The Impact of Data Quality on Innovation Outcomes

Data Quality isn't boring, its the backbone to data outcomes! Let's dive into some real-world examples that highlight why these six dimensions of data quality are crucial in our day-to-day work. 1. Accuracy:  I once worked on a retail system where a misplaced minus sign in the ETL process led to inventory levels being subtracted instead of added. The result? A dashboard showing negative inventory, causing chaos in the supply chain and a very confused warehouse team. This small error highlighted how critical accuracy is in data processing. 2. Consistency: In a multi-cloud environment, we had customer data stored in AWS and GCP. The AWS system used 'customer_id' while GCP used 'cust_id'. This inconsistency led to mismatched records and duplicate customer entries. Standardizing field names across platforms saved us countless hours of data reconciliation and improved our data integrity significantly. 3. Completeness: At a financial services company, we were building a credit risk assessment model. We noticed the model was unexpectedly approving high-risk applicants. Upon investigation, we found that many customer profiles had incomplete income data exposing the company to significant financial losses. 4. Timeliness: Consider a real-time fraud detection system for a large bank. Every transaction is analyzed for potential fraud within milliseconds. One day, we noticed a spike in fraudulent transactions slipping through our defenses. We discovered that our real-time data stream was experiencing intermittent delays of up to 2 minutes. By the time some transactions were analyzed, the fraudsters had already moved on to their next target. 5. Uniqueness: A healthcare system I worked on had duplicate patient records due to slight variations in name spelling or date format. This not only wasted storage but, more critically, could have led to dangerous situations like conflicting medical histories. Ensuring data uniqueness was not just about efficiency; it was a matter of patient safety. 6. Validity: In a financial reporting system, we once had a rogue data entry that put a company's revenue in billions instead of millions. The invalid data passed through several layers before causing a major scare in the quarterly report. Implementing strict data validation rules at ingestion saved us from potential regulatory issues. Remember, as data engineers, we're not just moving data from A to B. We're the guardians of data integrity. So next time someone calls data quality boring, remind them: without it, we'd be building castles on quicksand. It's not just about clean data; it's about trust, efficiency, and ultimately, the success of every data-driven decision our organizations make. It's the invisible force keeping our data-driven world from descending into chaos, as well depicted by Dylan Anderson #data #engineering #dataquality #datastrategy

AI & Innovation Thursday: The Hidden Heroes of AI - Data Quality When we talk about #AI in radiology, most of the spotlight shines on the algorithms: their accuracy, speed, and clinical performance. But behind every great model is something less glamorous yet absolutely essential: data quality. Poor-quality data leads to poor-quality AI. It’s as simple as that. Incomplete or mislabeled datasets can create blind spots. Lack of diversity can lead to bias and inequities in care. Inconsistent imaging protocols can limit reproducibility across sites. On the other hand, when we invest in high-quality, diverse, and well-curated data, we build AI that is: More reliable, more generalizable, more trusted by clinicians. At GE HealthCare, we often say: AI is only as good as the data it learns from. That makes radiologists, technologists, and data stewards the hidden heroes of AI innovation. The technology may be cutting-edge but its foundation is built on something timeless: doing the basics well. For my colleagues: What’s your experience: is the biggest challenge for AI in radiology today the algorithm, or the data it depends on? #AIInnovationThursday #Radiology #ArtificialIntelligence #DataQuality #Leadership #GEHealthcare

