Ensuring Data Quality

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.

Here are a few simple truths about Data Quality: 1. Data without quality isn't trustworthy 2. Data that isn't trustworthy, isn't useful 3. Data that isn't useful, is low ROI Investing in AI while the underlying data is low ROI will never yield high-value outcomes. Businesses must put an equal amount of time and effort into the quality of data as the development of the models themselves. Many people see data debt as another form of technical debt - it's worth it to move fast and break things after all. This couldn't be more wrong. Data debt is orders of magnitude WORSE than tech debt. Tech debt results in scalability issues, though the core function of the application is preserved. Data debt results in trust issues, when the underlying data no longer means what its users believe it means. Tech debt is a wall, but data debt is an infection. Once distrust drips in your data lake, everything it touches will be poisoned. The poison will work slowly at first and data teams might be able to manually keep up with hotfixes and filters layered on top of hastily written SQL. But over time, the spread of the poison will be so great and deep that it will be nearly impossible to trust any dataset at all. A single low-quality data set is enough to corrupt thousands of data models and tables downstream. The impact is exponential. My advice? Don't treat Data Quality as a nice to have, or something that you can afford to 'get around to' later. By the time you start thinking about governance, ownership, and scale it will already be too late and there won't be much you can do besides burning the system down and starting over. What seems manageable now becomes a disaster later on. The earliest you can get a handle on data quality, you should. If you even have a guess that the business may want to use the data for AI (or some other operational purpose) then you should begin thinking about the following: 1. What will the data be used for? 2. What are all the sources for the dataset? 3. Which sources can we control versus which can we not? 4. What are the expectations of the data? 5. How sure are we that those expectations will remain the same? 6. Who should be the owner of the data? 7. What does the data mean semantically? 8. If something about the data changes, how is that handled? 9. How do we preserve the history of changes to the data? 10. How do we revert to a previous version of the data/metadata? If you can affirmatively answer all 10 of those questions, you have a solid foundation of data quality for any dataset and a playbook for managing scale as the use case or intermediary data changes over time. Good luck! #dataengineering

It starts with one missing value, one duplicate row… and suddenly your entire system can’t be trusted. Because data issues don’t fail loudly. They compound silently. Here’s what keeps pipelines reliable 👇 - Null value checks Missing fields in key columns can quietly break logic and downstream outputs. - Duplicate checks Repeated records distort metrics, models, and business decisions. - Primary key validation Every record must be unique, or nothing stays consistent. - Referential integrity Broken relationships between tables lead to incorrect joins and insights. - Data type & format validation Wrong formats or types cause subtle but costly errors. - Range & outlier checks Values outside expected limits often signal deeper issues. - Freshness & volume checks Unexpected delays or spikes usually point to upstream failures. - Schema change detection Even small structural changes can break entire pipelines. - Distribution drift checks Data patterns shifting over time can silently degrade models. - Business rule validation If domain logic breaks, the output becomes unreliable. - Aggregation & historical checks Totals and trends must stay consistent across layers and over time. Data quality issues don’t crash systems. They corrupt them. What’s the one check your pipeline is missing right now? Follow Sumit Gupta for more such insights!!

