Ensuring Data Quality

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

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

Stop tracking email open rates. Open rates are the least reliable metric in your funnel. Honestly, they are wildly inaccurate. This is because an email "open" is only considered when a tracking image pixel loads in an email. Let’s break down the 7 ways your open rate is probably wrong. Apple Mail Privacy Protection (MPP): This feature pre-loads images and content in emails, potentially registering an "open" even if the recipient hasn't actually opened the email. Image blocking is enabled by default in many email clients, which prevents your tracking pixel from loading. So even if someone reads your email top to bottom, it may never count as an open. Email Client Settings: Some email clients automatically display images in the preview pane, which can register an "open" even if the recipient hasn't fully opened the email. Tracking Pixel Issues: Tracking pixels, which are tiny images used to track opens, can sometimes fail to load due to various technical issues, including server-side problems or client-side limitations. Spam Filters: Advanced spam filters and sorting algorithms might check the email though bots, potentially skewing open rates Forwarded emails may register multiple opens from different devices or IPs, even if only one person is reading it Inflated Open Rates: Certain programs that automatically open and view emails for spam detection can also contribute to inflated open rates. And yet, marketers obsess over it. Whole campaigns are “optimized” for a number that doesn't reflect real intent, real engagement, or real outcomes. We went one step ahead of open rates at Mailmodo. We bring web-like actionable interactions within emails. Instead of measuring who opened, we focus on what they did. Inside the email. That means: A one-click poll → submitted A meeting → booked right inside the email A feedback form → feedback provided An event registration form → user registered for the event If you want to build email journeys that measure action instead of illusions, let’s talk. I can share what we’ve seen work and why open rate should never be your success metric.

"Nearly half the data used for ad targeting is wrong." Have you heard of the Truthset study? 1. Truthset reviewed data providers with 790 million unique hashed email addresses and 133 million postal addresses and compared them with verified sources. 2. On average, the match was only correct 50% of the time. 3. Old data was one major driver of inaccuracy, but other factors included people moving or having multiple email addresses. 4. This match is basic, but it underpins targeting details like gender, age, income, and more. So, if it's wrong, it impacts ad personalization. 5. The study found the least validated data delivered an ROI of just $0.67 per dollar spent. The takeaway for marketers: Every additional targeting layer brings up your cost. And if you layer in bad data, your likelihood of success is close to zero.

Do you think Data Governance: All Show, No Impact? → Polished policies ✓ → Fancy dashboards ✓ → Impressive jargon ✓ But here's the reality check: Most data governance initiatives look great in boardroom presentations yet fail to move the needle where it matters. The numbers don't lie. Poor data quality bleeds organizations dry—$12.9 million annually according to Gartner. Yet those who get governance right see 30% higher ROI by 2026. What's the difference? ❌It's not about the theater of governance. ✅It's about data engineers who embed governance principles directly into solution architectures, making data quality and compliance invisible infrastructure rather than visible overhead. Here’s a 6-step roadmap to build a resilient, secure, and transparent data foundation: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗥𝗼𝗹𝗲𝘀 & 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Define clear ownership, stewardship, and documentation standards. This sets the tone for accountability and consistency across teams. 2️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Implement role-based access, encryption, and audit trails. Stay compliant with GDPR/CCPA and protect sensitive data from misuse. 3️⃣ 𝗗𝗮𝘁𝗮 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 & 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Catalog all data assets. Tag them by sensitivity, usage, and business domain. Visibility is the first step to control. 4️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Set up automated checks for freshness, completeness, and accuracy. Use tools like dbt tests, Great Expectations, and Monte Carlo to catch issues early. 5️⃣ 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Track data flow from source to dashboard. When something breaks, know what’s affected and who needs to be informed. 6️⃣ 𝗦𝗟𝗔 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Define SLAs for critical pipelines. Build dashboards that report uptime, latency, and failure rates—because business cares about reliability, not tech jargon. With the rising AI innovations, it's important to emphasise the governance aspects data engineers need to implement for robust data management. Do not underestimate the power of Data Quality and Validation by adapting: ↳ Automated data quality checks ↳ Schema validation frameworks ↳ Data lineage tracking ↳ Data quality SLAs ↳ Monitoring & alerting setup While it's equally important to consider the following Data Security & Privacy aspects: ↳ Threat Modeling ↳ Encryption Strategies ↳ Access Control ↳ Privacy by Design ↳ Compliance Expertise Some incredible folks to follow in this area - Chad Sanderson George Firican 🎯 Mark Freeman II Piotr Czarnas Dylan Anderson Who else would you like to add? ▶️ Stay tuned with me (Pooja) for more on Data Engineering. ♻️ Reshare if this resonates with you!