I don’t know how else to describe my experience in recent conversations about AI in clinical research and diagnostics. . . Everyone is talking about AI-driven insights, predictive modeling, and multimodal data integration. And yet, when you ask about the quality of the underlying data feeding these models, the conversation suddenly gets a little quieter. The reality is, without clean, structured, and well-annotated data, even the best AI models won’t deliver meaningful insights. Garbage in, garbage out—that’s a cliché for a reason. And yet, data quality still doesn’t get enough attention. It’s a strange paradox. We pour millions into AI solutions, but not nearly enough into ensuring the data feeding these models is accurate, complete, and harmonized. At every event, I meet brilliant researchers and industry leaders pushing boundaries in diagnostics, drug discovery, and precision medicine. But ask them about the biggest bottleneck in AI adoption, and it almost always comes back to data quality, standardization, and usability. I’m optimistic that we’ll get there. That as the field matures, we’ll start putting as much focus on the integrity of our data as we do on the sophistication of our models. Because at the end of the day, better data means better models, better insights, and ultimately, better patient outcomes. Looking forward to more conversations on this—and hopefully, to seeing data quality get the attention it deserves. #dataquality #AI #innovation #diagnostics #clinicalresearch #biomedicalresearch #datacentric #aimodels #healthcare

I’m obsessed with one truth: 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 is AI’s make-or-break. And it's not that simple to get right ⬇️ ⬇️ ⬇️ Gartner estimates an average organization pays $12.9M in annual losses due to low data quality. AI and Data Engineers know the stakes. Bad data wastes time, breaks trust, and kills potential. Thinking through and implementing a Data Quality Framework helps turn chaos into precision. Here’s why it’s non-negotiable and how to design one. 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗿𝗶𝘃𝗲𝘀 𝗔𝗜 AI’s potential hinges on data integrity. Substandard data leads to flawed predictions, biased models, and eroded trust. ⚡️ Inaccurate data undermines AI, like a healthcare model misdiagnosing due to incomplete records.   ⚡️ Engineers lose their time with short-term fixes instead of driving innovation.   ⚡️ Missing or duplicated data fuels bias, damaging credibility and outcomes. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗮 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 A data quality framework ensures your data is AI-ready by defining standards, enforcing rigor, and sustaining reliability. Without it, you’re risking your money and time. Core dimensions:   💡 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Uniform data across systems, like standardized formats.   💡 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Data reflecting reality, like verified addresses.   💡 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: Data adhering to rules, like positive quantities.   💡 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗻𝗲𝘀𝘀: No missing fields, like full transaction records.   💡 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀𝘀: Current data for real-time applications.   💡 𝗨𝗻𝗶𝗾𝘂𝗲𝗻𝗲𝘀𝘀: No duplicates to distort insights. It's not just a theoretical concept in a vacuum. It's a practical solution you can implement. For example, Databricks Data Quality Framework (link in the comments, kudos to the team Denny Lee Jules Damji Rahul Potharaju), for example, leverages these dimensions, using Delta Live Tables for automated checks (e.g., detecting null values) and Lakehouse Monitoring for real-time metrics. But any robust framework (custom or tool-based) must align with these principles to succeed. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲, 𝗕𝘂𝘁 𝗛𝘂𝗺𝗮𝗻 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗜𝘀 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 Automation accelerates, but human oversight ensures excellence. Tools can flag issues like missing fields or duplicates in real time, saving countless hours. Yet, automation alone isn’t enough—human input and oversight are critical. A framework without human accountability risks blind spots. 𝗛𝗼𝘄 𝘁𝗼 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 ✅ Set standards, identify key dimensions for your AI (e.g., completeness for analytics). Define rules, like “no null customer IDs.”   ✅ Automate enforcement, embed checks in pipelines using tools.   ✅ Monitor continuously, track metrics like error rates with dashboards. Databricks’ Lakehouse Monitoring is one option, adapt to your stack.   ✅ Lead with oversight, assign a team to review metrics, refine rules, and ensure human judgment. #DataQuality #AI #DataEngineering #AIEngineering