Reliability, evaluation, and “hallucination anxiety” are where most AI programmes quietly stall. Not because the model is weak. Because the system around it is not built to scale trust. When companies move beyond demos, three hard questions appear: →Can we rely on this output? →Do we know what “good” actually looks like? →How much human oversight is enough? The fix is not better prompting. It is a strategy and operating discipline. 𝐅𝐢𝐫𝐬𝐭: ⁣Define reliability like a product, not a vibe. Every serious AI use case should have a one-page SLO sheet with measurable targets across: →Task success ↳Right-first-time rate and rubric-based acceptance →Factual grounding ↳Evidence coverage and unsupported-claim tracking →Safety and compliance ↳Policy violations and PII leakage →Operational quality ↳Latency, cost per task, escalation to humans Now “good” is no longer opinion. It is observable. 𝐒𝐞𝐜𝐨𝐧𝐝:  evaluation must be continuous, not a one-off demo test. Use a simple loop: 𝐏lan: Define rubrics, datasets, and risk tiers 𝐃⁣o: Run offline evaluations and limited pilots 𝐂heck: Monitor drift and regressions weekly 𝐀ct: Update prompts, data, guardrails, and workflows Support this with an AI test pyramid: →Unit checks for prompts and tool behaviour →Scenario tests for real edge failures →Regression benchmarks to prevent backsliding →Live monitoring in production Add statistical control charts, and you can detect silent degradation before users do. 𝐓𝐡𝐢𝐫𝐝: reduce hallucinations by design. →Run a short failure-mode workshop and engineer controls: →Require retrieval or evidence before answering →Allow safe abstention instead of confident guessing →Add claim checking and tool validation →Use structured intake and clarifying flows You are not asking the model to behave. You are designing a system that expects failure and contains it. 𝐅𝐨𝐮𝐫𝐭𝐡: make human-in-the-loop affordable. Tier risk: →Low risk: Light sampling →Medium risk: Triggered review →High risk: Mandatory approval Escalate only when signals demand it: low confidence, missing evidence, policy flags, or novelty spikes. Review becomes targeted, fast, and a source of improvement data. 𝐅𝐢𝐧𝐚𝐥𝐥𝐲: Operate it like a capability. Track outcomes, risk, delivery speed, and cost on a single dashboard. Hold a short weekly reliability stand-up focused on regressions, failure modes, and ownership. What you end up with is simple: ↳Use case catalogue with risk tiers ↳Clear SLOs and error budgets ↳Continuous evaluation harness ↳Built-in controls ↳Targeted human review ↳Reliability cadence AI does not scale on intelligence alone. It scales on measurable trust. ♻️ Share if you found thisuseful. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #AI #AIReliability #TrustAtScale #OperationalExcellence

You've heard "garbage in, garbage out" a thousand times. But here's what that actually means: your fancy dashboard is only as good as the data behind it. Quantity is easy to measure—it's just Terabytes. But data quality? Quality is the hard part because it requires discipline, process, and ownership. Data quality and governance are no longer “nice-to-haves.” They define trust across the organization. → Growing demand due to privacy laws like GDPR and CCPA → Core skill required for roles like Data Engineer, Steward, and Architect → Tools like Collibra and Great Expectations now appear in almost every data job description Some numbers speak for themselves: → Data Quality Engineer roles growing 40%+ yearly → Governance Analysts earning around $80K–$120K → Chief Data Officers often crossing $200K+ Clean data isn’t just accuracy—it’s career growth and company credibility. What Good Data Quality Looks Like? Skip the theory. Here's what actually works: → Automated checks that catch issues before they spread → Validation rules that reject bad data at the source → Tracking where data comes from and where it goes → Alerts when something breaks (not after it's been broken for weeks) → Clear ownership so someone actually fixes problems Where in the real world it shows up? 👉This isn't abstract. Here's where data quality makes or breaks things: → Finance: Try explaining bad compliance data to auditors → Healthcare: Patient records need to be right, every time → Retail: Wrong inventory data means lost sales or wasted stock → ML projects: Your model is only as smart as your training data The Real Talk: Data quality feels boring until it's missing. Then suddenly everyone cares. It's not sexy work. Nobody celebrates when pipelines validate correctly. But it's the foundation everything else sits on. Gartner says organizations with formal data governance will see 30% higher ROI by 2026. As data engineers, that’s our call to design solutions that "𝘥𝘰𝘯’𝘵 𝘫𝘶𝘴𝘵 𝘮𝘰𝘷𝘦 𝘥𝘢𝘵𝘢, 𝘣𝘶𝘵 𝘮𝘰𝘷𝘦 𝘵𝘳𝘶𝘴𝘵." Honestly, I feel it's probably more if you count all the fires you don't have to fight. 👉 Folks I admire in this space - George Firican Dylan Anderson Piotr Czarnas 🎯 Mark Freeman II Chad Sanderson Here's a crisp guide on Data Quality & Governance for data engineers! 👇 What's the most annoying, recurring data quality issue you've had to fix lately? I'll go first: dates stored as strings. 🤦♂️

