Importance of Proactive Data Management for AI Success

Your SAP AI is only as good as your Data infrastructure. No clean data → No business impact. SAP is making headlines with AI innovations like Joule, its generative AI assistant. Yet, beneath the surface, a critical issue persists: Data Infrastructure. The Real Challenge: Data Silos and Quality Many enterprises rely on SAP systems - S/4HANA, SuccessFactors, Ariba, and more. However, these systems often operate in silos, leading to: Inconsistent Data: Disparate systems result in fragmented data. Poor Data Quality: Inaccurate or incomplete data hampers AI effectiveness. Integration Issues: Difficulty in unifying data across platforms. These challenges contribute to the failure of AI initiatives, with studies indicating that up to 85% of AI projects falter due to data-related issues. Historical Parallel: The Importance of Infrastructure Just as railroads were essential for the Industrial Revolution, robust data pipelines are crucial for the AI era. Without solid infrastructure, even the most advanced AI tools can't deliver value. Two Approaches to SAP Data Strategy 1. Integrated Stack Approach:   * Utilizing SAP's Business Technology Platform (BTP) for seamless integration.   * Leveraging native tools like SAP Data Intelligence for data management. 2. Open Ecosystem Approach:   * Incorporating third-party solutions like Snowflake or Databricks.   * Ensuring interoperability between SAP and other platforms. Recommendations for Enterprises * Audit Data Systems: Identify and map all data sources within the organization. * Enhance Data Quality: Implement data cleansing and validation processes. * Invest in Integration: Adopt tools that facilitate seamless data flow across systems. * Train Teams: Ensure staff are equipped to manage and utilize integrated data effectively. While SAP's AI capabilities are impressive, their success hinges on the underlying data infrastructure. Prioritizing data integration and quality is not just a technical necessity → It's a strategic imperative.

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 Incomplete or outdated datasets Duplicate or inconsistent records Noise from irrelevant or poorly labeled data Data quality The result?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

🚨 The real reason 60% of AI projects fail isn’t the algorithm, it’s the data. Despite 89% of business leaders believing their data is AI-ready, a staggering 84% of IT teams still spend hours each day fixing it. That disconnect? It’s killing your AI ROI. 💸 As CTO, I’ve seen this story unfold more times than I can count. Too often, teams rush to plug in models hoping for magic ✨ only to realize they’ve built castles on sand. I've lived that misalignment and fixed it. 🚀 How to Make Your Data AI-Ready 🔍 Start with use cases, not tech: Before you clean, ask: “Ready for what?” Align data prep with business objectives. 🧹 Clean as you go: Don't let bad data bottleneck great ideas. Hygiene and deduplication are foundational. 🔄 Integrate continuously: Break down silos. Automate and standardize data flow across platforms. 🧠 Context is king: Your AI can’t "guess" business meaning. Label, annotate, and enrich with metadata. 📊 Monitor relentlessly: Implement real-time checks to detect drift, decay, and anomalies early. 🔥 AI success doesn’t start with algorithms—it starts with accountability to your data.🔥 Quality in, quality out. Garbage in, garbage hallucinated. 🤯 👉 If you’re building your AI roadmap, prioritize a data readiness audit first. It’s the smartest investment you’ll make this year. #CTO #AIReadiness #DataStrategy #DigitalTransformation #GenAI

Data Quality is a blocker to AI adoption. If you don't know what your core data means, who is using it, what they are using it for, and what "good" looks like - it is terrifying to take AI-based production dependencies on data that might change or disappear entirely. As data engineers, ensuring the accuracy and reliability of your data is non-negotiable. Specifically, effective data testing is your secret weapon for building and maintaining trust. Want to improve data testing? Start by... 1. Understand what data assets exist and how they interact via data lineage. 2. Identify the data assets that bring the most value or have the most risk. 3. Create a set of key tests that protect these data assets. (more below) 4. Establish an alerting protocol with an emphasis on avoiding alert fatigue. 5. Utilize continuous testing within your CI/CD pipelines with the above. The CI/CD component is crucial, as automating your testing process can streamline operations, save time, and reduce errors. Some of the tests you should consider include: - Data accuracy (e.g. null values, incorrect formats, and data drift) - Data freshness - Performance testing for efficiency (e.g. costly pipelines in the cloud) - Security and compliance (e.g. GDPR) testing to protect your data - Testing assumptions of business logic. The other reason CI/CD testing is critical is because it informs data producers that something is going wrong BEFORE the changes have been made in a proactive and preventative fashion, and it provides context to both the software engineer and data engineer about what changes are coming, what is being impacted, and what expectations of both sides should be. Data Quality Strategy is not just about the technology you use or the types of tests that have been put in place, but on the communication patterns between producers and consumers put into place when failure events or potential failure events happen. Good luck!

