Understanding Data Context for Transformation

As AI continues to transform how we work, the real game-changer isn't just the intelligence of the models… it’s the quality of data we feed them 𝘼𝙉𝘿 the context in which AI is applied. In an industry like life sciences, it’s not enough to know 𝘸𝘩𝘢𝘵 happened. True value comes from capturing the 𝙒𝙃𝙔. But here's the challenge... context is messy 🥴 - Systems are still siloed - Data is disparate (and often incomplete) - Journeys are non-linear - Workflows are layered with nuance Unfortunately, it was not surprising to see “Improving coordination across systems & teams” as the #1 priority in Courier Health's 𝟮𝟬𝟮𝟱 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗣𝗮𝘁𝗶𝗲𝗻𝘁-𝗖𝗲𝗻𝘁𝗿𝗶𝗰𝗶𝘁𝘆 report. Meanwhile, progress on automation or applying AI is sacrificed. AI can’t sit on top of a broken foundation. If everything is manual, data is stuck in silos, and your systems don’t talk to each other.... you will struggle to get real value from AI. Purpose-built platforms that are designed for the complexity of a specific industry are essential. A Patient CRM engineered to capture granular details (not just what was done, but why it was done, who was involved, and what happened next), unlocks a new level of insight for both people and AI. The result: smarter automation, more intelligent recommendations, and better outcomes. AI isn’t magic… It’s context, at scale ✨⤴️ https://hubs.ly/Q03mlJXw0

🚨 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

Context Isn’t Optional! AI is amazing, yes, but AI without context is just guessing with confidence. I’ve seen companies spend millions on systems that were technically correct—yet completely wrong in reality. One healthcare trusted an AI diagnosis blindly. The result? A near-miss that could have caused irreversible harm . The data wasn’t wrong. The interpretation was. The difference? Experience. The kind you don’t get from a dashboard. The kind you can’t shortcut with a prompt. The kind that comes from years of pattern recognition, mistakes, and lessons learned in the real world. That’s why our job as leaders isn’t just to adopt technology—it’s to prepare our workforce to work with it. Because without context, we risk making stupid mistakes at scale—the kind of blunders that could have been avoided with one person in the room saying, “That doesn’t look right.” Two Recommendations for Every Leader: 1. Institutionalize critical thinking. Make it a KPI to challenge and validate AI outputs before they’re acted upon. 2. Pair AI with domain expertise. Don’t let tech run unsupervised in areas where one wrong decision can create legal, financial, or reputational damage. Because AI without context is automation without direction. And automation without direction doesn’t make you faster—it just gets you lost sooner. Let’s not outsource our thinking. Let’s value experience, let’s prepare our workforce so that when AI gets it wrong, we still get it right. (READ The title CAREFULLY)

This image illustrates how I’m thinking about metadata/ontologies/knowledge graph/semantic layers Left: we have the “Governed Metadata” which contains governed business, technical, and mapping metadata.  1️⃣ Business Metadata: Your glossaries, taxonomies, ontologies. The shared language of the business. 2️⃣ Technical Metadata: Schemas, tables, columns, data types. Extracted directly from systems like relational databases. 3️⃣ Mapping Metadata: this is the bridge that connects the technical to business metadata. It’s where meaning (i.e. semantics)  happens. These three parts evolve independently (and often do). Governance is how this gets aligned otherwise this turns into a “boiling the ocean”. Together, they form the core of your enterprise brain, the metadata foundation that gives your data context, structure, and meaning. Right: AI requires context and that is why it is driving the demand for Knowledge Graphs and BI Semantic Layers. Each tool expects metadata in its own syntax or format because it is dependent on the deployment mechanism of each tool. That is why I’m calling this “Deployed Metadata”, because it represents tool-specific, executable outputs like YAML, etc. Middle: we have a “Metadata Deployment Engine” which takes the governed metadata and transforms it into the syntaxes/formats specific to downstream platforms and tools. This is what takes the governed metadata and pushes out versions to each of these downstream systems consistently. The real power: ✅ Define and Governance once ✅ Deploy anywhere ✅ Stay aligned across tools. This is how we avoid having multiple answers for the same question What should power the Governed Metadata? My position: it should be a graph, and more specifically, RDF, because: - RDF is an open web standard made to connect resources - Supports ontologies (OWL), taxonomies (SKOS), validations (SHACL), provenance (PROV), etc - Built for reuse, governance, and interoperability of metadata across systems (the Web is the largest system!) 1️⃣ Business Metadata :OrderLineItem a owl:Class ;   rdfs:label "Order Line Item" . :OrderLineItemQuantity owl:DatatypeProperty ;   rdfs:label "Order Line Item Quantity" ;   rdfs:domain :OrderLineItem ;   rdfs:range xsd:int. 2️⃣ Technical Metadata :lineitem a dw:Table ;   dw:hasColumn :quantity . :l_quantity a dw:Column ;   dw:dataType "DECIMAL(15,2)" ;   dw:isNullable true . 3️⃣ Mapping Metadata :l_quantity dw:represents :OrderLineItemQuantity . :lineitem dw:represents :OrderLineItem . If you aim to support rich, linked, governed metadata across systems, and you don’t use RDF... you're probably going to end up building something like RDF anyway… just less standardized, less interoperable, and harder to maintain. As Mark Beyer states, "metadata is a graph", and that is why data catalog and governance platforms should be on a knowledge graph architecture. I plan to share more sophisticated examples next, but wanted to get this out first and see how folks react.

