Data Governance: The Backbone of Data-Driven Decisions

🚀 New Blog Alert on Medium 🚀 “Data Governance Simplified: A Beginner’s Guide for 2025” In today’s data-driven world, organizations struggle with data silos, poor quality, and compliance challenges. Without a strong data governance framework, scaling AI and analytics becomes nearly impossible. At Segmetriq Analytics LLP, we break down the essentials of Data Governance in simple terms: ✅ Metadata Management ✅ Data Quality Rules ✅ Data Lineage ✅ Business Glossary ✅ AI & Agentic AI Integration 👉 Read the full beginner-friendly guide here: 🔗 - https://lnkd.in/dRYq4quB 📊 Because in 2025, businesses that govern data well will lead the digital race. #DataGovernance #DataStrategy #AIinBusiness #AgenticAI #DigitalTransformation #DataQuality #MetadataManagement #SegmetriqAnalytics

🔐 Day 19 – Why Data Governance Matters More Than Ever Everyone wants advanced analytics, AI models, and real-time dashboards. But without data governance, those initiatives often collapse under their own weight. I’ve seen teams spend 60% of their time reconciling conflicting data definitions, or dashboards that don’t even align with financial reports. That’s what poor governance looks like — data becomes a blocker, not an enabler. Common pitfalls without governance: ❌ Conflicting definitions ❌ Data errors slipping into reports and dashboards ❌ Lack of accountability for data quality ❌ Slow decision-making due to mistrust in data Data governance is not about bureaucracy — it’s about balance. Benefits of strong governance: ✅ Clarity & Ownership → Every dataset has an accountable owner. ✅ Consistency → Standard definitions prevent multiple “versions of the truth.” ✅ Compliance & Security → Protects sensitive data and avoids regulatory risk. ✅ Quality & Accessibility → Ensures data is accurate, timely, and available. ✅ Enablement → Speeds up delivery by reducing rework and confusion. An iterative approach to solving data governance: Start small, iterate, and grow trust over time. #DataGovernance #DataStrategy #DataManagement #Analytics #DataQuality #30DayChallenge #AI

🚀 Embedding Data Governance into the Heart of Data Delivery One of the most powerful shifts we’ve made in our journey to build a modern data platform is baking in data governance directly into the delivery process—not as an afterthought, but as a foundational principle. This isn’t just about compliance or control. It’s about setting the standard for what good looks like in enterprise data practices. By engaging the business to own their own data, we’ve created a virtuous circle where data stewards feel empowered to identify opportunities for improvement, drive incremental change, and elevate data quality every single day. The result? A platform that doesn’t just deliver data—it delivers trustworthy, business-ready data that fuels the use cases that matter most, including AI. This is how we make data better, together. One improvement at a time. Every day. 💡 Shout out to our Data Governance Lead Nicole Cherian and our first Data Stewards Karthik Adla and Mylissa Garrick who have pioneered the way. #DataGovernance #ModernDataPlatform #AI #DataStewardship #BusinessValue #DataLeadership #IncrementalImprovement

𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗱𝗲𝗮𝗱 - 𝗶𝘁 𝗷𝘂𝘀𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗸𝗻𝗼𝘄 𝗶𝘁 𝘆𝗲𝘁 Remember when data lakes were supposed to solve everything? Store all your data in one magical repository and analytics would flow like water downstream. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: Your data lake became a data swamp. Studies show up to 80% of data lake initiatives fail to deliver expected business value. Poor governance turned your strategic investment into an expensive graveyard of unusable datasets. The fundamental problem isn't technology - it's centralization. When one team controls all data access, you create bottlenecks. When domain experts can't manage their own data products, quality suffers. When AI projects need clean, discoverable data, they find neither. 𝗧𝗵𝗲 𝗽𝗮𝘁𝗵 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝗶𝘀 𝗱𝗮𝘁𝗮 𝗺𝗲𝘀𝗵. Instead of centralizing everything, data mesh distributes ownership to domain teams. Each business unit becomes responsible for their own high-quality "data products" while maintaining universal standards for discoverability and compliance. Think of it this way: your data lake tried to be a central warehouse. Data mesh creates a network of specialized shops, each with expert owners who understand their customers' needs. 𝗙𝗼𝗿 𝗔𝗜 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴: • Domain teams ensure data quality because they own the outcomes • Self-service infrastructure accelerates innovation cycles • Built-in governance prevents compliance nightmares • Data becomes discoverable and trusted across the organization Before you embark on your next AI project, ask this critical question: 𝗜𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗲𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗼𝗿 𝗯𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀? Most enterprises discover their biggest AI blocker isn't algorithms or compute power - it's data that doesn't work when you need it to. The companies winning with AI aren't just buying better tools. They're fixing their foundations first. #datamesh, #datamaturity, #aireadiness Parallaxis

