Ensuring Data Quality

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝗻'𝘁 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗵𝗲𝗰𝗸 －it's a continuous contract enforced across the various data layers to avoid breakage. Think about it. Planes don’t just fall out of the sky when they land. Crashes happen when people miss the little signals that get brushed off or ignored. Same thing with data. Bad data doesn’t shout; it just drifts quietly—until your decisions hit the ground. When you bake quality checks into every layer and, actually use observability tools, You end up with data pipelines that hold up. Even when things get messy. That’s how you get data people can trust. Why does this matters? Bad data costs money → Failed ML models, wrong decisions. Good monitoring catches 90% of issues automatically. → Raw Materials (Ingestion)  • Inspect at the dock before accepting delivery.  • Check schemas match expectations. Validate formats are correct.  • Monitor stream lag and file completeness. Catch bad data early.  • Cost of fixing? Minimal here, expensive later.  • Spot problems as close to the source as you can. → Storage (Raw Layer)  • Verify inventory matches what you ordered.  • Confirm row counts and volumes look normal.  • Detect anomalies: sudden spikes signal upstream issues.  • Track metadata: schema changes, data freshness, partition balance.  • Raw data is your backup plan when things go sideways. → Processing (Transformation)  • Quality control during assembly is critical.  • Validate business rules during transformations. Test derived calculations.  • Check for data loss in joins. Monitor deduplication effectiveness.  • Statistical profiling reveals outliers and distribution shifts.  • Most data disasters start right here. → Packaging (Cleansed Data)  • Final inspection before shipping to warehouse.  • Ensure master data consistency across all sources.  • Validate privacy rules: PII masked, anonymization works.  • Verify referential integrity and temporal logic.  • Clean doesn’t always mean correct. Keep checking. → Distribution (Published Data)  • Quality assurance for customer-facing products.  • Check SLAs: freshness, availability, schema contracts met.  • Monitor aggregation accuracy in data marts.  • ML models: detect feature drift, prediction degradation.  • Dashboards: validate calculations match source data.  • Once data is published, you’re on the hook. → Cross-Cutting Layers (Force Multipliers)  • Metadata: rules, lineage, ownership, quality scores  • Monitoring: freshness, volume, anomalies, downtime  • Orchestration: dependencies, retries, SLAs  • Logs: failures, patterns, early warning signs Honestly, logs are gold. Don’t sleep on them. What's your job? Design checkpoints, not firefight data incidents. Quality is built in, not inspected in. Pipelines just 𝗺𝗼𝘃𝗲 data. Quality 𝗽𝗿𝗼𝘁𝗲𝗰𝘁𝘀 your decisions. Image Credits: Piotr Czarnas 𝘌𝘷𝘦𝘳𝘺 𝘭𝘢𝘺𝘦𝘳 𝘯𝘦𝘦𝘥𝘴 𝘪𝘯𝘴𝘱𝘦𝘤𝘵𝘪𝘰𝘯.  𝘚𝘬𝘪𝘱 𝘰𝘯𝘦, 𝘳𝘪𝘴𝘬 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘥𝘰𝘸𝘯𝘴𝘵𝘳𝘦𝘢𝘮.

