Importance of Data Quality for AI Insights

According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.

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

𝗪𝗵𝘆 𝟵𝟬% 𝗼𝗳 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗙𝗮𝗶𝗹—𝗮𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗔𝘃𝗼𝗶𝗱 𝗝𝗼𝗶𝗻𝗶𝗻𝗴 𝗧𝗵𝗲𝗺 AI is only as good as the data it’s fed. Yet, many organizations underestimate the critical role data quality plays in the success of AI initiatives. Without clean, accurate, and relevant data, even the most advanced AI models will fail to deliver meaningful results. Let’s dive into why data quality is the unsung hero of AI success. 🚀 The Data Dilemma: Why Quality Matters The surge of AI adoption has brought data into sharper focus. But here’s the catch: not all data is created equal. **📊 The harsh reality ** 80% of an AI project’s time is spent on data cleaning and preparation (Forbes). Poor data quality costs businesses an estimated $3.1 trillion annually in the U.S. alone (IBM). AI models trained on faulty or biased data are prone to errors, leading to misinformed decisions and reduced trust in AI systems. Bad data doesn’t just hinder AI—it actively works against it. Building Strong Foundations: The Value of Clean Data AI thrives on structured, high-quality data. Ensuring your data is pristine isn’t just a step in the process; it’s the foundation of success. Here are three pillars of data quality that make all the difference: 1️⃣ Accuracy: Data must reflect the real-world scenario it's supposed to model. Even minor errors can lead to significant AI missteps. 2️⃣ Completeness: Missing data creates gaps in AI training, leading to incomplete or unreliable outputs. 3️⃣ Relevance: Not all data is valuable. Feeding irrelevant data into AI models dilutes their effectiveness. 📌 Why Data Quality Equals AI Success AI models, no matter how advanced, can’t outperform the data they are trained on. Here’s why prioritizing data quality is non-negotiable: 🔑 Key Benefits of High-Quality Data: Improved Accuracy: Reliable predictions and insights from well-trained models. Reduced Bias: Clean data minimizes unintentional algorithmic bias. Efficiency: Less time spent cleaning data means faster deployment of AI solutions. Looking Ahead: A Data-Driven Future As AI becomes integral to businesses, the value of data quality will only grow. Organizations that prioritize clean, structured, and relevant data will reap the benefits of AI-driven innovation. 💡 What’s Next? Adoption of automated data cleaning tools to streamline the preparation process. I ntegration of robust data governance policies to maintain quality over time. Increased focus on real-time data validation to support dynamic AI applications. The saying “garbage in, garbage out” has never been more relevant. It’s time to treat data quality as a strategic priority, ensuring your AI efforts are built on a foundation that drives true innovation. ♻️ Share 👍 React 💭 Comment

AI is only as good as the data you train it on. But what happens when that data is flawed? 🤔 Think about it: ❌ A food delivery app sends orders to the wrong address because the system was trained on messy location data. 📍 ❌ A bank denies loans because AI was trained on biased financial history 📉 ❌ A chatbot gives wrong answers because it was trained on outdated information. 🤖🔄 These aren’t AI failures. They’re data failures. The problem is: 👉 If you train AI on biased data, you get biased decisions. 👉 If your data is messy, AI will fail, not because it's bad, but because it was set up to fail. 👉 If you feed AI garbage, it will give you garbage. So instead of fearing AI, we should fear poor data management. 💡 Fix the data, and AI will work for you How can organizations avoid feeding AI bad data? ✔ Regularly audit and clean data. ✔ Use diverse, high-quality data sources. ✔ Train AI with transparency and fairness in mind. What do you think? Are we blaming AI when the real issue is how we handle data? Share your thoughts in the comments! #AI #DataGovernance #AIEthics #MachineLearning -------------------------------------------------------------- 👋 Chris Hockey | Manager at Alvarez & Marsal 📌 Expert in Information and AI Governance, Risk, and Compliance 🔍 Reducing compliance and data breach risks by managing data volume and relevance 🔍 Aligning AI initiatives with the evolving AI regulatory landscape ✨ Insights on: • AI Governance • Information Governance • Data Risk • Information Management • Privacy Regulations & Compliance 🔔 Follow for strategic insights on advancing information and AI governance 🤝 Connect to explore tailored solutions that drive resilience and impact -------------------------------------------------------------- Opinions are my own and not the views of my employer.

How to Build AI That Actually Delivers Results (Bad data = bad AI. It’s that simple.) AI isn’t a guessing game — it learns from patterns in your data. If that data is messy, outdated, or biased, your AI will be too. The difference between AI that works and AI that fails? A rock-solid data strategy. Here’s how to get it right: ↳ Collect high-quality data: AI is only as good as the information it’s trained on. ↳ Clean and organize it: Errors, duplicates, and inconsistencies lead to faulty predictions. ↳ Diversify your datasets: Avoid bias by including different perspectives and sources. ↳ Keep it fresh: AI needs real-time, relevant data to stay accurate. ↳ Secure it: Protect sensitive data and comply with privacy regulations. Most AI failures aren’t tech failures — they’re data failures. Fix your data, and your AI will follow. Is your business making data quality a priority? ______________________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI