Data Management Issues in Manufacturing

A little while back, I shared an end-to-end data issue management framework. This week, instead of focusing on the theory, I want to talk about what actually makes these processes work in real organizations. When I look at places where data issue management has truly led to lasting change, a few patterns stand out: • People actually knew where to go with problems. • Fixes weren’t endless black holes—they got resolved in a reasonable time. • The process felt practical and not just another layer of bureaucracy. So, if you’re thinking about implementing or improving a data issue management process, here are 8 things I’d recommend from personal experience: 📲 Make It Stupidly Simple to Log an Issue – If reporting a data issue feels like filling out a tax form, people will avoid it. Keep it quick, easy, and accessible so anyone can raise a problem. 💰 Focus on the Impact, Not Just the Issue – A missing data field might seem minor, until you realize it’s causing failed transactions worth millions. Capture real business impact upfront to prioritize effectively. 👤 Assign Clear Ownership (Without the “Not My Problem” Dance) – Issues need clear owners, but ownership doesn’t mean one person is stuck fixing everything alone. It means they are responsible for driving the issue forward, with support. 🕵️♂️ Make It Easy to Track Data Back to Its Source – Many issues don’t start where they appear. A bad report might stem from an upstream system error. Having data lineage helps identify root causes faster. 🌱 Fix the Root Cause, Not Just the Symptoms – A patch fix isn’t enough. If sales teams keep entering incorrect data, maybe the CRM fields need better validation or training is required. Solve the problem at its source. 🚀 Build Momentum by Actually Resolving Issues – A long list of unresolved issues kills confidence in the process. Set realistic resolution timelines, track progress, and actually invest in resolving issues. 🧩 Look for Patterns and Fix Systemic Problems – Instead of fixing 100 similar issues separately, find the common denominator and solve it at scale. This is how data teams shift from firefighting to prevention. 🏆 Show Your Impact with Real Metrics (or Anecdotes!) – Want to prove the value of your data governance work? Track and share metrics—number of issues resolved, time saved, revenue protected. This builds buy-in. For the full article ➡️ https://lnkd.in/eWBaWjbX #DataGovernance #DataManagement #DataQuality #Business

😬 Many companies rush to adopt AI-driven solutions but fail to address the fundamental issue of data management first. Few organizations conduct proper data audits, leaving them in the dark about: 🤔 Where their data is stored (on-prem, cloud, hybrid environments, etc.). 🤔 Who owns the data (departments, vendors, or even external partners). 🤔 Which data needs to be archived or destroyed (outdated or redundant data that unnecessarily increases storage costs). 🤔 What new data should be collected to better inform decisions and create valuable AI-driven products. Ignoring these steps leads to inefficiencies, higher costs, and poor outcomes when implementing AI. Data storage isn't free, and bad or incomplete data makes AI models useless. Companies must treat data as a business-critical asset, knowing it’s the foundation for meaningful analysis and innovation. To address these gaps, companies can take the following steps: ✅ Conduct Data Audits Across Departments 💡 Create data and system audit checklists for every centralized and decentralized business unit. (Identify what data each department collects, where it’s stored, and who has access to it.) ✅ Evaluate the lifecycle of your data; what should be archived, what should be deleted, and what is still valuable? ✅ Align Data Collection with Business Goals Analyze business metrics and prioritize the questions you want answered. For example: 💡 Increase employee retention? Collect and store working condition surveys, exit interview data, and performance metrics to establish a baseline and identify trends. ✅ Build a Centralized Data Inventory and Ownership Map 💡 Use tools like data catalogs or metadata management systems to centralize your data inventory. 💡 Assign clear ownership to datasets so it’s easier to track responsibilities and prevent siloed information. ✅ Audit Tools, Systems, and Processes 💡 Review the tools and platforms your organization uses. Are they integrated? Are they redundant? 💡 Audit automation systems, CRMs, and databases to ensure they’re being used efficiently and securely. ✅ Establish Data Governance Policies 💡 Create guidelines for data collection, access, storage, and destruction. 💡 Ensure compliance with data privacy laws such as GDPR, CCPA, etc. 💡 Regularly review and update these policies as business needs and regulations evolve. ✅ Invest in Data Quality Before AI 💡 Use data cleaning tools to remove duplicates, handle missing values, and standardize formats. 💡 Test for biases in your datasets to ensure fairness when creating AI models. Businesses that understand their data can create smarter AI products, streamline operations, and ultimately drive better outcomes. Repost ♻️ #learningwithjelly #datagovernance #dataaudits #data #ai