As data engineers, we often talk about scalability, performance, and automation — but there’s one thing that silently determines the success or failure of every pipeline: Data Quality. No matter how advanced your stack, if your data is inconsistent, incomplete, or inaccurate, your downstream dashboards, ML models, and decisions will all be compromised. Here’s a detailed list of 25 critical checks that every modern data engineer should implement 👇 🔹 1. Null or Missing Value Checks Ensure no essential field (like customer_id, transaction_id) contains missing data 🔹 2. Primary Key Uniqueness Validation Verify that key columns (like IDs) remain unique to prevent duplicate business entities or revenue double counting. 🔹 3. Duplicate Record Detection Detect duplicates across ingestion stages 🔹 4. Referential Integrity Validation Confirm that all foreign key relationships hold true 🔹 5. Data Type Validation Ensure incoming data matches schema definitions — no strings in numeric fields, no invalid dates. 🔹 6. Numeric Range Validation Catch impossible values (e.g., negative ages, >100% percentages, invalid ratings). 🔹 7. String Length & Pattern Checks Enforce length constraints and validate formats (emails, phone numbers, IDs) with regex rules. 🔹 8. Allowed Value / Domain Validation Ensure categorical columns only contain valid entries — e.g., gender ∈ {‘M’, ‘F’, ‘Other’}. 🔹 9. Business Rule Consistency Check rules like order_amount = item_price * quantity or revenue = sum(product_sales). 🔹 10. Cross-Column Consistency Validate logical dependencies — e.g., delivery_date ≥ order_date. 🔹 11. Timeliness / Freshness Checks Detect data delays and SLA breaches — especially important for near real-time systems. 🔹 12. Completeness Check Verify all partitions, expected files, or dates are present — no missing data slices. 🔹 13. Volume Check Against Historical Data Compare record counts or data sizes vs previous runs to detect anomalies in ingestion. 🔹 14. Statistical Distribution Checks Validate stability of metrics like mean, median, and standard deviation to catch silent drifts. 🔹 15. Outlier Detection Identify records that deviate significantly from normal ranges 🔹 16. Schema Drift Detection Automatically detect added, removed, or renamed columns — common in dynamic source systems. 🔹 17. Duplicate File Ingestion Check Prevent reprocessing of already-loaded files or data across multiple sources. 🔹 18. Negative / Invalid Value Checks Block impossible values like negative prices or zero quantities where not allowed. 🔹 19. Percentage / Total Consistency Check Ensure calculated percentages correctly sum to 100% or totals match constituent values. 🔹 20. Hierarchy Validation Validate hierarchical consistency. 🔹 21. Audit Column Consistency Confirm audit columns like created_by, updated_at, and load_date are properly populated. #DataEngineering #DataQuality #Databricks #ETL #DataPipelines #DataGovernance

Let's talk about the elephant in the data room: You can't purchase your way to clean data. No tool, platform, or governance framework will magically fix your data quality issues. Only doing the work will. I've watched organizations pour thousands and even millions into cutting-edge data management tools and meticulously crafted governance frameworks. Yet years later, many are still grappling with the same problems: Data quality isn't where it needs to be. Data isn't documented. Data can't be connected. Why? Because the proponents of tools and frameworks are missing a core truth: Data quality is a human challenge at its heart. The real key to data quality lies in: ◾ How your teams communicate and collaborate and whether your departments even speak the same data language. ◾ How well your organization builds bridges between technical and business teams. ◾ Whether your employees understand why data quality matters and have meaningful incentives to care. To be clear: tools can help. But they won't create good data entry practices, foster cross-departmental collaboration, or build a culture of data ownership. And they certainly can't replace human judgment, no matter how "AI-powered" they claim to be. Real transformation begins with three fundamental questions: 1️⃣ Is the impact of data quality on the business understood in concrete terms, as in "value potential" and "value at risk" (not some abstract notion like "you need it for AI")? 2️⃣ Does everyone understand the impact of their role in data quality and the impact of data quality on their role? Again, this must be concrete and connected to daily work, not abstract like "it's important for the company." 3️⃣ Have you thoughtfully designed incentives for caring about data quality? (Or do you expect it to somehow emerge from everything else you're doing?) Building a culture of data stewardship means more than giving a few people fancy titles and occasionally inviting them for pizza. And measuring true quality requires looking beyond metrics and KPIs (after all, it's human nature to find ways to meet metrics, whether or not that achieves the actual goal). All too often, data quality is treated as "yes, it's important—among these other five priorities." That's a trap. It's either a priority or it isn't. The path to better data isn't paved with shortcuts. It requires rolling up your sleeves and doing the real work. When it comes to data quality, stop chasing silver bullets. Start investing in what truly matters: your people and the culture of quality they create. Either way, the results will speak for themselves.

