The Importance of Data Precision in Decision Making

If You Can't Trust Your Data, You Can't Trust Your Decisions. 𝗣𝗼𝗼𝗿 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘁𝗵𝗮𝗻 𝘄𝗲 𝘁𝗵𝗶𝗻𝗸—𝗮𝗻𝗱 𝗶𝘁 𝗰𝗮𝗻 𝗯𝗲 𝗰𝗼𝘀𝘁𝗹𝘆. Yet, many businesses don't realise the damage until too late. 🔴 𝗙𝗹𝗮𝘄𝗲𝗱 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁𝘀? Expect dire forecasts and wasted budgets. 🔴 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗿𝗲𝗰𝗼𝗿𝗱𝘀? Say goodbye to personalisation and marketing ROI. 🔴 𝗜𝗻𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗱𝗮𝘁𝗮? Prepare for delays, inefficiencies, and lost revenue. 𝘗𝘰𝘰𝘳 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘪𝘴𝘯'𝘵 𝘫𝘶𝘴𝘵 𝘢𝘯 𝘐𝘛 𝘪𝘴𝘴𝘶𝘦—𝘪𝘵'𝘴 𝘢 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘱𝘳𝘰𝘣𝘭𝘦𝘮. ❯ 𝑻𝒉𝒆 𝑺𝒊𝒙 𝑫𝒊𝒎𝒆𝒏𝒔𝒊𝒐𝒏𝒔 𝒐𝒇 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 To drive real impact, businesses must ensure their data is: ✓ 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 – Reflects reality to prevent bad decisions. ✓ 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 – No missing values that disrupt operations. ✓ 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 – Uniform across systems for reliable insights. ✓ 𝗧𝗶𝗺𝗲𝗹𝘆 – Up to date when you need it most. ✓ 𝗩𝗮𝗹𝗶𝗱 – Follows required formats, reducing compliance risks. ✓ 𝗨𝗻𝗶𝗾𝘂𝗲 – No duplicates or redundant records that waste resources. ❯ 𝑯𝒐𝒘 𝒕𝒐 𝑻𝒖𝒓𝒏 𝑫𝒂𝒕𝒂 𝑸𝒖𝒂𝒍𝒊𝒕𝒚 𝒊𝒏𝒕𝒐 𝒂 𝑪𝒐𝒎𝒑𝒆𝒕𝒊𝒕𝒊𝒗𝒆 𝑨𝒅𝒗𝒂𝒏𝒕𝒂𝒈𝒆 Rather than fixing insufficient data after the fact, organisations must 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 it: ✓ 𝗠𝗮𝗸𝗲 𝗘𝘃𝗲𝗿𝘆 𝗧𝗲𝗮𝗺 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲 – Data quality isn't just IT's job. ✓ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 – Proactive monitoring and correction reduce costly errors. ✓ 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Identify issues before they impact operations. ✓ 𝗧𝗶𝗲 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 – Measure the impact on revenue, cost, and risk. ✓ 𝗘𝗺𝗯𝗲𝗱 𝗮 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 – Treat quality as a mindset, not a project. ❯ 𝑯𝒐𝒘 𝑫𝒐 𝒀𝒐𝒖 𝑴𝒆𝒂𝒔𝒖𝒓𝒆 𝑺𝒖𝒄𝒄𝒆𝒔𝒔? The true test of data quality lies in outcomes: ✓ 𝗙𝗲𝘄𝗲𝗿 𝗲𝗿𝗿𝗼𝗿𝘀 → Higher operational efficiency ✓ 𝗙𝗮𝘀𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 → Reduced delays and disruptions ✓ 𝗟𝗼𝘄𝗲𝗿 𝗰𝗼𝘀𝘁𝘀 → Savings from automated data quality checks ✓ 𝗛𝗮𝗽𝗽𝗶𝗲𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 → Higher CSAT & NPS scores ✓ 𝗦𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 → Lower regulatory risks 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗱𝗮𝘁𝗮 𝗱𝗿𝗶𝘃𝗲𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀. 𝗣𝗼𝗼𝗿 𝗱𝗮𝘁𝗮 𝗱𝗲𝘀𝘁𝗿𝗼𝘆𝘀 𝘁𝗵𝗲𝗺.