You don’t need Python for everything. Sometimes, Excel is all it takes to clean messy data like a pro. That’s what I tell my students— who rush into advanced tools before mastering the basics. 📌 Before dashboards. 📌 Before analysis. 📌 Before AI. You need one thing: 👉 Clean. Usable. Data. And Excel already gives you the power— if you know where to look. Here’s what I teach in my beginner data cleaning sessions: ✅ Remove Duplicates ✅ Trim extra spaces ✅ Standardize text case ✅ Find & Replace nulls, dashes, typos ✅ Handle missing data ✅ Split names/addresses with Text-to-Columns ✅ Use Flash Fill like Excel magic ✅ Convert text to numbers ✅ Validate data entry ✅ Remove blank rows in bulk ✨ Master these steps and you’ll clean faster than many Python scripts. It’s not “just Excel.” It’s a core skill every analyst must build. Want a free cheat sheet or practice file? Join my community here → Let’s stop overcomplicating. Start cleaning smart. 💡 — A mentor who’s cleaned more sheets than bedsheets. -- 👋 I’m Jayen T. , Dedicated to helping aspiring data analysts thrive in their careers. ➕ Follow MetricMinds.in for more tips, insights, and support on your data journey!

If your SQL tables are messy, your analytics will always lie to you. Data cleaning is not optional, it is the foundation of trustworthy insights. Here’s a simple breakdown of 13 essential SQL techniques every data engineer and analyst should know: 1. Replace NULL with a Default Value Use COALESCE to safely fill missing values during queries. 2. Delete Rows with NULL Values Remove incomplete records when they can’t be repaired. 3. Convert Text to Lowercase Standardize fields like names and emails for clean comparisons. 4. Find Duplicate Rows Identify values that appear more than once using GROUP BY. 5. Delete Duplicate Rows (Keep One) Remove duplicates while preserving a single valid entry. 6. Remove Leading & Trailing Spaces Trim whitespace so joins and comparisons don’t break. 7. Split Full Name into First & Last Extract components using SUBSTRING functions (simple cases only). 8. Standardize Date Formats Convert inconsistent date strings into a unified format. 9. Eliminate Special Characters Strip symbols while keeping alphanumeric data clean. 10. Identify Outliers Spot values outside expected upper/lower thresholds. 11. Remove Outliers Delete invalid or extreme values when necessary. 12. Fix Typo or Incorrect Values Correct inconsistent categories to avoid fragmentation. 13. Standardize Phone Number Format Keep only digits for clean, uniform phone fields. Messy data leads to messy decisions. Small SQL cleanup steps like these dramatically improve model accuracy, dashboards, and business reporting.

I am a senior Data engineer at Amazon with 7+ years of experience. If I could sit down with a junior in Data, here are some good pieces of advice I would tell them that my seniors told me. Start simple. 1. Daily batch jobs? A cron scheduler is enough. You don’t need Airflow for everything. Complexity should be earned, not assumed. 2. Own your pipelines like production code. If your data is consumed across teams or feeds real-time products, treat it like software. Use DAGs, define SLAs, and log everything. 3. Tools are easy. Trade-offs are not. Snowflake and BigQuery are great for ad-hoc analysis. But for high-throughput systems, you’ll need serious tuning, caching, partitioning, pruning, the works. 4. Schema changes are dangerous. They don’t just break dashboards, they can break trust. Use contracts. Validate upstream assumptions. Think like a platform owner. 5. Monitoring is not optional. If your pipeline fails once and no one notices, that’s a miss. If it fails and nobody knows why, that’s a disaster. Build observability early. 6. Spark is powerful and unforgiving. You can move terabytes of data or crash your cluster. Learn how shuffles work. Understand partitioning. Tune before you scale. 7. . APIs will fail. Retries, deduping, and idempotency aren’t optional, they’re survival tools. Treat external data like it’s unreliable by default. 8. Data quality depends on context. Reporting pipelines? Focus on cost. Real-time ML systems? Focus on accuracy and latency. Your design goals should match your business impact. No fancy certification can replace this. These are the lessons you only learn by building real systems, breaking them, fixing them, and owning the fallout. You want to stand out? Start by thinking like the person who has to clean up what you ship. — P.S.: If you like this post, you will like my upcoming livestream session with Zach Wilson even more! I’ll be talking about my journey in Data, the lessons I’ve learned, and a few stories I’ve never shared before on the crazy 24-hour livestream that Zach has organized! Date: 23 May Time: 7: AM (PST), and 7.30 PM (IST) Here’s the link. https://lnkd.in/geVUZfh9 Be sure to join in, you don’t want to miss this.

ChatGPT can be a great data cleaning tool. But most analysts let it ruin their data. They upload a messy CSV and give a bad command: "Clean this." The LLM will "clean" it by making massive, undocumented assumptions. It will silently delete outliers. It will hallucinate standardizations. It will turn messy data into wrong data. Stop asking LLMs to be your data janitor. Start directing them to be your data engineer. Instead of asking for a clean dataset, ask for an executable script in Python or R that you can audit, trust, and scale to millions of rows. Here is the 3-part framework for a perfect Data Transformation Blueprint using an LLM: Provide the Schema Never ask an LLM to write code for data it cannot see. You must provide the context. Paste the output of df.info() or str(df) [if you use Python] or `str(df)` or `glimpse(df)` and `head(df, 5)` [if you prefer R] so the LLM knows your column types. Pasting the first five rows lets it see the messy reality. Separate Logic from Engineering Don't just say "clean this." Bifurcate your instructions. Tell it WHAT to do (business rules: "standardize dates to YYYY-MM-DD") and HOW to do it (engineering standards: "use vectorized operations, not loops"). Show, Don't Tell Complex text cleaning requires complex Regex. Don't try to describe it. Use examples. Show it: "Input: 'Calif.' -> Output: 'CA'". The LLM will deduce the pattern and write the complex code for you. If your data foundation is cracked by a bad prompt, your advanced models will just generate noise. Use an LLM to clean your data the right way and free yourself up to do the more important work of analysis and interpretation. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

We often talk about "trust" in terms of the data and AI team's responsibility. But trust is a two-way street. A few weeks ago, Stephen Klein shared an incredible post about the intrinsic unreliability of foundational models, and that story bears some repeating. Citing a study from Columbia University's Tow Center that tested AI search on one simple task: given a direct excerpt from a news article, identify the headline, publisher, date, and URL. Here were some of those results: - Grok 3: 94% wrong - Gemini: 1 correct answer out of 200 - ChatGPT: 67% wrong - Perplexity: 37% wrong (best performer) Now those numbers are bad by any metric. But the problem is more complicated than that. It’s not just that the AI is wrong--we know how respond to wrong. It’s that the AI is confidently wrong. At its core, AI isn’t designed to create doubt; it’s designed to instill confidence. It’s not successful when it’s right. It’s successful when you don’t tell it it’s wrong. But at the risk of stating the obvious, confidence isn't accuracy. And in the enterprise, we need accuracy far more than we need blind confidence. That means that the onus falls on the business users to demand more--and the data and AI teams to supply the tooling and processes to deliver it. Now, we recognize this intuitively when it comes to traditional data products. If a dashboard is wrong, we won’t use it. And we’ll often continue to withhold that trust until the team that created it can validate its fitness for production usage (typically with some sort of SLA). We need that same operational rigor for agents in production. That means we need to: - Demand tracing for every response. - Create a culture of validating sources.  - Define a standard for good.  - Create a governance strategy that validates the inputs AND the outputs. If you can’t validate the health and performance of a product in production, then it’s not ready for use in production. Period. As business users, you should demand visibility into the health and performance of your data and AI products – and refuse to use them until you get it. Trust IS the first-step to adoption... but the thing you’re trusting needs to actually be trustworthy in the first place. Don’t wait for the consequences. Ask for the receipts. As Mark Twain would say: "It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so."