St.” and “Street” like it was a coin toss, duplicate records stacked on top of each other, and critical fields left blank or filled with guesswork. It was more than just messy — it was risky - A complete nightmare! Data was being pulled from everywhere and nowhere. No single source of truth. No consistency. Just a patchwork of outdated inputs fuelling vital business operations. The worst part? We had to tackle it manually. A Time Sink: Highly skilled people stuck doing low-value, repetitive tasks. An Error Magnet: Fatigue set in. Errors crept through. Fix one issue, uncover two more. A Business Risk: Dirty data meant dirty output. Reports couldn’t be trusted. Customers were misbilled. Orders were sent to the wrong place. And confidence in the system? Gone. We knew we couldn’t carry that baggage into SAP. Something had to change. At this point, we built a purpose-specific solution which was created to automate and streamline data cleansing and validation, giving us the ability to: Proactively identify and rectify errors with precision. Ensure data consistency across all records. Validate information against business rules before migration. This impacts business by: 🔹Reducing Pre-Migration Data cleansing and validation Effort by Up to 75% Freeing up SMEs for strategic tasks, cutting contractor costs, and accelerating migration timelines. 🔹Delivering >99% Accuracy in Key Master Data Minimising migration errors, de-risks go-live, building trust in the new SAP system from day one. 🔹Reducing Migration Delays and Rework by 20–40% Fewer surprises in load cycles and UAT, protecting timelines, budgets, and overall project momentum. 🔹Achieving 100% Data Auditability and Compliance Ensuring full traceability, streamlining audits, and providing a defensible position on data quality from day one. 🔹Reducing Post-Go-Live Errors by 15–30% Fewer issues like misbilling and mis-shipments, leading to smoother operations, faster user adoption, and trusted SAP insights. If any of this sounds familiar, you're not alone. The good news is that we have built a solution which has already helped others through their migration journey, and we’d be happy to share it if it’s useful. Just drop us a message. Created in collaboration with Pawel Lipko ↗️

𝐓𝐡𝐞 𝐰𝐨𝐫𝐥𝐝 𝐨𝐟 𝐒𝐀𝐏 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐜𝐚𝐧 𝐛𝐞 𝐝𝐞𝐜𝐞𝐩𝐭𝐢𝐯𝐞.🔍 I'm passionate about helping businesses modernize, but there's a catch that many overlook. In my years of experience guiding companies through digital transformations, I've witnessed recurring challenges that can derail even the most promising projects: • Companies treating master data as an afterthought, leading to costly rework ↳ Example: A manufacturing firm spent months correcting product codes post-launch ↳ Impact: Delayed ROI and frustrated stakeholders • Teams struggling with data inconsistencies across systems, slowing down go-live ↳ Common issue: Customer information varies between CRM and ERP ↳ Result: Extended testing cycles and missed deadlines • Decision-makers misled by inaccurate reports due to poor data quality ↳ Scenario: Financial forecasts based on incomplete sales data ↳ Consequence: Misallocated resources and strategic missteps These issues share a common root: underestimating the critical role of master data management (MDM) in SAP implementations. So if you're feeling the pressure to deliver quick wins in your digital transformation journey, pause and consider this: Investing time in master data management upfront pays dividends throughout your entire SAP lifecycle. It's the unsexy work that drives real, lasting success. Key benefits of prioritising MDM: 1. Reduced implementation timelines 2. Lower total cost of ownership 3. Improved data accuracy and reporting 4. Enhanced cross-functional collaboration 5. Faster adaptation to business changes Remember: A solid data foundation enables agility, innovation, and informed decision-making. What's your experience with master data in SAP projects? Have you seen the impact – positive or negative – of how it's handled? Share your insights below. Let's learn from each other and elevate the conversation around this crucial aspect of digital transformation. 👇 🙌 𝐅𝐨𝐥𝐥𝐨𝐰 𝐌𝐞 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧:- If you found this useful, I share 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐒𝐀𝐏 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬, 𝐟𝐚𝐢𝐥𝐮𝐫𝐞 𝐬𝐭𝐨𝐫𝐢𝐞𝐬, 𝐚𝐧𝐝 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 from 14+ years in the trenches. 📲 Follow me for more: https://lnkd.in/dwuMRdkQ