𝐃𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈—𝐢𝐭'𝐬 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞𝐥𝐲 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥. AI solutions, particularly those embedded in ERP systems, are designed to deliver valuable insights and recommendations to businesses. However, the 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐥𝐢𝐧𝐤𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐮𝐧𝐝𝐞𝐫𝐥𝐲𝐢𝐧𝐠 𝐝𝐚𝐭𝐚. In traditional ERP implementations, businesses often found themselves achieving systems that were "on time, on budget, fully functional, and disappointing." Why? Because while the system technically worked, the data feeding it wasn't accurate enough to meet real-world expectations. Incorrect customer addresses, inaccurate inventory data, or faulty financial figures significantly compromised the value of the entire system. 𝐖𝐢𝐭𝐡 𝐀𝐈, 𝐭𝐡𝐞 𝐬𝐭𝐚𝐤𝐞𝐬 𝐚𝐫𝐞 𝐞𝐯𝐞𝐧 𝐡𝐢𝐠𝐡𝐞𝐫. AI-driven recommendations depend heavily on the accuracy and quality of data. If AI bases its recommendations on inaccurate or inconsistent data, users quickly lose trust and confidence in these insights, eventually ignoring them entirely. This lack of trust diminishes the value of AI systems, no matter how sophisticated the algorithms are. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐧𝐨𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 "𝐀𝐈 𝐢𝐬 𝐠𝐨𝐨𝐝 𝐚𝐭 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐛𝐚𝐝 𝐝𝐚𝐭𝐚" 𝐢𝐬 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐥𝐲 𝐟𝐥𝐚𝐰𝐞𝐝. While AI may process large volumes of data quickly, poor-quality input inevitably leads to poor-quality outcomes. 𝐀𝐈 𝐚𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐬 𝐛𝐨𝐭𝐡 𝐭𝐡𝐞 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬 𝐚𝐧𝐝 𝐰𝐞𝐚𝐤𝐧𝐞𝐬𝐬𝐞𝐬 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚—meaning bad data can severely degrade your results and decision-making quality. One of the longstanding strengths of SAP systems is their reliability and trustworthiness. Businesses have confidence in SAP solutions because they know the integrity of their data is preserved and accurately managed throughout the process. This reliability is especially critical in the age of AI, where the value derived is directly proportional to the quality of data provided. 𝐒𝐢𝐦𝐩𝐥𝐲 𝐩𝐮𝐭: 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈. 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐭, 𝐞𝐯𝐞𝐧 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐰𝐨𝐧'𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐭𝐡𝐞 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐯𝐚𝐥𝐮𝐞.

Despite decades of efforts to improve data usability, data quality and data governance programs are still failing to deliver real value. Not because leaders don’t care, not because the frameworks are unfamiliar, and certainly not because organizations lack frameworks or technology. The issue is that too many efforts prioritize policies, committees, and process formalities while overlooking the central question: How does this information actually support a business decision or create value? The problem is a persistent disconnect between these activities and the actual business use cases that give information its value. Too many programs still emphasize policies, committees, and process checklists without first defining how the data is supposed to support decisions, operations, or analytical objectives. When governance is treated as an academic exercise and is effectively decoupled from specific use cases, it just contributes to corporate overhead. And with the rapid adoption of AI, this gap becomes more than just inefficiencies. More critically it opens the door for hazardous scenarios with serious negative impacts. AI systems don’t simply consume data; they operationalize it. Any ambiguity, inconsistency, or defect in the underlying information doesn’t stay confined to a report. It cascades through models, influences predictions, and ultimately affects automated actions. In other words, poor data quality becomes systemic. If organizations expect AI to deliver value, they need to rethink their approach: 🔹 Begin with the business context. What decision, workflow, or outcome depends on this information? 🔹 Define quality and governance requirements based on that context. Precision, timeliness, lineage, trust are defined in relation to information use and not universally specified. 🔹 Prioritize activities that increase information utility. Not more rules, but more clarity and more alignment with business purpose. 🔹 Measure success by improved outcomes. Not by how many policies were published or meetings were held. Data governance isn’t about enforcing rules; it’s about enabling better decisions. Data quality isn’t about fixing errors; it’s about increasing the utility of information. Both should exist to ensure information reliably supports the work that creates business value. If organizations fail to anchor these efforts in real use cases, AI won’t fix business problems but instead will rapidly expose and scale inefficiencies. If we fail to anchor these efforts in business use cases, AI won’t compensate for the gaps. Instead, it will amplify them, and organizations will experience those failures at scale. It’s time to shift the focus from managing data as an asset to ensuring information delivers value where it matters.

