How to unlock value through trusted data systems

🚨 The AI Value Paradox: A Blueprint for Legacy Data Leaders Satya Nadella’s blunt take—“AI has produced almost no economic value”—is a wake-up call, not a death sentence. The truth? LLMs could unlock $10T+ in legacy data (SAP, SQL, COBOL systems, etc.), but only if enterprises fix the gateway problem. If I led a large org drowning in legacy data, here’s my playbook: 1️⃣ Stop Trying to “Lift and Shift” Forget migrating decades of data to modern warehouses. Instead, expose it via APIs—LLMs don’t care where data lives, only how it’s accessed. 2️⃣ Automate API Creation, Not Coding Manual API dev is a $2M/year trap. Use tools that auto-generate secure REST APIs for any database in minutes—no engineers needed. 3️⃣ Governance First, AI Second Would you let ChatGPT roam your financials? Enforce RBAC, OAuth2, and audit trails before connecting LLMs. Compliance isn’t optional. 4️⃣ Start Small, Scale Fast Pick one high-impact dataset (e.g., customer service logs). Let LLMs analyze it via APIs, prove ROI, then expand. 5️⃣ Kill Hallucinations with Structured Output LLMs guess when data access is messy. Standardize API responses to force factual, actionable insights. This isn’t theory. DreamFactory’s platform is how enterprises bypass AI’s “last mile” problem—turning legacy systems into governed LLM fuel without rebuilds. The math is simple: No secure APIs = No AI value Governed APIs = Your data becomes your AI moat #AI #DataStrategy #LegacySystems #LLM

❓ 𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗔𝗜 𝗶𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀 𝗳𝗮𝗶𝗹 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲? Because they skip the fundamentals. Without trustworthy, well-governed, and discoverable data, even the best AI models struggle to deliver consistent value. That’s why every organisation needs a clear, structured framework. ❓ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗧𝗿𝗶𝗻𝗶𝘁𝘆™ 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸? It’s a three-layer model designed to help organisations unlock the full value of their data and AI initiatives by building step-by-step capability: Foundational Layer Focus on data quality, governance, access, and compliance. Create trust. Semantic Layer Introduce shared understanding through metadata, ontologies, and knowledge graphs. Conversational Layer Enable everyone to interact with data using natural language and intelligent AI interfaces. ❓ 𝗪𝗵𝘆 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘀𝗮𝘁𝗶𝗼𝗻 𝗮𝗱𝗼𝗽𝘁 𝗶𝘁? ✅ It reduces duplication of effort by up to 40% ✅ Accelerates data product delivery by 3x ✅ Bridges the gap between technical teams and business users ✅ Enables true self-service, driven by trust and shared language ❓ 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗲𝗻𝗱 𝗴𝗼𝗮𝗹? A truly data-literate, AI-enabled organisation - where every person can find, understand, and use data effortlessly.

I’ve lost count of projects that shipped gorgeous features but relied on messy data assets. The cost always surfaces later when inevitable firefights, expensive backfills, and credibility hits to the data team occur. This is a major reason why I argue we need to incentivize SWEs to treat data as a first-class citizen before they merge code. Here are five ways you can help SWEs make this happen: 1. Treat data as code, not exhaust Data is produced by code (regardless of whether you are the 1st party producer or ingesting from a 3rd party). Many software engineers have minimal visibility into how their logs are used (even the business-critical ones), so you need to make it easy for them to understand their impact. 2. Automate validation at commit time Data contracts enable checks during the CI/CD process when a data asset changes. A failing test should block the merge just like any unit test. Developers receive instant feedback instead of hearing their data team complain about the hundredth data issue with minimal context. 3. Challenge the "move fast and break things" mantra Traditional approaches often postpone quality and governance until after deployment, as shipping fast feels safer than debating data schemas at the outset. Instead, early negotiation shrinks rework, speeds onboarding, and keeps your pipeline clean when the feature's scope changes six months in. Having a data perspective when creating product requirement documents can be a huge unlock! 4. Embed quality checks into your pipeline Track DQ metrics such as null ratios, referential breaks, and out-of-range values on trend dashboards. Observability tools are great for this, but even a set of SQL queries that are triggered can provide value. 5. Don't boil the ocean; Focus on protecting tier 1 data assets first Your most critical but volatile data asset is your top candidate to try these approaches. Ideally, there should be meaningful change as your product or service evolves, but that change can lead to chaos. Making a case for mitigating risk for critical components is an effective way to make SWEs want to pay attention. If you want to fix a broken system, you start at the source of the problem and work your way forward. Not doing this is why so many data teams I talk to feel stuck. What’s one step your team can take to move data quality closer to SWEs? #data #swe #ai

