How to Address Data Quality Issues for AI Implementation

𝟰𝟯% 𝗼𝗳 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Yet most organizations spend 80% on models and 20% on data. Your AI is only as smart as your data is clean. The pattern repeats across industries 👇 📊 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖��𝗶𝘀𝗶𝘀 Informatica's 2025 CDO survey found: ➜ 43% cite data quality as #1 obstacle to AI success ➜ 57% report data is NOT AI-ready ➜ Only 5% of organizations have comprehensive data governance 📉 𝗪𝗵𝗮𝘁 𝗕𝗮𝗱 𝗗𝗮𝘁𝗮 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲 The data exists but: → Lives in 47 different systems with no integration → Uses inconsistent formats and definitions → Contains unknown biases that propagate through AI → Lacks lineage—nobody knows where it came from → Has quality issues discovered only after deployment Gartner predicts 30% of GenAI projects abandoned by end of 2025 due to poor data quality. 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Organizations achieving production AI allocate 50-70% of timeline and budget to data readiness. Here's what they build: 1. 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 Completeness: Do you have sufficient volume? Accuracy: Is the data correct? Consistency: Do definitions match across systems? Timeliness: Is data current enough for decisions? Validity: Does data conform to business rules? 2. 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗣𝗿𝗼𝘃𝗲𝗻𝗮𝗻𝗰𝗲 For every data point: Where did it originate? How was it transformed? What systems touched it? When was it last validated? You can't trust AI you can't trace. 3. 𝗕𝗶𝗮𝘀 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 identify: Sample bias (unrepresentative training data) Historical bias (past discrimination baked in) Measurement bias (flawed data collection) Aggregation bias (combining incompatible data) Then engineer mitigation before deployment. 4. 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 requires: Model-specific data requirements documentation Continuous data quality monitoring Automated drift detection Regular revalidation cycles 5. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Build platforms that enable: Extraction from source systems Normalization and transformation Quality dashboards with real-time monitoring Retention controls meeting compliance requirements API access for AI consumption Data readiness is NEVER "complete." It's continuous discipline requiring dedicated ownership. The Data Excellence Test: Ask yourself these questions: ✓ Can you trace any data point from source to consumption? ✓ Can you explain its quality metrics and bias profile? ✓ Do you have automated systems detecting data drift? ✓ Can you demonstrate data governance to regulators? ✓ Do you spend more on data infrastructure than AI models? If you answered "no" to any of these, you're building on quicksand. ♻️ Repost if you've seen AI fail due to data problems ➕ Follow for Pillar 4 tomorrow: Governance & Risk 💭 What percentage of your AI budget goes to data readiness?

AI isn’t a magic fix. If the processes are broken and the data is messy, AI will only accelerate the chaos. That’s why over 80% of organizations aren’t seeing clear ROI from GenAI (McKinsey report, 2025). The risk is even greater in the construction sector. Because in most firms, data is still: - Siloed across teams - Buried in spreadsheets - Entered inconsistently (or not at all) As I spoke with Amine Nabi, CTO of BNMA, who has 30+ years of experience building software solutions for Fortune 500 and SMEs, here’s how you can build a solid foundation and prepare the data for real AI adoption and future ROI: 1. 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐚 𝐒𝐢𝐧𝐠𝐥𝐞 𝐒𝐨𝐮𝐫𝐜𝐞 𝐨𝐟 𝐓𝐫𝐮𝐭𝐡 (𝐒𝐒𝐎𝐓) This should be a system, a one place, where all key data is stored (either pick one, or build one). Relying on three systems that all say something slightly different will lead to confusion aand decisions based on incomplete or conflicting information. Define where your project, schedule, or delivery data lives, and make sure everyone is referencing the same source. 2. 𝐂𝐫𝐞𝐚𝐭𝐞 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐄𝐧𝐭𝐫𝐲 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 If one person writes “Project A" and another writes “Tower-A,” automation will break. Some examples of consistent data entry standards: - naming conventions - formats - required fields - regular update intervals Consistency makes your data usable and reliable. 3. 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐃𝐚𝐭𝐚 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐑𝐮𝐥𝐞𝐬 Good data starts at the front door. Data needs to be entered correctly and consistently. Some examples of these rules: - required fields must be filled out (you can use the pre-filled options for similar fields) - drop-downs instead of free text - date and currency formats enforced - duplicate entries flagged in real time The benefit: validation rules will save you time from cleaning up later. 4. 𝐑𝐮𝐧 𝐑𝐞𝐠𝐮𝐥𝐚𝐫 𝐃𝐚𝐭𝐚 𝐀𝐮𝐝𝐢𝐭𝐬 (𝐀𝐈 𝐜𝐚𝐧 𝐡𝐞𝐥𝐩 𝐡𝐞𝐫𝐞) Use AI to detect anomalies, catch duplicates, or flag inaccuracies. You don’t need a massive team to clean your data, you just need visibility and structure. 5. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐀𝐥𝐥 𝐘𝐨𝐮𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 Data should flow seamlessly across your systems. Your ERP, project management tool, and field systems should talk to each other. AI only works when it can “see” across your workflows. Whether you use off-the-shelf integrations or build a custom software layer, the goal is clear: Your systems should share data, not hoard it. _________________ TL;DR: If you want to future-ready your organization for AI adoption, it's crucial to start with the foundation first by having: 1. Clean, connected, consistent data 2. Clear workflows that tech can actually support 3. One version of the truth Once your data and workflows are aligned, AI adoption becomes not just possible, but far more likely to deliver real, measurable ROI. Agree? #enterprisesoftware #construction