Would you make critical business decisions without knowing if your data is accurate, accessible, or even trustworthy?   Many organizations do—because they lack effective data governance.   Governance isn’t just about compliance; it’s about unlocking the full potential of data. And in the age of generative AI, getting it right is more important than ever.   The 2025 Amazon Web Services (AWS) Chief Data Officer study highlights this urgency:   ➝️ 39% cite data cleaning, integration, and storage as barriers to generative AI adoption. ➝️ 49% are working on data quality improvements. ➝️ 46% are focusing on better data integration.   Effective data governance rests on four pillars:   1. Data visibility – Clarify available data assets so teams can make informed decisions. Without full transparency into what data exists and where it lives, AI models risk being trained on incomplete or irrelevant information, reducing their accuracy and reliability.   2. Access control – Balance security and accessibility to enable collaboration without increasing risk. AI adoption requires seamless yet governed data access, ensuring that sensitive information is protected while still being available for innovation.   3. Quality assurance – Ensure data is accurate and reliable for AI-driven insights. Poor data quality leads to hallucinations and flawed predictions, making robust data validation and cleansing essential for AI success.   4. Ownership – Secure leadership commitment to drive accountability and business-wide adoption. Without clear data ownership, AI initiatives struggle to scale, as governance policies remain fragmented and inconsistent across the organization.   Without a strong governance strategy, organizations risk unreliable insights, compliance issues, and missed AI opportunities.   How is your organization tackling data visibility challenges? Let’s discuss.   You can read more on Data Governance in the Age of Generative AI. https://go.aws/4j4F4ni  #DataGovernance #generativeAI #AWS #BuildOnAWS

Unlocking AI Success: Your Roadmap to Data Mastery & Readiness AI isn’t a “nice-to-have” anymore; it’s table stakes for competitive advantage. Yet too many organizations stumble at the start line, armed with ambition and budget but lacking the right data foundation and change-management playbook. Here’s how to bridge that gap: 1. Build a Rock-Solid Data Bedrock: - Data Quality & Governance: Automate validation checks, enforce clear policies, and empower dedicated data stewards. - Unified Platforms: Break down silos with cloud-native lakes and warehouses for real-time access. - Scalable Architecture: Future-proof your stack so it flexes with emerging AI agents and growing workloads. 2. Cultivate an AI-Ready Culture: People, not just technology, fuel transformation. - Leadership Alignment: Run executive workshops to nail down a shared AI vision. - Skill Building: Invest in data literacy, basic machine-learning know-how, and AI ethics. - Cross-Functional Teams: Stand up “AI Tiger Teams” that blend IT, analytics, and business experts. 3. Steer Transformation with Purpose: Digital change requires more than new tools; it demands a holistic strategy. - Strategic Roadmapping: Tie AI initiatives directly to business goals: revenue growth, cost reduction, or customer experience. - Change Management: Highlight early wins, gather feedback, and celebrate champions along the way. - Governance & Ethics: Set up oversight committees to safeguard compliance and responsible AI use. 4. Embrace AI Agents for Operational Excellence: Autonomous agents can revolutionize everything from support to supply-chain. - Use Case Identification: Start small! Think chatbots or predictive-maintenance alerts. - Pilot & Iterate: Launch MVPs, measure performance, and refine relentlessly. - Scale Responsibly: Monitor behaviors and embed guardrails to keep agents aligned with your values. By mastering your data, empowering your people, and marrying strategy with ethics, you turn AI from a buzzword into a business accelerator. Which part of this roadmap will you tackle first? —----------------- Ready to unlock AI success in your organization? Take our free AI Readiness Assessment Test: https://lnkd.in/efsUn89N Ensure you're positioned for AI success.

𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐈𝐬𝐧’𝐭 𝐀𝐛𝐨𝐮𝐭 𝐁𝐢𝐠𝐠𝐞𝐫 𝐌𝐨𝐝𝐞𝐥𝐬. 𝐈𝐭’𝐬 𝐀𝐛𝐨𝐮𝐭 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐃𝐚𝐭𝐚.  𝐇𝐞𝐫𝐞’𝐬 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐈 𝐈𝐬 𝐭𝐡𝐞 𝐑𝐞𝐚𝐥 𝐆𝐚𝐦𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐫. 1. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:    ↳ Focus on clean, relevant data, not just more data.    ↳ Reduce noise by filtering out irrelevant information.    ↳ Prioritize high-quality labeled data to improve model precision. 2. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:    ↳ Understand the environment your AI operates in. Tailor data accordingly.    ↳ Incorporate real-world scenarios to make AI more adaptable.    ↳ Align data collection with specific business goals for better results. 3. 𝐈𝐭𝐞𝐫𝐚𝐭𝐞 𝐨𝐟𝐭𝐞𝐧:    ↳ Continuously refine data sources to improve model accuracy.    ↳ Implement feedback loops to catch and correct errors quickly.    ↳ Use small, frequent updates to keep your AI models relevant. 4. 𝐁𝐢𝐚𝐬 𝐜𝐡𝐞𝐜𝐤:    ↳ Identify and eliminate biases early. Diverse data leads to fairer AI.    ↳ Regularly audit data for hidden biases.    ↳ Engage diverse teams to broaden perspectives in data selection. 5. 𝐄𝐧𝐠𝐚𝐠𝐞 𝐝𝐨𝐦𝐚𝐢𝐧 𝐞𝐱𝐩𝐞𝐫𝐭𝐬:    ↳ Collaborate with those who understand the data best.    ↳ Leverage expert insights to guide data annotation and validation.    ↳ Involve stakeholders to ensure data aligns with real-world needs. LinkedIn 𝐟𝐨𝐥𝐥𝐨𝐰𝐞𝐫𝐬? Share this post with your network to spark a conversation on why smarter data is the key to AI success. Encourage your connections to think critically about their data strategy. Let's shift the focus from bigger models to better data and make AI truly impactful. Smarter data leads to smarter decisions.  𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐚 𝐫𝐞𝐚𝐥 𝐠𝐚𝐦𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐫? ♻️ Repost it to your network and follow Timothy Goebel for more. #DataCentricAI #AIInnovation #MachineLearning #ArtificialIntelligence #DataStrategy

This visual captures how a 𝗠𝗼𝗱𝗲𝗹-𝗙𝗶𝗿𝘀𝘁, 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝘆𝗰𝗹𝗲 breaks the limitations of reactive data quality maintenance and overheads. 📌 Let's break it down: 𝗧𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝘀𝗽𝗼𝘁𝘀 𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀𝘀𝘂𝗲 But instead of digging through pipelines or guessing upstream sources, they immediately access metadata-rich diagnostics. Think data contracts, semantic lineage, validation history. 𝗧𝗵𝗲 𝗶𝘀𝘀𝘂𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗳𝗹𝗮𝗴𝗴𝗲𝗱 Caught at the ingestion or transformation layer by embedded validations. 𝗔𝗹𝗲𝗿𝘁𝘀 𝗮𝗿𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗿𝗶𝗰𝗵 No generic failure messages. Engineers see exactly what broke, whether it was an invalid assumption, a schema change, or a failed test. 𝗙𝗶𝘅𝗲𝘀 𝗵𝗮𝗽𝗽𝗲𝗻 𝗶𝗻 𝗶𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝗯𝗿𝗮𝗻𝗰𝗵𝗲𝘀 𝘄𝗶𝘁𝗵 𝗺𝗼𝗰𝗸𝘀 𝗮𝗻𝗱 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻𝘀 Just like modern application development. Then they’re redeployed via CI/CD. This is non-disruptive to existing workflows. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 𝗸𝗶𝗰𝗸 𝗶𝗻 Metadata patterns improve future anomaly detection. The system evolves. 𝗨𝗽𝘀𝘁𝗿𝗲𝗮𝗺 𝘀𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁𝗶𝗳𝗶𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 In most cases, they’re already resolving the root issue through the data product platform. --- This is what happens when data quality is owned at the model layer, not bolted on with monitoring scripts. ✔️ Root cause in minutes, not days ✔️ Failures are caught before downstream users are affected ✔️ Engineers and analysts work with confidence and context ✔️ If deployed, AI Agents work without hallucination and context ✔️ Data products become resilient by design This is the operational standard we’re moving toward: 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲, 𝗺𝗼𝗱𝗲𝗹-𝗱𝗿𝗶𝘃𝗲𝗻, 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁-𝗮𝘄𝗮𝗿𝗲 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆. Reactive systems can’t support strategic decisions. 🔖 If you're curious about the essence of "model-first", here's something for a deeper dive: https://lnkd.in/dWVzv3EJ #DataQuality #DataManagement #DataStrategy

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.