Last week, I shared a framework for structuring #datagovernance within #CRM platforms. This week, double-clicking on the #impact: why it matters and how to think about the outcomes it unlocks. One lens I’ve found helpful, previously used at the enterprise level, but also powerful at the data asset level, is the offensive vs. defensive framework. We can use it to make the case for #datamanagement not as overhead, but as a foundation for both protecting the business and enabling growth. 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐑𝐌 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 start with a clear data model, including consistent field definitions and metadata to ensure clarity in what’s captured. Strong reference data and hierarchy management brings structure to key entities like customers and products. A connected Customer 360 view ties everything together, while data quality rules and monitoring enforce standards from the start. Together, these are the scaffolding for both regulatory compliance and scalable value creation. On the 𝐝𝐞𝐟𝐞𝐧𝐬𝐢𝐯𝐞 side, governance ensures regulatory alignment, audit readiness, and risk reduction. This is especially important now. For one major client we worked with, the no. 1 data privacy concern was unstructured text in CRM notes, where reps were entering sensitive personal information, unknowingly triggering global privacy risks. Governance helps classify, restrict, and manage access to that kind of data before it becomes a liability. But 𝐨𝐟𝐟𝐞𝐧𝐬𝐞 is where things get exciting. Clean, reliable CRM data directly powers better segmentation, smarter recommendations, more accurate forecasts, and faster service response. Governance doesn’t slow these things down—it enables them. Attached, you’ll see seven CRM use cases where governance acts as a multiplier. Together, they can generate 𝟓%+ commercial impact. But 𝐧𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞𝐦 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐮𝐬𝐭𝐞𝐝 𝐝𝐚𝐭𝐚.

Many data people and technologists want to build data capabilities — data lakes, catalogs, lineage, warehouses, governance frameworks — thinking that’s how they’ll unlock business value. The data is a mess. Let's clean it up. Or, so the thinking goes. Capabilities alone don’t deliver results. Focusing on specific use cases does. Why? Because data is a means to an end, not the end itself. A capabilities approach is about what you can build. A use case approach is about what you want to solve. When you start with a real business problem—say, reducing churn or increasing sales—you're forced to decide what data you need, how to get it, and how to analyze it. The result? Quick wins, measurable impact, and a clear path to scaling. Without real use cases, organizations often get lost in complexity, investing in shiny tools and frameworks that never move the needle. Think about it. How many companies have massive data teams but struggle to demonstrate true value? It’s because they’re building capabilities first, then hoping use cases will somehow emerge. So, here’s my challenge to your thinking: Next time you plan your data strategy, start with the business problem. Ask: what’s the specific outcome we want? Then work backwards to the data, tools, and processes needed to make it happen.

I’ve advised 100s of organizations in my career. The secret formula to harness unstructured data: Over the last decade, I’ve helped companies navigate the complexities of digital transformation. I’ve also managed data strategies for major enterprises. During that time, I've identified 5 critical components for effective unstructured data management: → Analysis: to derive insights from diverse data sources → Storage: to handle vast amounts of data efficiently → Retrieval: to access information quickly and accurately → Governance: to ensure compliance and security → Integration: to combine structured and unstructured data for a holistic view ... As well as what happens when each is missing. • Lack of analysis = "Missed Insights" • Poor storage = "Data Overload" • Inefficient retrieval = "Lost Opportunities" • Weak governance = "Compliance Risks" • No integration = "Fragmented View" And remember, mastering unstructured data is a continuous journey. You can improve in each of these areas. Here's how to do it: 𝟭/ 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Invest in advanced analytics and machine learning technologies. Use natural language processing and sentiment analysis to understand customer feedback. 𝟮/ 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: Implement scalable storage solutions that can grow with your data needs. Consider cloud-based options for flexibility and cost-effectiveness. 𝟯/ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: Develop robust search capabilities to find and use data quickly. Use metadata and tagging systems for better organization. 𝟰/ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Create policies for data categorization, security, and compliance. Regularly audit your data management practices. 𝟱/ 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Ensure your unstructured data systems work seamlessly with your structured data. Use data integration tools to get a comprehensive view of your operations. The best organizations constantly adapt and innovate. Start using this formula today. And unlock the full potential of your unstructured data. Your business will thank you!

Business Services need clarity, not more clutter. AI can help. But only if you do these things first …   Business Services teams understand the potential of generative AI. And no one wants to be left behind. But as business services teams themselves are quick to talk to me about, there's a big difference between accelerating adoption and skipping vital steps. To have confidence in generative AI, they need to address training, security, and risk. But the cornerstone is your information architecture. Here’s why …    💸 Plug and play is a false economy   We often see a myriad of legal AI systems claim to be plug and play. The issue? Many don’t start by considering what they’re actually plugging into. AI tools are built on LLMs that require focused datasets to produce meaningful and accurate results. If you’re plugging into a chaotic information architecture, you end up with results you can’t trust. That’s why I say: IA before AI. I sound like a broken record on that one, but it is so important if you actually want to tap into the potential of AI.   🏗 You build trust with better data   Feeding your AI tools with the data they need to give you reliable outputs starts with considering where your data lives. It’s also fundamental to ensuring your organization gets the most value from its DMS. And engaging business teams to use the DMS. It's about ensuring you’re focussed about the data you’re feeding in. For example, flood the LLM with every single document available and you could get more clutter than clarity. Instead …  assemble a content collection team that establishes processes for what good data looks like. For example, putting RFP documents in a common place flagged with say client and value tagged. Point your AI to that and get trusted answers. This clarity will also allow you to unlock advanced features like Insight+'s Ask Knowledge, which allows business services to use natural language queries to get answers from focused resources.                                                                                                                     🔐 Never compromise on security   As you assemble your best data to point AI at, you need to champion security and confidentiality. Information like billing, RFPs, and employee data needs to be treated as if it were client confidential. But you also need consistency. Imagine several HR professionals want the best examples of a presentation for a talent development seminar. Depending on what kind of access they have to the firm’s files, they could all end up with different outputs from generative AI. This highlights the need for a nuanced approach to security posture and best practices for content used to run LLMs on. That’s how you keep sensitive information locked away but enable great outcomes.   Does this strike a chord? Let me know in the comments about the issues you’ve run into with AI on business data/use cases and how you’ve solved them. So much opportunity for Ai with IA!