Data Governance: Busting the Bureaucracy Myth Ever felt that "Data Governance" sounds like a fancy term for endless meetings and paperwork, slowing everything down? You're not alone. This perception often makes organizations hesitant to fully invest, leading to missed opportunities and increased risk. The clear challenge: Many still see Data Governance as a rigid, resource-intensive overhead, disconnected from tangible business value. This view often results in underinvestment or half-hearted implementation, leaving organizations vulnerable to data quality issues, compliance breaches, and stalled AI initiatives. It's perceived as a barrier, not an enabler. Let's bust that myth: Effective Data Governance isn't about bureaucracy; it's about enabling agility, building trust, and fueling innovation. • It's a strategic framework that clearly defines roles, responsibilities, and processes for your critical data assets. • It ensures data accuracy, consistency, and compliance, making your data trustworthy for analytics and advanced AI models. • Leveraging AI-powered tools for automated data lineage, metadata management, and policy enforcement can transform governance from a manual burden into an efficient, value-driving engine. This means proactive risk mitigation, enhanced decision-making, and faster time-to-insight. What's one common misconception about Data Governance you've encountered that you'd like to see debunked? Share your thoughts below! #DataGovernance #DataManagement #AI #DigitalTransformation #DataStrategy #BusinessValue Sunil Zarikar

Data isn’t complicated. People make it complicated. Somewhere between the 200th mandatory field on a form, a zillion outdated reports, and the 17th dashboard no one opens, businesses decided that more data = better business. When everything is important, nothing is important. It doesn’t matter whether the data is filled in by a sales person, a recruiter, customer, an AI agent, or some ghost… if the business hasn’t defined what gets collected, when, why, and what “good” looks like, it’s all noise. Data is only as valuable as the process it serves What does your team actually need to move forward in their workflow? What does your customer actually need to make a decision? What does the business actually need to stay compliant, profitable, or both? Start there. Strip it back. Because if your foundation is junk, the prettiest ontology or AI layer won’t save you. And if you truly want to be AI-first? You start with data architecture. Data streams, data models, and governance aren’t optional, they’re the operating system for your AI and agents. Without trusted sources, they won’t run. AI won’t fix your messy data. Agents won’t fix your messy data. Only clarity will. So before you add another field, another report, another “must-have metric”… ask yourself: Does this move the work forward? Or am I just hoarding data for the illusion of control?

Thoughts from our panel discussion on how to ensure high quality and trusted data for AI, at Data Management Summit New York: -Moving from reactive /rules based to pro-active -Apply AI across the DQ process - not necessarily replacing existing rues but augmenting - AI helps with proactive Managing Unstructured Data: -Growth & opportunity but the risks is higher -Historical tools are focused on structured data -Unstructured Quality KPIs - lineage is key -Ensure vendors meet formatting standards -we're still early in the Unstructured data quality journey -Raw document to JSON - enables applying traditional DQ rules Automating unstructured remediation at scale? -Agentic AI can flag - automate the fix - then test that the fix is valid - what's missing? First step - Structure the data - by transforming into JSON - then apply analytics -e.g. Take 50 records for manual assessment - apply AI and compare how it performed - Human in the Loop is always necessary Key org challenge for establishing the Ground truth: -Used to be 100% correct before data hit production - we're now probability based meaning 90% of the data is correct going into prod - creating greater oversight challenges so demands Probabilistic tools are great for probabilistic answers - not for 100% accuracy Using AI to streamline data management: -dealing with scale -identifying missing / incomplete data -detecting issues earlier in the pipeline A governance framework can fail if everyone interprets and executes differently - essentially creating multiple frameworks If governance slows you down, it's a cost center, if it's keeping you safe, it's an asset - Does a speed limit slow you down or keep you safe? Where AI should NOT be used: -Where bias in data -Don't use AI where you need a believable answer -Critical decision making Think of it as an excellent assistant Implementing new DQ monitoring and observability tools tops Priority actions Key takeaways: -Culture -Trust framework -Governance model - we have new tools but the good data management skillset is probably more important than ever - we can do it at scale and faster #DMSNYC #datamanagement #data #AI #GenAI #LLMs #dataquality #governance #unstructureddata BNY SMBC Group Datactics Informatica Context Analytics, Inc. (CA) Brian Greenberg Manal Alimari Eugene Coakley, OLY Amy Horowitz Joe Gits, CFA