Do you think Data Governance: All Show, No Impact? → Polished policies ✓ → Fancy dashboards ✓ → Impressive jargon ✓ But here's the reality check: Most data governance initiatives look great in boardroom presentations yet fail to move the needle where it matters. The numbers don't lie. Poor data quality bleeds organizations dry—$12.9 million annually according to Gartner. Yet those who get governance right see 30% higher ROI by 2026. What's the difference? ❌It's not about the theater of governance. ✅It's about data engineers who embed governance principles directly into solution architectures, making data quality and compliance invisible infrastructure rather than visible overhead. Here’s a 6-step roadmap to build a resilient, secure, and transparent data foundation: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗥𝗼𝗹𝗲𝘀 & 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Define clear ownership, stewardship, and documentation standards. This sets the tone for accountability and consistency across teams. 2️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Implement role-based access, encryption, and audit trails. Stay compliant with GDPR/CCPA and protect sensitive data from misuse. 3️⃣ 𝗗𝗮𝘁𝗮 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 & 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Catalog all data assets. Tag them by sensitivity, usage, and business domain. Visibility is the first step to control. 4️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Set up automated checks for freshness, completeness, and accuracy. Use tools like dbt tests, Great Expectations, and Monte Carlo to catch issues early. 5️⃣ 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Track data flow from source to dashboard. When something breaks, know what’s affected and who needs to be informed. 6️⃣ 𝗦𝗟𝗔 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Define SLAs for critical pipelines. Build dashboards that report uptime, latency, and failure rates—because business cares about reliability, not tech jargon. With the rising AI innovations, it's important to emphasise the governance aspects data engineers need to implement for robust data management. Do not underestimate the power of Data Quality and Validation by adapting: ↳ Automated data quality checks ↳ Schema validation frameworks ↳ Data lineage tracking ↳ Data quality SLAs ↳ Monitoring & alerting setup While it's equally important to consider the following Data Security & Privacy aspects: ↳ Threat Modeling ↳ Encryption Strategies ↳ Access Control ↳ Privacy by Design ↳ Compliance Expertise Some incredible folks to follow in this area - Chad Sanderson George Firican 🎯 Mark Freeman II Piotr Czarnas Dylan Anderson Who else would you like to add? ▶️ Stay tuned with me (Pooja) for more on Data Engineering. ♻️ Reshare if this resonates with you!

The only way to prevent data quality issues is by helping data consumers and producers communicate effectively BEFORE breaking changes are deployed. To do that, we must first acknowledge the reality of modern software engineering: 1. Data producers don’t know who is using their data and for what 2. Data producers don’t want to cause damage to others through their changes 3. Data producers do not want to be slowed down unnecessarily Next, we must acknowledge the reality of modern data engineering: 1. Data engineers can’t be a part of every conversation for every feature (there are too many) 2. Not every change is a breaking change 3. A significant number of data quality issues CAN be prevented if data engineers are involved in the conversation What these six points imply is the following: If data producers, data consumers, and data engineers are all made aware that something will break before a change has deployed, it can resolve data quality through better communication without slowing anyone down while also building more awareness across the engineering organization. We are not talking about more meaningless alerts. The most essential piece of this puzzle is CONTEXT, communicated at the right time and place. Data producers: Should understand when they are making a breaking change, who they are impacting, and the cost to the business Data engineers: Should understand when a contract is about to be violated, the offending pull request, and the data producer making the change Data consumers: Should understand that their asset is about to be broken, how to plan for the change, or escalate if necessary The data contract is the technical mechanism to provide this context to each stakeholder in the data supply chain, facilitated through checks in the CI/CD workflow of source systems. These checks can be created by data engineers and data platform teams, just as security teams create similar checks to ensure Eng teams follow best practices! Data consumers can subscribe to contracts, just as software engineers can subscribe to GitHub repositories in order to be informed if something changes. But instead of being alerted on an arbitrary code change in a language they don’t know, they are alerted on breaking changes to the metadata which can be easily understood by all data practitioners. Data quality CAN be solved, but it won’t happen through better data pipelines or computationally efficient storage. It will happen by aligning the incentives of data producers and consumers through more effective communication. Good luck! #dataengineering

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

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.

