Why Good Enough Data Is Important

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

What’s my favorite used case for AI (that many think is the easiest, but happens to be the trickiest and messiest)? Rapid DATA ANALYSIS! The discrepancies created can be WILD! The perfect use case for artificial intelligence is to do complex analysis on varied data sets, from varied data sources, from varied data functions. Here’s what typically happens “Our AI says one thing. Our finance dashboards say another.” “We spent $2M on AI tools to power digital marketing but our conversion rates went down.” Here’s the uncomfortable truth: AI doesn’t magically reconcile the mess you already had. It accelerates it. So if you think you can abdicate a proper data governance program, or skip building proper semantic layers in any form, I think again. Because AI isn’t failing you. Your data is. And with so many starts/stops with data programs, not enough funding or resources to do all that’s needed with data archtecture and ownership, and high turnover of CDOs because of “perceived value” - the data problem is never really being addressed. To make matters trickier, Harvard Business Review said only 27% of companies maintain consistently high data quality across their org. That means 73% are feeding their AI models fast food data and wondering why they get junk results. To combat this??? Get ahead by doing this: ✔ centralized data ownership as much as you can (no more 7 versions of “the truth”) ✔ standardized definitions as much as you can across teams (so “revenue” doesn’t mean 5 different things) ✔ evolve the foundation - don’t get lazy here, it needs to keep up with the shiny models ✔ don’t skip the semantic layer - it’s needed for data analysis at scale Embedding AI into your enterprise is possible, but the investment in AI can’t trump the investment in data, IF you want enterprise-grade AI with high accuracy, dependency, and trust. >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Worlds 1st Chief AI Officer (2016) for enterprise, and Inventor of The Human Amplification Index™ with AI. 10 patents, former Amazon & C-Suite Exec (5x), best-selling author, FORBES “AI Maverick & Visionary of the 21st Century” , Top ‘100 AI Thought Leaders’, and 1st to market by launching IBM Watson back 2011. My job is not just to develop & create, but to protect and outfit our new digital identity to ensure: 1) the security of our workforce & human relevance and 2) data security in the age of AI & Automation 🤖

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.

𝗪𝗵𝘆 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗜𝘀 𝗢𝗻𝗹𝘆 𝗮𝘀 𝗚𝗼𝗼𝗱 𝗮𝘀 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸 I recently spoke with a mid-sized high tech company that had spent $250,000 on AI solutions last year. Their ROI? Almost nothing. When we dug deeper, the issue wasn't the AI technology they'd purchased. It was the foundation it was built upon. 𝗧𝗵𝗲 𝗨𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝗧𝗿𝘂𝘁𝗵 𝗳𝗼𝗿 𝗦𝗠𝗕𝘀 Many of us are rushing to implement AI while overlooking the unsexy but critical component: 𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. It's like building a sports car with a lawnmower engine. The exterior might look impressive, but the performance will always disappoint. 𝗧𝗵𝗲 𝟯 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸 After working with dozens of SMBs on their digital transformation, I've identified three non-negotiable elements: 𝟭. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 Before adding AI, ensure your existing systems talk to each other. One client discovered they had 7 different customer databases with conflicting information—no wonder their personalization efforts failed. 𝟮. 𝗖𝗹𝗲𝗮𝗻 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗞𝗶𝗻𝗴 In a recent project, we found that just cleaning contact data improved sales conversion by 23%—before implementing any AI. Start with basic data hygiene; the returns are immediate. 𝟯. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝘀 𝗚𝗿𝗼𝘄𝘁𝗵 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 The companies seeing the best AI results have clear data ownership and quality standards. This isn't just IT policy—it's business strategy that belongs in your leadership meetings. 𝗦𝘁𝗮𝗿𝘁 𝗦𝗺𝗮𝗹𝗹, 𝗦𝗰𝗮𝗹𝗲 𝗦𝗺𝗮𝗿𝘁 You don't need to overhaul everything at once. One retail client began by simply unifying their inventory and customer data systems. Six months later, their AI-powered recommendation engine was driving 17% more revenue per customer. 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Your competitors are likely making the same mistake: chasing AI capabilities while neglecting data fundamentals. The SMBs that will thrive aren't necessarily those with the biggest AI budgets, but those who build on solid data foundations. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝗻𝗲 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝘀𝘂𝗲 𝘁𝗵𝗮𝘁'𝘀 𝗵𝗼𝗹𝗱𝗶𝗻𝗴 𝗯𝗮𝗰𝗸 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄? I'd love to hear your challenges in the comments—and maybe share some solutions. #DataStrategy #SMBgrowth #AIreadiness #BusinessIntelligence #DigitalTransformation

