How to Ensure Data Quality in Products

I’ve lost count of projects that shipped gorgeous features but relied on messy data assets. The cost always surfaces later when inevitable firefights, expensive backfills, and credibility hits to the data team occur. This is a major reason why I argue we need to incentivize SWEs to treat data as a first-class citizen before they merge code. Here are five ways you can help SWEs make this happen: 1. Treat data as code, not exhaust Data is produced by code (regardless of whether you are the 1st party producer or ingesting from a 3rd party). Many software engineers have minimal visibility into how their logs are used (even the business-critical ones), so you need to make it easy for them to understand their impact. 2. Automate validation at commit time Data contracts enable checks during the CI/CD process when a data asset changes. A failing test should block the merge just like any unit test. Developers receive instant feedback instead of hearing their data team complain about the hundredth data issue with minimal context. 3. Challenge the "move fast and break things" mantra Traditional approaches often postpone quality and governance until after deployment, as shipping fast feels safer than debating data schemas at the outset. Instead, early negotiation shrinks rework, speeds onboarding, and keeps your pipeline clean when the feature's scope changes six months in. Having a data perspective when creating product requirement documents can be a huge unlock! 4. Embed quality checks into your pipeline Track DQ metrics such as null ratios, referential breaks, and out-of-range values on trend dashboards. Observability tools are great for this, but even a set of SQL queries that are triggered can provide value. 5. Don't boil the ocean; Focus on protecting tier 1 data assets first Your most critical but volatile data asset is your top candidate to try these approaches. Ideally, there should be meaningful change as your product or service evolves, but that change can lead to chaos. Making a case for mitigating risk for critical components is an effective way to make SWEs want to pay attention. If you want to fix a broken system, you start at the source of the problem and work your way forward. Not doing this is why so many data teams I talk to feel stuck. What’s one step your team can take to move data quality closer to SWEs? #data #swe #ai

One of the most powerful uses of AI is transforming unstructured data into structured formats. Structured data is often used for analytics and machine learning—but here’s the critical question: Can we trust the output? 👉 Structured ≠ Clean. Take this example: We can use AI to transform retail product reviews into structured fields like Product Quality, Delivery Experience, and Customer Sentiment, etc. This structured data is then fed into a machine learning model that helps merchants decide whether to continue working with a vendor based on return rates, sentiment trends, and product accuracy. Sounds powerful—but only if we apply Data Quality (DQ) checks before using that data in the model. Here’s what DQ management should include at least the following: 📌 Missing Value Checks – Are all critical fields populated? 📌 Valid Value Range: Ratings should be within 1–5, or sentiment should be one of {Positive, Negative, Mixed}. 📌 Consistent Categories – Are labels like “On Time” vs “on_time” standardized? 📌 Cross-field Logic – Does a “Negative” sentiment align with a “Excellent product quality” value? 📌 Outlier Detection – Are there reviews that contradict the overall trend? For example, a review with all negative fields but field "Recommend Vendor” has “Yes". 📌 Duplicate Records – Same review text or ID appearing more than once. AI can accelerate many processes—but DQ management processes is what make that data trustworthy.

📊 How do you ensure the quality and governance of your data? In the world of data engineering, maintaining proper data governance and quality is critical for reliable insights. Let’s explore how Google Cloud Platform (GCP) can help. 🌐 Data Governance and Quality in GCP Managing data governance and ensuring data quality are essential for making informed decisions and maintaining regulatory compliance. Here are some best practices for managing data governance and quality on GCP: Key Strategies for Data Governance: 1. Centralized Data Management: - Data Catalog: Use Google Cloud’s Data Catalog to organize and manage metadata across your GCP projects. This tool helps you discover, classify, and document your datasets for better governance. 2. Data Security and Compliance: - Encryption: Implement end-to-end encryption (both in transit and at rest) for all sensitive data. GCP provides encryption by default and allows you to manage your own encryption keys. 3. Data Auditing and Monitoring: - Audit Logs: Enable Cloud Audit Logs to track access and changes to your datasets, helping you maintain an audit trail for compliance purposes. - Data Retention Policies: Implement policies to automatically archive or delete outdated data to ensure compliance with data retention regulations. Key Strategies for Data Quality: 1. Data Validation: Automated Checks: Use tools like Cloud Data Fusion to integrate automated data validation checks at every stage of your data pipelines, ensuring data integrity from source to destination. Monitoring Data Quality: Set up alerts in Stackdriver Monitoring to notify you if data quality metrics (like completeness, accuracy, and consistency) fall below defined thresholds. 2. Data Cleaning: Cloud Dataprep: Use Cloud Dataprep for data cleaning and transformation before loading it into data warehouses like BigQuery. Ensure data is standardized and ready for analysis. Error Handling: Build error-handling mechanisms into your pipelines to flag and correct data issues automatically. 3. Data Consistency Across Pipelines: Schema Management: Implement schema enforcement across your data pipelines to maintain consistency. Use BigQuery’s schema enforcement capabilities to ensure your data adheres to predefined formats. Benefits of Data Governance and Quality: - Informed Decision-Making: High-quality, well-governed data leads to more accurate insights and better business outcomes. - Compliance: Stay compliant with regulations like GDPR, HIPAA, and SOC 2 by implementing proper governance controls. - Reduced Risk: Proper governance reduces the risk of data breaches, inaccuracies, and inconsistencies. 📢 Stay Connected: Follow my LinkedIn profile for more tips on data engineering and GCP insights: https://zurl.co/lEpN #DataGovernance #DataQuality #GCP #DataEngineering #CloudComputing

Data quality is one of the most essential investments you can make when developing your data infrastructure. If you're data is "real-time" but it's wrong, guess what, you're gonna have a bad time. So how do you implement data quality into your pipelines? On a basic level you'll likely want to integrate some form of checks that could be anything from: - Anomaly and Range checks - These checks ensure that the data received fits an expected range or distribution. So let's say you only ever expect transactions of $5-$100 and you get a $999 transaction. That should set off alarms. In fact I have several cases where the business added new products or someone made a large business purchase that exceeded expectations that were flagged because of these checks - Data type checks - As the name suggests, this ensures that a date field is a date. This is important because if you're pulling files from a 3rd party they might send you headerless files that you have to trust they will keep sending you the same data in the same order. - Row count checks - A lot of businesses have a pretty steady rate of rows when it comes to fact tables. The number of transactions follow some sort of pattern, many are lower on the weekends and perhaps steadily growing over time. Row checks help ensure you don't see 2x the amount of rows because of a bad process or join. - Freshness checks - If you've worked in data long enough you'e likely had an executive bring up that your data was wrong. And it's less that the data was wrong, and more that the data was late(which is kind of wrong). Thus freshness checks make sure you know the data is late first so you can fix it or at least update those that need to know. - Category checks - The first category check I implemented was to ensure that every state abbreviation was valid. I assumed this would be true because they must use a drop down right? Well there were bad state abbreviations entered nonetheless As well as a few others. The next question would become how would you implement these checks and the solutions there range from setting up automated tasks that run during or after a table lands to dashboards to finally using far more developed tools that provide observability into far more than just a few data checks. If you're looking to dig deeper into the topic of data quality and how to implement it I have both a video and an article on the topic. 1. Video - How And Why Data Engineers Need To Care About Data Quality Now - And How To Implement It https://lnkd.in/gjMThSxY 2. Article - How And Why We Need To Implement Data Quality Now! https://lnkd.in/grWmDmkJ #dataengineering #datanalytics