𝗧𝗵𝗲 𝗜𝗹𝗹𝘂𝘀𝗶𝗼𝗻 𝗼𝗳 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: 𝗪𝗵𝘆 𝗖𝗹𝗲𝗮𝗻 𝗗𝗮𝘁𝗮 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗠𝗼𝗱𝗲𝗹𝘀 We love shiny things. Advanced analytics. AI-powered dashboards. Predictive models that promise to “reveal the future.” But here’s the catch: 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗠𝗜𝗦 𝗱𝗮𝘁𝗮 𝗶𝘀 𝗺𝗲𝘀𝘀𝘆, 𝗻𝗼 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗶𝗻 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱 𝗰𝗮𝗻 𝘀𝗮𝘃𝗲 𝘆𝗼𝘂. I’ve seen it time and again—leaders invest in complex reporting systems, but the real bottleneck isn’t the sophistication of the model... it’s the reliability of the inputs. Garbage in, garbage out. Think about your Excel MIS sheets. Are they designed to be error-proof, or do they rely on human vigilance? Here are 3 simple practices that can change everything: ✅ 𝗗𝗮𝘁𝗮 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 – Prevent wrong entries before they creep in. ✅ 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗙𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴 – Let your sheet flag the anomalies visually. ✅ 𝗔𝘂𝗱𝗶𝘁 𝗧𝗿𝗮𝗶𝗹𝘀 – Keep track of who changed what, and when. These aren’t “advanced” techniques. They’re basic 𝘩𝘺𝘨𝘪𝘦𝘯𝘦. Yet, they make the difference between a decision rooted in confidence and one built on illusion. The real power in data-driven decision making lies not in complexity, but in reliability. Get the basics right, and suddenly, even the simplest models will shine. 👉 𝙃𝙤𝙬 𝙖𝙧𝙚 𝙮𝙤𝙪 𝙚𝙣𝙨𝙪𝙧𝙞𝙣𝙜 𝙮𝙤𝙪𝙧 𝙈𝙄𝙎 𝙙𝙖𝙩𝙖 𝙨𝙩𝙖𝙮𝙨 𝙘𝙡𝙚𝙖𝙣 𝙖𝙣𝙙 𝙧𝙚𝙡𝙞𝙖𝙗𝙡𝙚 𝙗𝙚𝙛𝙤𝙧𝙚 𝙞𝙩 𝙥𝙤𝙬𝙚𝙧𝙨 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣𝙨? #DataDrivenDecisionMaking #DataAnalytics #DataAccuracy #DataValidation