AI at scale is a trust challenge, and that’s the biggest opportunity for every enterprise leader right now. As we partner with our customers, we see a clear shift: the conversation has moved beyond “AI for efficiency” and “responsible AI” to one of tangible, measurable value. The key to unlocking that value is trust. For our most regulated customers in healthcare, finance, and government, trust is not a “nice-to-have,” it’s a gatekeeper. These industries need to prove AI agents operate exactly as intended, protecting sensitive data and respecting jurisdictional rules from Day One. The reality is, a pilot program is only a pilot until it is built to scale. And what separates a successful pilot from a transformative enterprise solution is the foundation of trust. The governance layer has to be designed within the solution from the start, weaving together these three critical enablers: ☑️ Verification: You must be able to prove your AI agent is operating within approved business policies. This is not a technical feature; it is a way to ensure accountability and speed up time-to-market in a regulated world. ☑️ Sovereignty: Your sensitive data must be protected and observable in its rightful jurisdiction. This gives customers the peace of mind they need to truly innovate with their most valuable asset – their data. ☑️ Auditability: You must confidently demonstrate how a decision was made through transparent records that empower human oversight. This allows enterprises to stand behind their AI investments with certainty. When these three pillars converge, they remove the barriers that slow down progress and allow customers to move faster, smarter, and with greater confidence. This is where real business value emerges. Performance plateaus when trust is an afterthought. But when trust, structure, and impact advance together, we lay the foundation for true business transformation. #AIGovernance #GoogleCloud #DataSovereignty #AgentVerification

We’ve invested in AI—but where’s the business value?” That’s the question I’ve heard again and again in conversations with 4 senior data and AI leaders this past week. Here’s the hard truth: Most AI projects don’t fail because of the tech. They fail because they’re disconnected from real users, real problems, and real workflows. And with GenAI, this gap is only getting wider—or much smaller—depending on how you approach it. For years, we’ve chased the perfect data architecture—lakes, warehouses, semantic layers. But GenAI is flipping the script. Now, with the right modeling and access, you can ask a question once—and pull context-rich answers from across silos. No dashboards. No tickets. No waiting. One exec said it best: “GenAI is becoming the new UI for decision-making.” And the magic? It’s not in unifying all the data. It’s in unifying access and context. That shift is happening fast—and here are 5 patterns the most forward-thinking teams are leaning into: 1️⃣ Start with the problem, not the platform Winning teams don’t chase the next model. They focus on a painful business challenge, involve users early, and build iteratively toward something that sticks. 2️⃣ From siloed sources to connected action The real unlock? Connecting various data sources through business context. This turns fragmented systems, and knowledge into automated workflows and insight engines. 3️⃣ Trust is earned, not assumed The goal isn’t to wow users with “magic”—it’s to help them feel confident, reduce toil, and surface relevant knowledge in the flow of work. 4️⃣ Make governance invisible but real “GenAI magnifies what can go wrong.” That’s why monitoring, traceability, and controls need to be baked into every layer—not bolted on. 5️⃣ User discovery is the differentiator The winning teams are treating this like a product—not a project. They’re iterating fast, learning from users, and building systems that are extensible, not one-off. One thing’s clear: We’re not just building AI tools. We’re reshaping how decisions happen. And the teams that embrace this shift—grounded in business context, powered by smart data modeling, and committed to trust—are pulling ahead. What shifts are you seeing inside your organization? Let’s compare notes

For Chief Data Officers, the key to unlocking data’s full potential is to make it a true business driver. Here’s how an outcome-driven approach can turn data into measurable results: 1. Think Beyond Metrics—Aim for Transformational KPIs- Traditional data metrics like accuracy and volume fall short of demonstrating true value. Instead, look for KPIs that are transformational—like “time-to-insight” or “decision acceleration.” These capture how fast data helps you pivot, innovate, and win market opportunities. 2. Create a 'Data-Centric Culture' with Cross-Functional Teams- Silos are a common pitfall, but a cross-functional approach can turn data insights into shared wins. For example, embedding data leads within business units fosters a culture where everyone has a stake in data-driven decisions. When every department feels ownership, data projects gain momentum and support across the board. 3. Invest in Scalable Governance from Day One- Governance isn’t just about compliance—it’s what allows your team to scale insights quickly and confidently. Automating quality checks and setting clear data ownership across departments is critical for reliable, enterprise-level data management. This approach builds a foundation that accelerates trust and innovation.