What does ‘semantics’ mean and why is it important? Simply put, semantics describes the meaning of things. It also describes how something relates to something else. For example, take the word ‘fire’. It could be describing something aflame, terminating someone’s job, or shooting a weapon. The word has multiple meanings - which also mesns the only way you’ll ever understand what the intended meaning of the word, is to understand the broader context of how it’s being used. This is semantics, and it’s a critical aspect of data quality. To understand meaning is to understand intent. And without it, it’s impossible for us to know if what’s being asserted in data accurately reflects the source. Firing somebody is very different than a campfire. 🔥 I see this issue constantly in data, where different business domains have different definitions for the same concept - like customers. This is one of the biggest challenges with a data mesh - in that domain autonomy is awesome - but the tradeoff is that everyone is essentially speaking a different language. If the definitions of data vary, then their meaning is also going to vary - and then you’ve got a huge data quality issue on your hands. Despite the importance of semantics, we don’t talk about them nearly enough in the context of data quality. Why? Because understanding meaning is extremely hard in our analytical systems today. That’s because meaning is lost when you reduce data down to the intersection of a row and column. Meaning is at the heart if the ability to use data to accurately model the world, and far too often, we’re simply guessing at it. Not only is this a huge data quality challenge for analytics, it’s also a huge problem for GenAI. GenAI systems are quite good at inferring meaning, but without specific guidance, they’ll often be wrong. To more accurately understand meaning, we must also understand context or intent. To do this, we need to go beyond rows and columns. This could be text, and it could also be a knowledge graph. The latter is a particularly powerful tool to help better understand meaning, and if your not using graphs today - you should definitely be considering them. Especially if you’re thinking about using your legacy data in relational databases to feed GenAI based systems. What are other tools you’re using to help better understand meaning within your data? #semantics #ai #datagovernance

The data streaming from our buildings is useless without context, and it's holding back technology programs (and the whole f*cking industry). Building owners often wrongly assume BAS or IoT data is useful by default, finding it unlabeled, inconsistent, or incomplete. Readings like temperature or flow lack critical context ('who/what/where'), making them technically available but practically unusable without extensive cleanup. While AI excels at pattern recognition, it cannot magically fill in missing context for raw building data. AI models struggle with cryptic point names or unrecorded information; training cannot recover a sensor's location or a valve's function if never captured. There's no easy button for transforming data exhaust into insight. Building owners (and their facility and OT teams) cannot simply expect clean data to emerge from their systems. Leaving data quality to chance ends with increased labor costs during implementation, rework, delayed time to value, and failed use cases due to erroneous analytics. Here’s the key reality: it’s the building owner/operator's responsibility to run a program that builds and maintains context for your data. You don’t have to do it all yourself—there’s a growing ecosystem of vendors and tools to help—but you do need to take ownership of the outcome. That's what this week's Nexus Labs deep dive is all about...  https://lnkd.in/gri63eqw I spoke to experts Jason Koh of Mapped, Stephen Dawson-Haggerty of Normal Software, Andrew Rodgers of ACE IoT Solutions, and B. Scott Muench of J2 Innovations - a Siemens Company to get their take. But what do you think?

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