Is “good enough” data really good enough? For 88% of MOps pros, the answer is a resounding no. Why? Because data hygiene is more than just a technical checkbox. It’s a trust issue. When your data is stale or inconsistent, it doesn’t just hurt campaigns; it erodes confidence across the org. Sales stops trusting leads. Marketing stops trusting segmentation. Leadership stops trusting analytics. And once trust is gone, so is the ability to make bold, data-driven decisions. Research tells that data quality is the #1 challenge holding teams back from prioritizing the initiatives that actually move the needle. Think of it like a junk drawer: If you can’t find what you need (or worse, if what you find is wrong), you don’t just waste time, you stop looking altogether. So what do high-performing teams do differently? → They schedule routine maintenance. → They establish ownership - someone is accountable for data processes. → They invest in validation tools - automation reduces the manual grind. → They set governance policies - because clean data only stays clean if everyone protects it. Build a culture where everyone values accuracy, not just the Ops team. Because clean data leads to clearer decisions and a business that can finally operate with confidence.

Too many teams accept data chaos as normal. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North take a different path - eliminating silos, reducing wasted effort, and unlocking real business value. Here’s the playbook they’ve used to break down silos and build a scalable data strategy: 1️⃣ Empower domain teams - but with a strong foundation. A central data group ensures governance while teams take ownership of their data. 2️⃣ Create a clear governance structure. When ownership, documentation, and accountability are defined, teams stop duplicating work. 3️⃣ Standardize data practices. Naming conventions, documentation, and validation eliminate confusion and prevent teams from second-guessing reports. 4️⃣ Build a unified discovery layer. A single “Google for your data” ensures teams can find, understand, and use the right datasets instantly. 5️⃣ Automate governance. Policies aren’t just guidelines - they’re enforced in real-time, reducing manual effort and ensuring compliance at scale. 6️⃣ Integrate tools and workflows. When governance, discovery, and collaboration work together, data flows instead of getting stuck in silos. We’ve seen this shift transform how teams work with data - eliminating friction, increasing trust, and making data truly operational. So if your team still spends more time searching for data than analyzing it, what’s stopping you from changing that?

In today’s interconnected world, data sovereignty is more than a technical concept—it’s a fundamental right. Data created on our journey to better should be my data, in my control, secured for my use, and shared according to my permission. The recent recognition of tribal data sovereignty in Colorado sets a powerful precedent, showing how communities can reclaim control over their information to drive equity, empowerment, and trust (see article link in comments). At The Human Flourishing Collaborative (HFC), we believe data sovereignty is essential for both nations and individuals. As a founding member of HFC, Fluid Medical leads this charge through innovative platforms like Wave and Wind, ensuring this vision becomes a reality. Why Data Sovereignty Matters 🔹 My Data, My Control: Individuals and communities must have authority over how their data is stored, accessed, and shared. 🔹 Equity and Empowerment: Tribal nations and underserved groups gain access to resources while preserving cultural identity. 🔹 Security: Protecting sensitive health and cultural data ensures trust and resilience in the face of global challenges. As a critical member of HFC, Fluid Medical’s platforms are transforming data sovereignty into actionable solutions: 🔹 Wave empowers individuals to manage their health data securely, enabling informed Care of Health wherever they are. Your data, with you and for you, at all times. 🔹 Wind gives Indigenous communities full control over their cultural knowledge—preserving histories while fostering growth. Your culture, with you and for you, for this generation and for future generations. The time to act is now and we are acting. Governments, healthcare leaders, and organizations must prioritize systems that uphold sovereignty for all. At HFC, we envision a future where every individual and community flourishes through secure access to their data. Let’s make “My Data” truly mean My Control. We can build a world where innovation respects identity, and trust drives progress. Today, your data is an asset that fuels the profits of other organizations. In the future, your data will fuel your understanding, your unique culture, your economic gain, and your journey to flourishing. Count it. Beyond Better. Extraordinarily Different. Simplifying Health. #Flourishing #Innovation #Vision #Change _______________ I build ecosystems for human flourishing and am a global advisor to founders, boards, organizations, and institutions pursuing human flourishing. I am also a writer (Toward Human Flourishing/2025) and speaker (various). Reach out to explore engagement or collaborative opportunities.

