The Impact Of Data Quality On AI Model Performance

“Garbage in, garbage out” is the reason that a lot of AI-generated text reads like boring, SEO-spam marketing copy. 😴😴😴 If you’re training your organization's self-hosted AI model, it’s probably because you want better, more reliable output for specific tasks. (Or it’s because you want more confidentiality than the general use models offer. 🥸 But you’ll take advantage of the additional training capabilities, right?)  So don’t let your in-house model fall into the same problem! Cull the garbage data, only feed it the good stuff. Consider these three practices to ensure only high-quality data ends up in your organization’s LLM. 1️⃣ Establish Data Quality Standards: Define what “good” data looks like. Clear standards are a good defense against junk info. 2️⃣ Review Data Thoroughly: Your standard is meaningless if nobody uses it. Check that data meets your standards before using it for training. 3️⃣ Set a Cut-off Date: Your sales contracts from 3 years ago might not look anything like the ones you use today. If you’re training an LLM to generate proposals, don’t give them examples that don’t match your current practices! With better data, your LLM will provide more reliable results with less revision needed. #AI #machinelearning #fciso

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 High-Quality Training Data Matters in Machine Learning 🌐 Low-quality data can be the Achilles' heel for machine learning (ML) models. The pitfalls are numerous and can have cascading effects on the success of ML projects. Here's why it's vital to prioritize data quality: 1️⃣ Reduced Model Accuracy: Inaccurate predictions can arise from subpar data. 2️⃣ Overfitting: Models can get too attached to noise or outliers, faltering on fresh data. 3️⃣ Compromised Decision-making: Poor data quality can lead to flawed decision-making with lasting repercussions. 4️⃣ Increased Model Complexity: Unnecessary complexity arises when navigating noise or irrelevant features. 5️⃣ Loss of Trust: Stakeholder confidence erodes when decisions are based on dubious data. 6️⃣ Wasted Resources: Both computational power and human effort can be squandered. 7️⃣ Debugging & Validation Issues: Distinguishing between data or algorithm issues becomes tricky. 8️⃣ Bias & Fairness Concerns: Underrepresented or biased data can perpetuate systemic issues. 9️⃣ Impeded Model Convergence: Poor data can stall or hinder the training process. 🔟 Inconsistency: Diverse data representations lead to erratic model behavior. 1️⃣1️⃣ Misleading Metrics: Your evaluation might not depict the real story. 1️⃣2️⃣ Higher Costs: Financial or operational setbacks can occur. 1️⃣3️⃣ Loss of Competitive Edge: You could lag behind competition using superior data. 1️⃣4️⃣ Ethical Concerns: Real-world harm can result from ill-informed decisions. 1️⃣5️⃣ Difficulty in Generalization: Models can falter in real-world applications. 1️⃣6️⃣ Increased Maintenance: More effort in updates, retraining, and cleaning. 🔍 Data preprocessing, cleaning, and validation are the unsung heroes of a robust ML pipeline. Investing time in ensuring data quality sets the foundation for ML success. #DataQuality #MachineLearning #DataScience #AI Feel free to adapt and share this post on LinkedIn!