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

In the race to adopt the latest technologies, many companies are jumping on the AI bandwagon. But here's the truth: You don't need an "AI" strategy – you need a solid data strategy. + AI can only be as good as the data it processes. Without high-quality, well-organized data, even the most advanced AI systems will fall short. Start by ensuring your data is accurate, comprehensive, and easily accessible. + Invest in the tools and processes that allow you to collect, store, and analyze data effectively. This includes data governance, data quality management, and scalable storage solutions. + Break down silos within your organization. Ensure that data from different departments and sources can be integrated and analyzed cohesively. A unified data approach will provide a more complete and actionable view of your business. + A successful data strategy requires collaboration between IT, data science, and business units. Ensure everyone understands the value of data and works together to harness its potential. + With a solid data strategy in place, you'll be in a prime position to adopt AI technologies. Your AI initiatives will be more effective and deliver better results because they're built on a strong foundation of reliable data. In conclusion, before you think about implementing AI, make sure you have a robust data strategy. It's the backbone of successful AI applications and will drive long-term value for your organization. #DataStrategy #AI #DataDriven #BusinessIntelligence #DataQuality #TechStrategy #Innovation

Whenever I present on #AIStrategy, there's one slide that consistently sparks the most questions and interest, and that's "The AI Data Quality Challenge." As technical leaders, we're all dealing with the reality that the immense power of the new AI/LLM/Agents era critically depends on the quality of the #Data flowing through it. Here is the AI Data landscape post-training, in my opinion (IMHO): 1️⃣ Enterprise Data: ➖ Task Specific Labeled Data: Used to Fine-Tune models for your specific business tasks. ➖ Knowledge Data: Your proprietary information or production data, crucial for your core AI features or for grounding AI responses in factual or specific context. ➖ Few Shots: Small sets of examples used in Prompt Engineering and In-Context Learning to guide the model. 2️⃣ User Data: ➖ User Input: The direct language users provide to the AI in the form of queries, questions, prompts, or pure data points. 3️⃣ Operational Data: ➖ Evaluation Data: Used to rigorously assess model performance and accuracy for specific tasks and roles. ➖ Generated Outputs and Logs Data: The AI's responses and system logs, vital for monitoring, feedback, and iterative improvement. (Consider the privacy and security implications of this data and establish clear protocols for its use.) For fellow Technical Leaders, here's why this is so important, in my opinion: ❇️ Better Data Quality = Better AI Outcomes. Period! ❇️ Direct Impact: The quality of your data inputs directly dictates the quality and reliability of your AI's outputs. ❇️ Streamlined Solutions: Optimizing data sources, flows, and schemas is key to boosting AI efficiency and accuracy. ❇️ Precision through Knowledge Data: This is what makes AI truly Enterprise-grade. ❇️ Logs Fuel Improvement: Don't underestimate Generated Outputs and Logs Data. They are essential for iterative refinement of AI performance. What are your thoughts? I'd love to hear your insights in the comments section below 👇 or repost to share with your network 📣 #AI #DataQuality #LLMs #ResponsibleAI #TechLeadership #EnterpriseAI #DataStrategy #AIGovernance #MachineLearning #GenAI AI Accelerator Institute AI Realized AI Makerspace