#blockchain | #digitalidentity | #crossborder | #trade : "Unlocking Trade Data Flows with Digital Trust Using Interoperable Identity Technology" The paper reviews the current challenges in unlocking cross-border data flows, and how interoperability of digital identity regimes using high level types of decentralized technologies can overcome this with active public-private partnerships. Decentralized identity technologies, such as verifiable credentials (VCs) and decentralized identifiers (DIDs), coupled with interoperability protocols can complement the current Web3 infrastructure to enhance interoperability and digital trust . It is noted in the World Economic Forum White Paper that global trust worthiness is an important identity system principle for future supply chains, as this process of dynamically verifying counterparts through digital identity management and verification is a critical step in establishing trust and assurance for organizations participating in digital supply-chain transactions. As the number of digital services, transactions and entities grow, it is crucial to ensure that digitally traded goods and services take place in a secure and trusted network in which each entity can be dynamically verified and authenticated. Web3 describes the next generation of the internet that leverages blockchain to “decentralize” storage, compute and governance of systems and networks, typically using open source software and without a trusted intermediary. With the new iteration of Web3 being the next evolution of digitalized paradigms, several new decentralized identity technologies have become an increasingly important component to complement existing Web3 infrastructure for digital trade. VCs are an open standard for digital credentials, which can be used to represent individuals, organizations, products or documents that are cryptographically verifiable and tamper-evident. The important elements of the design framework of digital identities involves three parties – issuer, holder and verifier. This is commonly referred to the self sovereign identity (SSI) trust triangle. The flow starts with the issuance of decentralized credentials in a standard format. The holder presents these credentials to a service provider in a secure way. The verifier then assesses the authenticity and validity of these credentials. Finally, when the credential is no longer required, the user revokes it. This gives rise to the main applications of digital identities and VCs in business credentials, product credentials and document identifiers in the trade environment involving businesses, goods and services. EmpowerEdge Ventures

You want to deliver actionable insights? It all begins with thorough data validation. Follow these steps to avoid "garbage in, garbage out": 1. 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲:    Understand how your data was gathered to assess its reliability. Ask yourself if you truly know where your data comes from.     2. 𝗖𝗵𝗲𝗰𝗸 𝗳𝗼𝗿 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆:    Verify that data formats, labels, and measurement units are aligned. Look for inconsistencies, such as varying date formats.     3. 𝗘𝗻𝘀𝘂𝗿𝗲 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀𝘀 & 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲:    Confirm that your data is up-to-date and fits your analytical goals.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗮𝗻𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗚𝗮𝗽𝘀:    Look for missing values that could skew your findings. Investigate why gaps exist and fix them through additional data collection or statistical methods.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗮 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲:    Cross-check your data against business logic. Ensure that figures make sense in context, for example, by avoiding impossible values such as negative stock levels. Clarify any discrepancies with stakeholders. Your aim is to generate insights that can be trusted. What are your steps to ensure data quality? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you know the importance of data validation. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #datacleaning #datavalidation #careergrowth