9 years ago, I filed a patent for something that sounds embarrassingly simple: Using machine learning to cleanse data. Out of all the patents I've worked on - spanning data governance, enterprise management, and AI applications, this one felt almost too basic to pursue. Definitely not the kind of breakthrough that makes headlines. But here's the thing - sometimes the most obvious solutions today were completely invisible problems yesterday. Picture this: It's 2016, and companies are drowning in dirty customer data. CRM systems filled with duplicate records, misspelled names, inconsistent formats. Sales teams couldn't reach customers. Marketing campaigns were hitting dead ends. Billing was a nightmare. The standard approach? Hire armies of interns to manually clean spreadsheets. Or invest in expensive data stewards who'd spend months creating rules-based systems that broke every time the data changed. Meanwhile, I'm sitting there thinking: "Wait, machine learning models are getting really good at pattern recognition. Why aren't we using them to automatically detect and fix data quality issues?" It felt so obvious to me that I almost didn't pursue the patent. But "obvious" in hindsight is usually breakthrough thinking in real-time. That patent became the foundation for data cleansing algorithms that could automatically identify duplicate customer records, standardize address formats, and fix attribution errors. All without human intervention. Today, every major CRM platform uses some variation of ML-powered data cleansing. What seemed like an obvious solution in 2016 is now standard practice across the industry. The lesson? The best innovations often solve problems hiding in plain sight. They're not the flashy, headline-grabbing breakthroughs. They're the practical solutions that make everyone's job easier even if they sound "boring" at first.

The only way to prevent data quality issues is by helping data consumers and producers communicate effectively BEFORE breaking changes are deployed. To do that, we must first acknowledge the reality of modern software engineering: 1. Data producers don’t know who is using their data and for what 2. Data producers don’t want to cause damage to others through their changes 3. Data producers do not want to be slowed down unnecessarily Next, we must acknowledge the reality of modern data engineering: 1. Data engineers can’t be a part of every conversation for every feature (there are too many) 2. Not every change is a breaking change 3. A significant number of data quality issues CAN be prevented if data engineers are involved in the conversation What these six points imply is the following: If data producers, data consumers, and data engineers are all made aware that something will break before a change has deployed, it can resolve data quality through better communication without slowing anyone down while also building more awareness across the engineering organization. We are not talking about more meaningless alerts. The most essential piece of this puzzle is CONTEXT, communicated at the right time and place. Data producers: Should understand when they are making a breaking change, who they are impacting, and the cost to the business Data engineers: Should understand when a contract is about to be violated, the offending pull request, and the data producer making the change Data consumers: Should understand that their asset is about to be broken, how to plan for the change, or escalate if necessary The data contract is the technical mechanism to provide this context to each stakeholder in the data supply chain, facilitated through checks in the CI/CD workflow of source systems. These checks can be created by data engineers and data platform teams, just as security teams create similar checks to ensure Eng teams follow best practices! Data consumers can subscribe to contracts, just as software engineers can subscribe to GitHub repositories in order to be informed if something changes. But instead of being alerted on an arbitrary code change in a language they don’t know, they are alerted on breaking changes to the metadata which can be easily understood by all data practitioners. Data quality CAN be solved, but it won’t happen through better data pipelines or computationally efficient storage. It will happen by aligning the incentives of data producers and consumers through more effective communication. Good luck! #dataengineering

The EU published its five-year progress report on the Green Deal. Can we really afford far-reaching changes through the #omnibus with these results? Out of the 154 assessed Green Deal targets only 32 are "on track", 64 are identified as "acceleration needed", 15 are "not progressing" or "regressing", and for 43 targets data is not even available (see image). This report shows where Europe stands with the Green Deal. Yes, there is some progress, but the tasks ahead are monumental, especially on biodiversity, smart mobility and climate action. We need a robust data infrastructure around sustainability and therefore regulations like #CSRD and the #EUTaxonomy are of significant importance to collect and analyse data from the corporate sphere. The report emphasises: "Data and knowledge gaps remain on ecosystem condition and pressures: more knowledge of the value of natural capital and the cause effect relationships between socio-economic systems and ecosystems is needed to systematically integrate into policy and investment decisions." This is why we need a proportionate omnibus simplification strategy, and not simplistic reporting or deregulation... === Full Report: https://lnkd.in/dKzD7873 Press Release: https://lnkd.in/dfHTmDP8 #sustainability, #esg, #eugreendeal

Would you live in a home where someone else holds the keys?   That’s the essence of data sovereignty: ensuring that your most valuable information: such as customer records, IP, and financials, remains under your legal, operational, and strategic control.   It’s like making sure the keys to your digital home stay in your hands.   AI thrives on data. It feeds algorithms, shapes outcomes, and influences real-world actions. But when that data is stored in environments governed by external jurisdictions, you risk losing visibility, agility, and trust.   The goal isn’t to avoid the cloud, but to use it with sovereignty in mind.   In our daily work, we support CIOs and organizations in building infrastructure and data strategies that are local, trusted, and aligned with company’s values and regulations.   🔐 Data sovereignty means knowing who's at the door and who holds the key.   That’s how CIOs can secure the foundation and give leadership the clarity and control needed to make data - and AI-driven decisions, securely.   #DataSovereignty #AI #Leadership #iwork4dell

One of the quietest but most important conversations in the Department of War is not about drones, LLMs, or autonomous agents. It is about data. Specifically, how we label it, tag it, and standardize it across the enterprise so our future AI systems can actually learn, operate, and make decisions. At SOCPAC, we learned this lesson the hard way. You cannot scale autonomy unless you first standardize the data that feeds the models. Here is why a Department-wide Data Standardization is no longer optional. It is the prerequisite for unlocking LLM-enabled planning assistants, computer vision-based targeting systems, autonomous UxS swarms, and resilient multi-agent operations. The way I see it, the models and autonomous systems are just commodities, and they are only as useful as our data is organized. Why Labeling and Metadata Tagging Matter LLMs and CV models do not learn from raw data. They learn from structured, labeled, and standardized data. If every organization uses different schemas and naming conventions, the models cannot generalize. If metadata is missing or inconsistent, autonomous systems cannot reason with confidence. As you can imagine Industry has figured this out years ago. Every high-performing AI company has a central data governance function responsible for: • Global taxonomies and data dictionaries • Unified metadata schemas • Automated labeling pipelines • Standardized APIs for every system • Platform-independent data transport layers The Public Sector has to adopt these practices now. Not in 2030. The Bottom Line We cannot build autonomous UxS fleets, multi-agent coordination systems, or LLM-driven workflows without clean, labeled, standardized data flowing through open interfaces. Data standardization is national defense.

Dear #DataEngineers, No matter how confident you are in your SQL queries or ETL pipelines, never assume data correctness without validation. ETL is more than just moving data—it’s about ensuring accuracy, completeness, and reliability. That’s why validation should be a mandatory step, making it ETLV (Extract, Transform, Load & Validate). Here are 20 essential data validation checks every data engineer should implement (not all pipeline require all of these, but should follow a checklist like this): 1. Record Count Match – Ensure the number of records in the source and target are the same. 2. Duplicate Check – Identify and remove unintended duplicate records. 3. Null Value Check – Ensure key fields are not missing values, even if counts match. 4. Mandatory Field Validation – Confirm required columns have valid entries. 5. Data Type Consistency – Prevent type mismatches across different systems. 6. Transformation Accuracy – Validate that applied transformations produce expected results. 7. Business Rule Compliance – Ensure data meets predefined business logic and constraints. 8. Aggregate Verification – Validate sum, average, and other computed metrics. 9. Data Truncation & Rounding – Ensure no data is lost due to incorrect truncation or rounding. 10. Encoding Consistency – Prevent issues caused by different character encodings. 11. Schema Drift Detection – Identify unexpected changes in column structure or data types. 12. Referential Integrity Checks – Ensure foreign keys match primary keys across tables. 13. Threshold-Based Anomaly Detection – Flag unexpected spikes or drops in data volume or values. 14. Latency & Freshness Validation – Confirm that data is arriving on time and isn’t stale. 15. Audit Trail & Lineage Tracking – Maintain logs to track data transformations for traceability. 16. Outlier & Distribution Analysis – Identify values that deviate from expected statistical patterns. 17. Historical Trend Comparison – Compare new data against past trends to catch anomalies. 18. Metadata Validation – Ensure timestamps, IDs, and source tags are correct and complete. 19. Error Logging & Handling – Capture and analyze failed records instead of silently dropping them. 20. Performance Validation – Ensure queries and transformations are optimized to prevent bottlenecks. Data validation isn’t just a step—it’s what makes your data trustworthy. What other checks do you use? Drop them in the comments! #ETL #DataEngineering #SQL #DataValidation #BigData #DataQuality #DataGovernance