How to Improve Pipeline Visibility

Change Management is the missing piece in most data ecosystems. I often repeat a quote: "If nothing ever changes, nothing ever breaks." In other words, quality issues are almost a result of change occurring at some point in the data pipeline. For example: 1. Application code can change by adding features, removing features, updating business logic, or changing data ingestion points 2. Application events can change by creating or removing events, updating event schemas, modifying how frequently events are emitted, or changing the conditions under which events are emitted 3. Database code could change by altering schemas, creating new databases/tables, adding columns that may or may not be versions of the previous columns, making fields Nullable, and much more 3. Pipelines/processing steps can change by modifying transformations, adjusting dependencies, adding or removing filtering logic, restructuring aggregations, or altering orchestration logic. 4. Stream processing jobs can change by updating windowing strategies, modifying partitioning keys, adjusting stateful processing logic, or changing the underlying infrastructure. 5. File storage structures can change by restructuring folder hierarchies, modifying file formats (for example, JSON to Parquet), adjusting partitioning schemes, or changing retention policies. 6. Medallion architecture layers can change by altering how data progresses from raw to refined layers, introducing new validation checks, enforcing stricter governance, or restructuring tables. 7. Data warehouse tables & transformations can change by modifying joins, adjusting business logic in transformations, deprecating or renaming tables, or changing partitioning strategies. 8. Data products can change by evolving definitions, updating metrics, shifting ownership, or integrating new data sources. Now consider that the above list is merely a fraction of the many changes that can and do emerge daily in a data ecosystem. These changes are usually untracked and invisible to all other participants in the data supply chain. They are not audited, which makes root-causing failures incredibly difficult. Most importantly, the more data debt accumulates in a downstream system, the more ripple effects even a single change will have. My recommendation? Begin with visibility first. Start by creating the systems to track changes over time at every layer of technology in your pipeline. Capture information such as: 1. What data object was changed? 2. Who changed it? 3. What was the diff between version 1 & 2? 4. What were the expectations of the consumers of this object? 5. Was this change communicated in advance? Once you have this information, you can begin to correlate changes with outages, governance issues, failed compliance audits, untrustworthy data, and other instances of low quality. Good luck!

Your supply chain isn’t a list of vendors. It’s a network, so start treating it like one. Disconnected systems create blind spots.  Delays, shortages, and unexpected failures can ripple through operations. Graphs and graph databases provide a smarter way forward. Here’s how: 📍 Supply Chain Visibility ↳ Graphs connect suppliers, transport routes, and logistics hubs into a single, real-time view. ↳ This helps leaders detect bottlenecks early and take action before small issues escalate. 🚦 Optimized Route Planning ↳ Graphs analyze real-time conditions including traffic, weather, and transport availability to instantly compute the best alternative routes when disruptions occur. ↳ This minimizes delays and reduces costs. 🔍 Fraud & Anomaly Detection ↳ Graphs connect financial transactions, supplier activity, and shipment patterns to detect hidden irregularities. ↳ By seeing the entire network, businesses can identify risks before they become costly problems. 🤝 Supplier Network Intelligence ↳ Graphs uncover deep interdependencies in the supply chain. ↳ This helps businesses anticipate risks, reduce vulnerabilities, and negotiate from a position of strength. 🔧 Predictive Maintenance ↳ Graphs combine sensor data, maintenance logs, and historical trends to predict breakdowns before they happen. ↳ This prevents costly downtime and ensures a more reliable supply chain. 📦 Adaptive Supply Planning ↳ Graphs enable real-time “what-if” simulations that adjust sourcing strategies based on demand fluctuations, supplier availability, and external shocks. ↳ This allows businesses to stay agile and resilient. These reasons are why at data² we built the reView platform on the foundation of a graph database. Connected data is driving the future of logistics and supply chain planning. 💬 What’s the biggest challenge you’ve faced managing your supply chain? Share your thoughts below. ♻️ Know someone dealing with complex logistics? Share this post to help them out. 🔔 Follow me Daniel Bukowski for daily insights about delivering value from connected data.

Your 3PL isn't just a vendor—it's the single biggest lever you're ignoring. We ship 35,000 orders monthly with a tiny 0.16% error rate. Here's how we turned our logistics from a headache into a competitive advantage... When I tell other founders our error rate is 0.16%, they don't believe me. That's just 50 mistakes in 35,000 monthly orders. If we had settled for “industry standard” rates of 1-3%, that would = 350 to 1050. This isn't luck. It's the difference between a service provider and a true partner in your business. Most founders underestimate the complexity behind the scenes: • Multiple product variations • Bundled offers • Flash promotions • Custom inserts • Subscription management • Retail allocation One small mistake in any of these compounds into customer service nightmares, retention issues, negative brand perception, and cash flow problems. Here’s how we deal with this → Our 3PL built us an HOURLY inventory tracking system with near-perfect accuracy for over six years. Do you obsess over attribution models for your marketing spend but accept outdated weekly inventory counts? That's madness. Real-time inventory visibility changes EVERYTHING: • Confident marketing decisions • Better cash flow management • Proactive stock planning • Prevention of stockouts But the biggest unlock isn't technology — it's communication. We have 20+ dedicated Slack channels with our 3PL team, designed to help organize every possible scenario from customs delays to bundle changes. When something critical happens, we can text their leadership directly. We actually take it on step further with them and have our main rep, JOIN our weekly all hands. This way there are no surprises for them (or us). EVER. Now, tell me a 3PL that's willing to do that? This isn't standard. This is a TRUE partnership. Something you need to forge with your key supply chain vendors. The moment I knew we had something special came a few years ago... When we were about to stock out during a major promotion, our account manager called me at 11pm. But it wasn’t to report the problem, it was to share their solution. ❤️ Other reasons our 3PL works for us: → They're centrally located in the US, providing exceptional blended shipping rates. → Their pricing model has no surprises - we know our costs based on order volume. But most importantly, they treat Obvi like their own business. If you're in the market for a 3PL that truly understands DTC brands and can scale with you, DM me. I don't recommend partners lightly, but this relationship has been transformative for our business.