Not All Data is Equal: What Dr. Eli Goldratt Warned Us About in 1982 — And Why It Matters Even More Now in the Age of AI & Digital Twins In his seminal 1982 article, “Over 95% Data Accuracy—A Need or Just a Myth?”, Dr. Eli Goldratt challenged a dangerous assumption still prevalent today: That we must collect accurate, "real-time" data before we can make good decisions. In the video below from the late 1980's, Dr Goldratt proposed something radically practical instead: “Only a few data points actually matter. Focus on those few—the ones that, if wrong, would materially impact the bottom line.” 📊 He distinguished between: A data system, where all data is treated as equally important, and An information system, where data is prioritized based on the sensitivity of decisions to inaccuracies. In today’s world of AI agents and autonomous decision-making, this insight couldn’t be more relevant. We now have systems that: Depend on accurate data to make real-time decisions Learn from patterns in data to make recommendations Even act on our behalf in trading, hiring, inventory, or medical diagnostics. ⚠️ But here’s the problem: Some data (like demand forecasts) can never be 100% accurate. Some data is just too costly or impractical to measure precisely in real-time. And yet—decisions must still be made. 💡 So what do we do? We must ask: Of all the MANY data points we could measure, which FEW truly matter for making good decisions? And this is where AI and Digital Twins become powerful allies. ✅ Use AI + Digital Twins to simulate decisions and test sensitivity: Which variables cause wild swings in outcomes if inaccurate? Which don’t materially change anything—even if way off? Which data must be real-time (fractions of a second)? Which is “good enough” at weekly or monthly granularity? 🔁 Example: In short-cycle trading, being a few milliseconds more accurate than your competitors yields massive gains. But in long-term investment strategy, average weekly prices may be more than enough. We apply this principle in Digital Twins of supply chains and capital projects: 👉 Before obsessing over collecting every possible data point, we let the simulation and AI show us which variables drive the biggest differences in outcomes. 🎯 The result? Better decisions, lower cost of data acquisition, and systems that stay robust—even when not all data is perfect. This is Goldratt’s wisdom in action. ✅ Actionable Tip: Next time you’re building an AI model, dashboard, or process automation, ask not: “How accurate is the data?” but rather: “If this data is wrong, will it matter to the outcome?” How are you deciding what data points need to be accurate and which not? Would love to hear your views and questions on this... #Goldratt #TheoryOfConstraints #impossibleunless #AI #DigitalTwins #DecisionMaking #DataScience #MRP #SupplyChain #BusinessIntelligence #Simulation #Focus #InformationSystems #AgentryAI

All data ultimately has a human source—it is not collected, but created. Data-savvy leaders understand this nuance. Decision infrastructures are often built on the premise that data is objective, definitive, and value-neutral. This leads organizations to treat data as an infallible compass. However, every byte of information springs from human actions, decisions, interactions, goals, and biases. Customer data, for example, doesn't just show behavior but reflects how people navigate interfaces we've designed, within constraints we've established. Even pristine financial data carries the imprint of human judgment—from revenue recognition timing to expense categorization—codified in vast accounting guidelines, but human-made nonetheless. Does this mean data is just subjective figures open to any conclusion? Of course not! It means that for proper understanding and interpretation, data's context is vital. All that metadata and methodology documentation isn't a footnote, but a crucial user's manual. Even the most carefully constructed dataset can be misinterpreted without proper context. This demands a targeted response. Implementing the following five specific structural changes can help address this reality: 1️⃣ Make the documentation of collection methods, decision points, known biases, and limitations a part of your data quality metrics. 2️⃣ For major decisions, require stakeholders to articulate which assumptions the data implicitly reflects and how changes would affect conclusions. 3️⃣ Pair data specialists with subject matter experts who understand the contexts generating the data. Formalize this collaboration for critical insights. 4️⃣ Integrate behavioral variables into risk assessment by testing how human motivations could invalidate data patterns. Create alternate scenarios for more robust strategies. 5️⃣ Establish mechanisms to test data-derived insights against lived experiences, where frontline observations can challenge or validate data-based conclusions. When businesses acknowledge that humans shape every piece of data, they gain insights that others miss and avoid misinterpretations, strategic missteps and compliance failures (like algorithmic bias). Success comes not from making data more human-friendly, but from recognizing data as fundamentally human in the first place.

Last week, a CEO told me something that hit hard. “Our strategy meetings always start with ambition… and end with doubt.” Why? Because the first 10 - 20 minutes are spent arguing whether the numbers in the report are even correct. The head of sales says, “These figures don’t match my dashboard.” Finance says, “That’s not the revenue I’m seeing.” Operations says, “Our system shows something else.” By the time the dust settles, the energy in the room is gone — and so is the decision we were supposed to make. This isn’t rare. Across industries, I hear the same story. More time reconciling data than acting on it. Meetings derailed by disputes over accuracy. Big decisions postponed because the foundation (data) is shaky. The fix isn’t “more reports.” It’s a strong data & analytics practice that ensures the numbers are trusted before the meeting even starts. Because when data is reliable, decisions can be bold. 💬 Have you been in a meeting where the data debate killed the decision?