If you feel like your business operations are feeling sluggish, listen up... Odds are quite high poor data management could be part of the problem. In my experience, companies that struggle with inefficiencies often overlook how their data is being handled. If you want to streamline operations, you must take control of your data. Here are 3 actionable steps to get started: 1️⃣ 𝗖𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 Many businesses store data in silos across different departments. The result? It’s hard to get a clear picture of what’s really going on. The solution is to integrate your data into a central platform. This will eliminate redundancy and create a single source of truth that everyone can access. 𝗔𝗰𝘁𝗶𝗼𝗻 𝘀𝘁𝗲𝗽: 𝘈𝘶𝘥𝘪𝘵 𝘺𝘰𝘶𝘳 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘥𝘢𝘵𝘢 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘵𝘰 𝘪𝘥𝘦𝘯𝘵𝘪𝘧𝘺 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶 𝘩𝘢𝘷𝘦 𝘥𝘶𝘱𝘭𝘪𝘤𝘢𝘵𝘦 𝘰𝘳 𝘪𝘴𝘰𝘭𝘢𝘵𝘦𝘥 𝘥𝘢𝘵𝘢 𝘴𝘰𝘶𝘳𝘤𝘦𝘴. 2️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗱𝗮𝘁𝗮 𝗲𝗻𝘁𝗿𝘆 𝗮𝗻𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Manual data entry is not only slow but also prone to error. Automation tools can help you capture, process, and organize data in real-time. This frees up your team to focus on higher-value tasks. 𝗔𝗰𝘁𝗶𝗼𝗻 𝗦𝘁𝗲𝗽: 𝘐𝘥𝘦𝘯𝘵𝘪𝘧𝘺 𝘳𝘦𝘱𝘦𝘵𝘪𝘵𝘪𝘷𝘦 𝘥𝘢𝘵𝘢 𝘦𝘯𝘵𝘳𝘺 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘦𝘴 𝘪𝘯 𝘺𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘢𝘯𝘥 𝘦𝘹𝘱𝘭𝘰𝘳𝘦 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘭𝘪𝘬𝘦 𝘡𝘢𝘱𝘪𝘦𝘳 𝘰𝘳 𝘗𝘰𝘸𝘦𝘳 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘦 𝘵𝘰 𝘴𝘵𝘳𝘦𝘢𝘮𝘭𝘪𝘯𝘦 𝘵𝘩𝘦𝘮. 3️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘂𝗽-𝘁𝗼-𝗱𝗮𝘁𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 It's essential to work with the most current data to make well-informed decisions. Ensure your analytics are refreshed regularly to give you accurate, up-to-date insights. This allows you to respond to changes and make decisions based on the latest available data, improving your business agility. 𝗔𝗰𝘁𝗶𝗼𝗻 𝘀𝘁𝗲𝗽: 𝘚𝘦𝘵 𝘶𝘱 𝘢 𝘴𝘪𝘮𝘱𝘭𝘦 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥 𝘪𝘯 𝘵𝘰𝘰𝘭𝘴 𝘭𝘪𝘬𝘦 𝘗𝘰𝘸𝘦𝘳 𝘉𝘐 𝘰𝘳 Looker 𝘵𝘰 𝘵𝘳𝘢𝘤𝘬 𝘺𝘰𝘶𝘳 𝘮𝘰𝘴𝘵 𝘤𝘳𝘪𝘵𝘪𝘤𝘢𝘭 𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘪𝘯 𝘳𝘦𝘢𝘭 𝘵𝘪𝘮𝘦. TLDR: Streamlining your operations starts with managing your data effectively. The more accessible and accurate your data is, the faster you can make informed decisions. P.S. What’s the biggest data challenge your business is currently facing? Let me know in the comments!

Data Management must be handled upstream, downstream, and in-between, or ultimately these initiatives will fail. Here's why: Catalogs, Monitoring solutions, testing suites, and even data contracts applied solely to downstream data systems are definitionally reactive. While these tools can detect whether or not schema changes, unexpected events, and other quality issues have occurred, it can be very challenging to root cause problems or take preventative action. This is primarily because downstream tooling detects ALL changes regardless of where they originated in the data supply chain. For example, quality issues may be caused by: 1. Code changes to data generators (events/logs) 2. Code changes to streaming systems (Kafka Topics) 3. Code changes to transactional database structure 4. Unexpected data contents from data generators 5. Unexpected data transformations between source and target 6. Missing/dropped events during flight 7. Pipeline latency caused by timeouts (large file sizes) 8. Changes to 3P platform schemas/data (Salesforce, SAP) 9. Unexpected updates to business logic in SQL 10. Code changes to orchestration jobs And so on and so forth... During some of my conversations with data teams, I've heard that changes initiated by data producers make up more than 50% of ALL data quality issues. If your downstream tooling does a great job at detecting these problems but not preventing them, it ultimately burdens data engineers to become full-time bug bashers who cannot take corrective action! In my mind, this is why Data Management MUST shift left to encompass the entire data supply chain. Downstream is a great starting point and having coverage goes a long way to better understanding the problem of data quality, but to resolve the problem requires ownership at all layers including data sources. Good luck!

Finding Data Blind Spots – It’s Not Magic, It’s Methodology Whether you’re developing a new product or transitioning from engineering to manufacturing, it all comes down to data. The challenge? Blind spots—gaps, inconsistencies, and hidden inefficiencies that slow you down. The solution? A structured approach to data mining: ✅ Catalog Your Data – Identify and document key data sources across PLM, ERP, MES, and beyond. ✅ Map Data Relationships – Understand how data flows between systems and where disconnects occur. ✅ Profile & Cleanse – Use data science techniques like anomaly detection and clustering to find inconsistencies. ✅ Analyze Trends – Apply simple statistical methods to uncover hidden patterns and root causes. ✅ Build a Roadmap – Prioritize fixes, automate workflows, and ensure data-driven decision-making. No more guessing. No more blind spots. Just connected, actionable insights. How are you uncovering your data gaps? #DataScience #PLM #DigitalTransformation #SmartManufacturing #DataStrategy