The impact of AI on research & development & innovation could well be the big story. An excellent report from Arthur D. Little, "Eureka! On Steriods", explores the potential in detail. A summary of some key insights: 🤖 AI complements researchers, acting as a knowledge manager, hypothesis generator, and decision assistant. It works best as an orchestrator, integrating simulations, Bayesian models, and generative AI while keeping humans in the loop. Companies leveraging AI effectively have seen up to 10x productivity gains, proving its transformative impact in R&D&I. 📊 In AI-driven R&D&I, well-structured, high-quality data is the true competitive advantage, as algorithms are becoming commoditized. Preparing and cleaning data may take 18-24 months initially, but each iteration accelerates future progress, making robust data management the key to unlocking AI’s full potential. 🧠 AI augments rather than replaces researchers, freeing time for higher-value tasks. It enables breakthroughs by tackling problems once deemed unsolvable, like optimizing nutrition plans or predicting protein structures. As AI evolves, it is shifting from a mere assistant to a "planner-thinker", helping make complex strategic decisions based on weak signals. ⚡ Fast, iterative deployment trumps waiting for perfection, while high-quality, structured data remains the foundation for AI impact. Organizations must prioritize AI investments wisely—choosing to buy, fine-tune, or build models based on needs—while balancing trade-offs like data acquisition vs. synthesis and precision vs. recall. Upskilling teams, embedding AI talent, and aligning with IT ensure smoother adoption, while early wins and continuous monitoring keep AI models effective and trusted. 🔮 The trajectory of AI in R&D&I depends on technical reliability, public and researcher trust, and cost-effectiveness. Six future scenarios range from AI revolutionizing every aspect of innovation ("Blockbuster") to limited, low-risk applications ("Cheap & Nasty"). Organizations must prepare for uncertainty by investing in compute power, data sharing, governance, and workforce training, ensuring resilience no matter how AI evolves. There's a lot more and a lot more detail in the report, link in comments. AI in innovation is a core theme in my work, I'll be sharing more insights coming up.

My AI was ‘perfect’—until bad data turned it into my worst nightmare. 📉 By the numbers: 85% of AI projects fail due to poor data quality (Gartner). Data scientists spend 80% of their time fixing bad data instead of building models. 📊 What’s driving the disconnect? Incomplete or outdated datasets Duplicate or inconsistent records Noise from irrelevant or poorly labeled data Data quality The result? Faulty predictions, bad decisions, and a loss of trust in AI. Without addressing the root cause—data quality—your AI ambitions will never reach their full potential. Building Data Muscle: AI-Ready Data Done Right Preparing data for AI isn’t just about cleaning up a few errors—it’s about creating a robust, scalable pipeline. Here’s how: 1️⃣ Audit Your Data: Identify gaps, inconsistencies, and irrelevance in your datasets. 2️⃣ Automate Data Cleaning: Use advanced tools to deduplicate, normalize, and enrich your data. 3️⃣ Prioritize Relevance: Not all data is useful. Focus on high-quality, contextually relevant data. 4️⃣ Monitor Continuously: Build systems to detect and fix bad data after deployment. These steps lay the foundation for successful, reliable AI systems. Why It Matters Bad #data doesn’t just hinder #AI—it amplifies its flaws. Even the most sophisticated models can’t overcome the challenges of poor-quality data. To unlock AI’s potential, you need to invest in a data-first approach. 💡 What’s Next? It’s time to ask yourself: Is your data AI-ready? The key to avoiding AI failure lies in your preparation(#innovation #machinelearning). What strategies are you using to ensure your data is up to the task? Let’s learn from each other. ♻️ Let’s shape the future together: 👍 React 💭 Comment 🔗 Share

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

AI is only as strong as the data behind it. Without a trusted, integrated foundation, even the most advanced models cannot deliver sustainable value.     The enterprises that succeed are the ones that treat data strategy as a business priority, not a technical afterthought. Quality, governance, and accessibility at scale are what turn AI from isolated pilots into enterprise-wide outcomes. Without that discipline, AI efforts stall before they create measurable value.     Ed Lovely, IBM’s Chief Data Officer, underscored this point in CIO Magazine®. His perspective reinforces something I see across enterprises — the effectiveness of AI isn’t determined only by the sophistication of the model but by the strength of the data foundation it relies on. Trusted, well-governed data is what makes AI outcomes reliable, explainable, and scalable. https://lnkd.in/encgyrSz