"Without data, you're just another person with an opinion." –W. Edwards Deming Strategy without trusted data is not strategy. It is assumption dressed as strategy. Data connects a company's mission, vision, and values to real decisions. It tells leadership whether the business is actually delivering what it says it stands for. If the mission is customer excellence, data should show service levels, response times, retention, and resolution speed. If the vision is growth, data should show where revenue is expanding, where margin is improving, and where risk is rising. If the values include accountability and integrity, those should be visible through transparent reporting, strong controls, and disciplined decisions. This is why Data Governance matters. Data Governance is the operating framework that defines what data matters most, who owns it, how it is defined, classified, protected, and trusted at the decision layer. Classification is critical because not all data carries the same sensitivity, value, or risk. A company must know what is public, internal, confidential, or restricted so access and protection match actual exposure. It answers: - Who owns customer master data? - What does "on-time delivery" mean enterprise-wide? - Why does finance report one number while operations reports another? - Who has access to sensitive data, and how is that reviewed? Without those answers, dashboards become debates and leadership spends more time arguing over numbers than acting on them. Governance must also be tested end-to-end. Validating that data feeding executive dashboards is accurate from source to report. Confirming definitions are consistent, classifications applied correctly, and exceptions escalated. If sales, finance, and operations define "customer profitability" differently, leadership acts on fragmented logic. If that data is also misclassified, the organization now has a decision-quality issue and a control issue. That is a business risk. At the Board and C-Suite level, data is a strategic asset. When governed well: better capital allocation, stronger compliance, responsible AI, and faster execution. When governed poorly: noise, rework, exposure, and avoidable risk. Judgment is strongest when it is grounded in data that is trusted, governed, and proven in practice. That is when data stops being a reporting exercise and starts becoming a business advantage. #DataGovernance #DataStrategy ♻️ Repost if this made AI architecture click ➕ Follow Shellie Delaney for daily insights on leadership, AI and cybersecurity

Procurement decisions are only as good as the data behind them. Unfortunately, I’ve often seen million-dollar strategies built on numbers that are incomplete, outdated, or simply incorrect. The consequences of this can be significant: inflated costs, compliance risks, and strained supplier relationships, all stemming from a weak foundation. In my experience, inaccurate data is more dangerous than a challenging supplier. You can negotiate with a vendor, but you cannot negotiate with bad information. The solution isn’t complicated, but it does require discipline: 1️⃣ Integrate systems so that data flows seamlessly. 2️⃣ Conduct regular audits; don’t wait for a crisis to assess your data. 3️⃣ Train teams to view data accuracy as everyone’s responsibility. The organizations that thrive are those that can trust their procurement intelligence to guide strategic decisions, rather than just day-to-day transactions. 👉 How confident are you in the accuracy of your procurement data today?

You build a powerful system and then realize the data behind it can’t be trusted. And suddenly everything starts to break in subtle ways. Outputs look correct… but feel off. Decisions get made… but confidence drops. And fixing it later becomes expensive. Because AI doesn’t fail loudly. It fails quietly through bad data. Here’s what actually matters beneath the surface 👇 𝗗𝗮𝘁𝗮 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 If your data isn’t accurate, complete, and consistent, nothing else in the system will behave reliably. 𝗧𝗵𝗶𝗻𝗸 𝗟𝗮𝘆𝗲𝗿 (𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗟𝗼𝗴𝗶𝗰) Models can reason and generate outputs, but they amplify whatever quality of data you feed them. 𝗢𝗽𝗲𝗿𝗮𝘁𝗲 𝗟𝗮𝘆𝗲𝗿 (𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 & 𝗦𝘆𝘀𝘁𝗲𝗺𝘀) Automation only works when the inputs are trustworthy, otherwise you just scale bad decisions faster. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀 Strong systems detect issues early, contain failures, and prevent bad data from spreading downstream. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 When data is reliable, systems can scale confidently without constant firefighting. 𝗖𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 Fixing data early reduces rework, avoids bad decisions, and keeps operations efficient. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Tracking data quality, drift, and system behavior ensures problems are visible before they become critical. Final Insight People usually invest in models first, but the real leverage is in data. Reliable AI isn’t built on smarter models. It’s built on trustworthy data. If your system had to make a critical decision today… would you trust the data behind it? Follow Sumit Gupta for more such insights!!