In the realm of data management, the significance of quality data cannot be overstated. As W. Edwards Deming aptly put it, "Without data, you’re just another person with an opinion." Organisations often strive to enhance data quality, employing various approaches: 1. **Reactive cleansing:** This method involves fixing data issues after they occur. While quick, it can be costly and lacks long-term sustainability. 2. **Process re-engineering:** By enhancing controls in source systems, this approach adds value but may be slow to implement across a complex infrastructure. 3. **Enterprise data domain models:** Aligned with DMBOK principles, this method establishes clear data domains (such as Customer, Payments, Finance, Compliance), standardizes taxonomy, assigns ownership, and integrates governance to prevent issues at the root. This systematic approach fosters trust over temporary solutions. The game-changer today is the integration of AI, which expedites the data enhancement journey: - Automated profiling identifies duplicates, anomalies, and missing values on a large scale. - AI-driven ETL accelerates and refines data transformations. - Natural-language glossaries and taxonomies are created from metadata. - Lineage mapping evolves through learning and adaptation. The key takeaway: AI complements governance by enhancing its effectiveness. By merging domain-focused models with AI's agility, organizations can shift from reactive measures to establishing enduring data infrastructures. Ultimately, as Deming emphasized, quality data is pivotal for informed decision-making, steering clear of mere opinions that fall short in shaping strategic initiatives. What's worked well for you? #DataGovernance #DataQuality #DMBOK #AI #DataManagement #ETL #Transformation

The role of the Chief Data Officer has never been more critical — or more complex. For years, CDOs were measured on governance fundamentals: catalog coverage, data quality, and compliance reporting. These were the foundations of trust. But the rise of GenAI has shifted the terrain. Practically overnight, every employee has become a data consumer. The scale of demand is unlike anything we’ve seen before. And this is where old models break down. Ticket-based approvals that take weeks? They don’t work when thousands of employees — and AI agents — are requesting access at scale. Governance teams drown, business slows, and innovation stalls. That’s why data provisioning is emerging as the CDO’s new mandate. Provisioning is where governance meets business velocity. It’s about enabling the right people to get the right data, securely and quickly, without friction. And it’s measurable. 𝐍𝐞𝐰 𝐊𝐏𝐈𝐬 𝐚𝐫𝐞 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐭𝐨 𝐝𝐞𝐟𝐢𝐧𝐞 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐂𝐃𝐎 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: ⏱️ Average time from request to access (weeks → hours) 📈 Percentage of requests handled automatically through policy or AI 🔒 Share of sensitive requests requiring exception handling 👥 Proportion of the workforce actively provisioned with governed data These aren’t just operational stats — they’re indicators of how effectively an enterprise is using data to make decisions (I am spending time writing a data provisioning guide for Data Governance Book of Knowledge - coming soon! -- and I plan to get it integrated into conversational AI). At Immuta, we’ve reimagined how to make this possible. By meeting data consumers in the tools they already use, routing approvals intelligently, and leveraging AI to cut through bottlenecks, we help CDOs transform provisioning from a pain point into a competitive advantage. Because here’s the truth: the future of data doesn’t belong to those who store it, secure it, or even catalog it. 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐛𝐞𝐥𝐨𝐧𝐠𝐬 𝐭𝐨 𝐭𝐡𝐨𝐬𝐞 𝐰𝐡𝐨 𝐜𝐚𝐧 𝐩𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧 𝐢𝐭. And it’s the CDO who sets that future in motion. Read more here --> https://lnkd.in/eSy2C6d9

I post about data governance a lot. I talk about it with customers. I bring it up in meetings. And the other day I thought: maybe not everyone actually knows what “data governance” means. Here’s how I think about it. Data governance isn’t some abstract IT thing. It’s the rules of the road for your data. Making sure it’s 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲, 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁, 𝘀𝗲𝗰𝘂𝗿𝗲, 𝗮𝗻𝗱 𝘂𝘀𝗮𝗯𝗹𝗲. That translates into a few simple but powerful practices: - 𝗔𝗰𝗰𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 → who should really see what data? - 𝗦𝗵𝗮𝗿𝗲𝗱 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻𝘀 → “revenue” means the same thing in finance, marketing, and sales. - 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 → knowing where the data came from and how it’s been changed. - 𝗔𝘂𝗱𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → being able to trace back who used what and when. Without governance, you get chaos: dashboards that don’t match, models that don’t perform, risks that slip through the cracks. With governance, you get trust: one version of the truth, faster decisions, and AI that’s reliable instead of risky. It’s not glamorous, but it’s the foundation everything else rests on.