How to Improve Data Management Processes

After building 10+ data warehouses over 10 years, I can teach you how to keep yours clean in 5 minutes. Most companies have messy data warehouses that nobody wants to use. Here's how to fix that: 1. Understand the business first Know how your company makes money • Meet with business stakeholders regularly • Map out business entities and interactions  • Document critical company KPIs and metrics This creates your foundation for everything else. 2. Design proper data models Use dimensional modeling with facts and dimensions • Create dim_noun tables for business entities • Build fct_verb tables for business interactions • Store data at lowest possible granularity Good modeling makes queries simple and fast. 3. Validate input data quality Check five data verticals before processing • Monitor data freshness and consistency • Validate data types and constraints • Track size and metric variance Never process garbage data no matter the pressure. 4. Define single source of truth Create one place for metrics and data • Define all metrics in data mart layer • Ensure stakeholders use SOT data only • Track data lineage and usage patterns This eliminates "the numbers don't match" conversations. 5. Keep stakeholders informed Communication drives warehouse adoption and resources • Document clear need and pain points • Demo benefits with before/after comparisons • Set realistic expectations with buffer time • Evangelize wins with leadership regularly No buy-in means no resources for improvement. 6. Watch for organizational red flags Some problems you can't solve with better code • Leadership doesn't value data initiatives • Constant reorganizations disrupt long-term projects • Misaligned teams with competing objectives • No dedicated data team support Sometimes the solution is finding a better company. 7. Focus on progressive transformation Use bronze/silver/gold layer architecture • Validate data before transformation begins • Transform data step by step • Create clean marts for consumption This approach makes debugging and maintenance easier. 8. Make data accessible Build one big tables for stakeholders • Join facts and dimensions appropriately • Aggregate to required business granularity • Calculate metrics in one consistent place Users prefer simple tables over complex joins. Share this with your network if it helps you build better data warehouses. How do you handle data warehouse maintenance? Share your approach in the comments below. ----- Follow me for more actionable content. #DataEngineering #DataWarehouse #DataQuality #DataModeling  #DataGovernance #Analytics

I always say that "data quality is a people and process challenge masquerading as a technical challenge." First and foremost, you have to understand what the business cares about. Do these issues impact the wider business enough to warrant resolving this, or does the pain of a couple of data engineers suffice (learn to pick and choose your battles)? Assuming it's worth solving, you need to follow the chain of events that go from data entry, to ingestion, and then to landing in your database of interest. Then determine which parts of the chain you control and don't. The areas you don't control are going to be where the real change needs to happen. You need to get out of the safety of code and databases and start talking to the teams to understand their processes and how to incentivize them to change. Not doing this means you will constantly put a technical band-aid over continually changing garbage data. How have I done this before? In a previous role I was combining sales, product, and customer success (CS) time tracking data. Being in B2B SaaS means expansions were a big deal and were managed by CS. I quickly found the time tracking data was awful, and looking at the time stamps of "tracked time" and "time it was entered" showed that everyone was dumping data at the end of the quarter. How do I get them to improve time tracking (despite doing such is awful)? Well I analyzed the data and saw that some employees were overworked while others didn't have enough hours (imbalanced account assignments). In addition, some accounts were time intensive but low contract size (ie problematic customers). This got the attention of leadership, who gave me two CS staff to work on this project of improving time tracking data. I empowered those two CS staff with data and building a business case, and let them present to the CS org and take all the credit (I don't care that people know its me, I just want good data to make my life easier). Coming from their peers instead of me or leadership was way more powerful too! The driver? If you consistently submit time tracking data, we will ensure you don't waste time on problematic accounts, you will be overworked less, and or have more opportunities for meaningful work as leadership will know how to allocate better. Not a single line of data quality code written... just getting in the weeds of the business and incentivizing people to change their behaviors that help them create better data. I hope this helps! --- 👋🏽 Hi! I'm 🎯 Mark Freeman, a data scientist turned data engineer obsessed with data quality. Click "follow" on my profile if you want more content like this in your feed!

This week, I want to talk about something that might not be the most exciting or sexy topic—it might even seem plain boring to some of you. Very impactful, yet even in many large and complex organizations with tons of data challenges this foundational data process simply doesn’t exist: the Data Issue Management Process. Why is this so critical? Because #data issues, such as data quality problems, pipeline breakdowns, or process inefficiencies, can have real business consequences. They cause manual rework, compliance risks, and failed analytical initiatives. Without a structured way to identify, analyze, and resolve these issues, organizations waste time duplicating efforts, firefighting, and dealing with costly disruptions. The image I’ve attached outlines my take on a standard end-to-end data issue management process, broken down below: 📝 Logging the Issue – Make it simple and accessible for anyone in the organization to log an issue. If the process is too complicated, people will bypass it, leaving problems unresolved. ⚖️ Assessing the Impact – Understand the severity and business implications of the issue. This helps prioritize what truly matters and builds a case for fixing the problem. 👤 Assigning Ownership – Ensure clear accountability. Ownership doesn’t mean fixing the issue alone—it means driving it toward resolution with the right support and resources. 🕵️♂️ Analyzing the Root Cause – Trace the problem back to its origin. Most issues aren’t caused by systems, but by process gaps, manual errors, or missing controls. 🛠️ Resolving the Issue – Fix the data AND the root cause. This could mean improving data quality controls, updating business processes, or implementing technical fixes. 👀 Tracking and Monitoring – Keep an eye on open issues to ensure they don’t get stuck in limbo. Transparency is key to driving resolution. 🏁 Closing the Issue and Documenting the Resolution – Ensure the fix is verified, documented, and lessons are captured to prevent recurrence. Data issue management might not be flashy, but it can be very impactful. Giving business teams a place to flag issues and actually be heard, transforms endless complaints (because yes, they do love to complain about “the data”) into real solutions. And when organizations step back to identify and fix thematic patterns instead of just one-off issues, the impact can go from incremental to game-changing. For the full article ➡️ https://lnkd.in/eWBaWjbX #DataGovernance #DataManagement #DataQuality #BusinessEfficiency