Data inertia and system limitations

As a data engineer, I have faced many data extraction challenges. Here are few that I remember 😇 1. Schema Nightmares - Schema changes often happen silently, breaking production pipelines without warning. - Development and production environments frequently experience schema drift, causing inconsistencies. - Mixed data types in the same column, like strings in integer fields, can wreak havoc. 2. File Format Hell - Parsing files becomes a nightmare with corrupted compressions or inconsistent delimiters in CSVs. - Hidden formatting in Excel/CSV files and mixed encodings across datasets break standard parsers. - Even structured-looking PDFs can turn out to be image-based, complicating data extraction. 3. API Extraction Headaches - Unpredictable rate limits and undocumented changes in APIs often disrupt pipelines. - Token expiration mid-extraction or inconsistent response formats lead to incomplete data. - Pagination sometimes duplicates or skips data, adding complexity to extraction workflows. 4. Real-Time Data Challenges - Source systems often fail under extraction loads, causing duplicate or missing events. - Out-of-order data arrival and timezone inconsistencies complicate real-time processing. - High-throughput streams can overflow buffers, leading to data loss or delays. 5. Modern Data Infrastructure Complexities - Docker containers and Kubernetes pods often fail during long-running extractions. - Serverless functions face timeout limitations, and cross-region data transfers can inflate costs. - Distributed systems introduce challenges in state management and resource limits during peak loads. 6. Data Quality Surprises - Supposedly “clean” data often contains HTML tags, inconsistent phone formats, or default placeholder values. - Duplicate records with minor differences and non-unique primary keys undermine data integrity. - Reference data frequently fails to align with its source, leading to mismatched results. 7. Compliance & Security Hurdles - Sensitive data often hides in unexpected fields, creating compliance risks. - Encryption requirements and cross-border transfer restrictions introduce operational challenges.

𝗧𝗵𝗲 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 -- 𝗜𝗧/𝗢𝗧 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗨𝗡𝗦 Industrial enterprises are facing the "data paradox", generating petabytes of operational data yet struggling to get real-time, contextualized insights. 𝗧𝗵𝗲 𝗜𝗧/𝗢𝗧 𝗗𝗶𝘃𝗶𝗱𝗲 For decades, #OT and #IT have been working separately due to priority differences: 🔸 𝗢𝗧 - Deterministic control, availability, and uptime. 🔸 𝗜𝗧 - Data storage, security, and scalability. This division led to "spaghetti architectures" following a hierarchical (PLC → SCADA/DCS → Historian → MES → ERP → Cloud → BI) and request-response model relying on hardcoded point-to-point integrations, with rigid and maintenance-heavy infrastructures creating single points of failure and several challenges: 🔸𝗣𝗼𝗹𝗹𝗶𝗻𝗴 𝗜𝗻𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝗶𝗲𝘀 – Cyclical polling (e.g., #OPC DA, #Modbus) introduces latency and creates unnecessary network load. 🔸𝗛𝗶𝗴𝗵 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘀𝘁𝘀 – Middleware solutions (e.g., #ETL pipelines, #APIs) require custom coding and maintenance. 🔸𝗗𝗮𝘁𝗮 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 & 𝗜𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 – Multiple, conflicting data versions emerge across IT and OT. 🔸𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀 – Cloud #DataLakes and #historians struggle to synchronize with real-time, #edge-driven systems. 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲 #UNS is a real-time, event-driven data architecture that centralizes all industrial data into a single, logical namespace, acting as a fully structured, hierarchical data model and a single source of truth that integrates IT, OT, edge, and #cloud ecosystems. Instead of having data residing in application-specific silos, UNS introduces a model that decouples producers and consumers, allowing systems to publish and subscribe to relevant data: 🔸 𝗘𝗱𝗴𝗲-𝗱𝗿𝗶𝘃𝗲𝗻 – All data sources publish updates as they occur. 🔸 𝗘𝘃𝗲𝗻𝘁-𝗯𝗮𝘀𝗲𝗱 – Enables push-based streaming. 🔸 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗲𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 – Systems subscribe to relevant data without direct dependencies on other systems. 𝗞𝗲𝘆 𝗨𝗡𝗦 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 🔸 𝗠𝗤𝗧𝗧 - The de facto transport layer for UNS, enabling asynchronous, distributed, and scalable communications. The #SparkplugB extension tracks device online/offline states, normalizes data across heterogeneous device fleets, and notifies clients when devices go offline. 🔸 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 – Structured, context-rich data organization (e.g., ISA-95 model: Enterprise → Site → Area → Line → Machine → Sensor). 🔸 𝗗𝗲𝗰𝗼𝘂𝗽𝗹𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗲𝗮𝗺𝘀 – No hardcoded connections between systems; they interact dynamically as needed. UNS supports hybrid IT/OT deployments: 🔸𝗘𝗱𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – Pre-processes high-frequency OT data before publishing it to UNS. 🔸𝗖𝗹𝗼𝘂𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 – #AI / #ML models can subscribe to edge-generated insights. ***** ▪ Follow me and ring the 🔔 to stay current on #Industry40 Insights!

Do you want to know what keeps the person with all the data and insights in your company up at night? I host a monthly Rev Ops Peer Group that brings together dozens of leaders to discuss what's going on in their business. Here are the key challenges that continue to come up: 1) Fragmented Data Across Systems: Many teams are dealing with data scattered across different tools (CRM, LMS, sales enablement platforms, etc.), making it hard to consolidate insights. As one member put it, "we have so many different systems that track different data that there's no one easy way to just click a button and say, here's my dashboard where it has everything I need". 2) Inconsistent Metric Definitions: Different teams may use different terms or definitions for the same metrics, complicating reporting. This challenge was mentioned alongside the difficulty of pulling clean, comparable data sets across the org. 3) Proving Enablement and Training Impact: A persistent issue is showing how enablement programs and training translate to business outcomes beyond onboarding. While ramp time is often well-tracked, broader enablement effectiveness—especially linking to quota attainment or win rates—is harder to quantify. 4) Overwhelming or Unstructured Data: Even when data is available, there's sometimes too much of it, or no clear cadence to assess its impact. One RevOps leader described struggling with when and how to review and iterate based on the data collected. 5) Lack of Leadership Buy-In or Action: Even with data available, without leadership acting on it—whether for training completion or enforcing enablement programs—there’s limited impact. If I were to summarize them, it would look like this: 👉 Enablement and RevOps leaders are sitting on valuable insights—but can’t always activate them. 👉 Organizational alignment (around tools, metrics, and priorities) is still a massive gap. 👉 As companies scale, the cost of this misalignment grows exponentially.