A lot of conversations about AI systems focus on the model. Which LLM to pick. How to prompt it. Whether to fine-tune. What matters just as much is the data that flows in. And in most enterprises, that data is messy. CRM notes with shorthand and typos. Tax documents missing fields. Email threads where the key decision is buried three replies deep. The limits of the data become the limits of the model. An advisor-facing agent can’t resolve a client’s retirement question if the income data is incomplete. A compliance workflow can’t review communications effectively if half the emails aren’t categorized correctly. At Advisor360°, we spend as much time on data quality as on models. That means normalizing across systems, filling gaps, and attaching metadata that makes records machine-readable. It also means instrumenting agents to surface when they can’t find what they need—so missing data gets corrected, not ignored. The result is that our AI teammates don’t just run faster—they run on solid ground. The quality of insights, recommendations, and actions comes directly from the reliability of the underlying data. Models matter. But without disciplined work on data quality, the best LLM in the world will still stumble on the basics.

𝗙𝗶𝘅𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗕𝗲𝗳𝗼𝗿𝗲 𝗜𝘁 𝗕𝗿𝗲𝗮𝗸𝘀 𝗔𝗜 As organizations accelerate AI adoption, many are discovering an uncomfortable truth: AI does not fix bad data. 𝗜𝘁 𝗲𝘅𝗽𝗼𝘀𝗲𝘀 𝗶𝘁. Models trained on flawed, inconsistent, or incomplete data do not create intelligence. They amplify risk, accelerate errors, and erode trust. The issue is rarely the algorithm. It is the data. In conversations with CIOs, I often hear the same phrase: “𝗢𝘂𝗿 𝗱𝗮𝘁𝗮 𝗶𝘀𝗻’𝘁 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 𝗔𝗜.” What they are describing is not a tooling gap. It is a quality and governance gap. In legacy environments, data quality challenges are deeply embedded: Inconsistent definitions across systems. Duplicate and fragmented records. Unreliable lineage and limited traceability. Pipelines designed for reporting, not operational decision-making. Data that is simply unfit for AI. When AI initiatives scale, these weaknesses surface quickly and publicly. Data quality is not a cleansing exercise. It is a governance discipline. Improving it requires: Clear ownership and accountability. Standardized definitions and metadata. End-to-end lineage and traceability. Quality controls embedded into data pipelines. Alignment between business context and technical architecture. 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗱𝗮𝘁𝗮, 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝗔𝗜. Organizations that modernize successfully treat data quality not as remediation, but as foundational infrastructure. Fix the data before it breaks the AI. #AIGovernance #DataQuality

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐜𝐡𝐚𝐬𝐞𝐬 𝐁𝐢𝐠𝐠𝐞𝐫 𝐌𝐨𝐝𝐞𝐥𝐬.   Meanwhile, their AI fails because of Bad Data.  Data Quality beats Model size every time. Here are the 8 dimensions of data quality that actually matter: 1. TIMELINESS What it means: Data is updated, accessible, and available exactly when needed Why it matters: Late data limits timely actions 2. ACCURACY What it means: Data correctly represents real-world values without errors or distortions Why it matters: Prevents incorrect decisions based on wrong information 3. COMPLETENESS What it means: All required data fields are present with no critical information missing Why it matters: Missing data weakens analysis and outcomes 4. CONSISTENCY What it means: Same data values remain identical across systems, reports, and datasets Why it matters: Inconsistencies reduce confidence and usability 5. VALIDITY What it means: Data conforms to defined formats, rules, constraints, and business standards Why it matters: Invalid data cannot be processed or trusted 6. UNIQUENESS What it means: Each real-world entity is recorded once, without duplicate or repeated entries Why it matters: Duplicates distort metrics and reporting 7. INTEGRITY What it means: Relationships between data elements remain accurate, connected, and logically preserved Why it matters: Broken relationships cause system failures 8. RELIABILITY What it means: Data consistently produces dependable results across repeated use and scenarios Why it matters: Reliable data supports long-term decision making THE PRINCIPLE You can have: • GPT-5 on garbage data = garbage outputs • Basic model on quality data = reliable insights Data quality always wins. WHAT TEAMS GET WRONG They assume more data = better AI. It doesn't. They spend months optimizing model parameters while: • Training data has duplicates (Uniqueness) • Production data is stale (Timeliness) • Fields are inconsistently formatted (Validity) • Relationships are broken (Integrity) THE FAILURE PATTERN AI works in demo (curated data) → Fails in production (real data) Why?  Demo data had all 8 dimensions.  Production data has maybe 3. MY RECOMMENDATION Before scaling any AI system, audit: ✓ Timeliness: Is data fresh enough for decisions? ✓ Accuracy: Does it match ground truth? ✓ Completeness: Are critical fields populated? ✓ Consistency: Same values across systems? ✓ Validity: Conforms to schemas and rules? ✓ Uniqueness: No duplicates? ✓ Integrity: Relationships preserved? ✓ Reliability: Consistent results over time? THE STRATEGIC INSIGHT ✓ Scaling AI without data quality is like building skyscrapers on sand. ✓ The taller you build (more complex models), the faster it collapses. ✓ Teams that win invest in data quality infrastructure before model complexity. ✓ Which data quality dimension is breaking your AI systems? ♻️ Repost this to help your network ➕ Follow Sivasankar Natarajan for more insights on Enterprise AI #GenAI #EnterpriseAI #AgenticAI