Challenges of building trusted analytics solutions

Pointing Claude at your warehouse is a trust problem you've deferred by two weeks. Week one: magic. Everyone gets answers in seconds. Week two: two execs ask the same question, get two different numbers. Claude joins the wrong tables and answers confidently anyway. Someone ships a decision off a metric fed by a pipeline that broke three days ago. You haven't accelerated your analytics function. You've replaced a slow, accurate one with a fast, unreliable one. I've seen this play out enough to know: the failure is always architecture. Trusted self-serve AI analytics is achievable. But it requires three things most teams skip: - Route to curated dashboards first. Most "analytics questions" are discovery questions. The right answer is a link, not a query. - Anchor ad-hoc analysis to a semantic layer. "Active user" should mean the same thing every time, full stop. - Run live data health checks before presenting results. A seasoned analyst doesn't just run the query. They check if the pipeline ran, if there's an open incident on that table. Claude doesn't have that institutional memory by default. Give it that capability and it starts telling you when not to trust the answer. That's the gap between an AI analyst demo and one you trust in production. We built this at Monte Carlo. It covers 100% of internal data inquiries across our 150-person company. Our analysts don't spend their days fielding ad-hoc SQL requests. They do the strategic work. The real metric is trust: how many answers can a decision-maker act on without picking up the phone? Lior Gavish shares more about how we did it. Link in comments 👇 #dataquality #aiobservability

Building Trust Between Data Producers and Data Consumers at Scale Trust does not scale with your data platform. But most organizations assume it does. Data moves faster than people. And decisions depend on data you did not produce. So let me ask you: When a critical dataset changes upstream, who is accountable for the decisions that break downstream? In most enterprises, the answer is unclear. And that is where friction starts. At scale, you are not managing datasets. You are managing dependencies across teams with different incentives, priorities, and timelines. That complexity is where trust erodes. A global retailer saw this play out. They spent 18 months building a customer lifetime value model. Strong analytics. Well validated. Then a merchandising system update changed the transaction data structure. No alert. No coordination. Three core features became invalid overnight. The model didn't fail. The relationship between producer and consumer was never defined. ➜ Trust in data is not a downstream validation problem. ➜ It is an upstream accountability design. That distinction is where most data strategies fall short. Organizations invest heavily in visibility. But visibility is not the same as trust. You can see the data and still not trust it. You see it in patterns like: ➞ Producers optimized for system performance, not downstream reliability ➞ Consumers inheriting data they cannot influence or enforce ➞ Schema changes communicated locally, not across dependencies ➞ Data quality measured in isolation from business impact The result is predictable. ➞ Teams spend more time validating than building ➞ AI initiatives slow down under repeated scrutiny ➞ Decisions are made with hesitation or hidden doubt And over time, confidence declines. Not because the data is always wrong. Because no one can confidently say it will be right tomorrow. The shift required is structural. ➞ Producers must know who depends on their data and why it matters ➞ Consumers must be informed of changes before they feel the impact ➞ Quality metrics must reflect decision impact, not system health ➞ Accountability must exist on both sides of the relationship Without that, trust remains accidental. And accidental trust does not scale. Data does not become trusted when it is consumed. It becomes trusted when accountability is designed at creation. This is not about better tooling. It is about aligning ownership with the decisions data enables. Organizations that do this well move differently. Less validation. Faster deployment. Higher confidence in action. Because trust is not rebuilt every time. It is built once, structurally. Follow Arun Gamidi for data, AI, and the leadership decisions that shape real outcomes.

𝐃𝐚𝐭𝐚 𝐭𝐫𝐮𝐬𝐭 𝐢𝐬 𝐚𝐬𝐲𝐦𝐦𝐞𝐭𝐫𝐢𝐜. 𝐘𝐨𝐮 𝐥𝐨𝐬𝐞 𝐢𝐭 𝐟𝐚𝐬𝐭. 𝐘𝐨𝐮 𝐞𝐚𝐫𝐧 𝐢𝐭 𝐬𝐥𝐨𝐰𝐥𝐲. According to Deloitte, 67% of executives say they're not comfortable accessing or using data from their analytics systems. Even in companies with strong data cultures, 37% still express discomfort. This creates a strange reality. Companies invest millions in data infrastructure. They build dashboards. They hire analysts. Then decision-makers ignore the outputs and trust their gut instead. KPMG found that 67% of CEOs prefer intuition over data-driven insights. Not because they're anti-data. Because they've been burned by unreliable numbers before. The trust gap has real causes: broken dashboards, siloed departments, alert fatigue, metrics that don't match reality. Great Expectations found that 77% of organizations have data quality issues, and 91% say it impacts company performance. Trust isn't rebuilt with better tools. It's rebuilt with consistency. Every time a number is wrong, trust drops. Every time a number is right, trust barely moves. One thing that works: pick your five most-used metrics. Run automated checks on them daily. When something breaks, fix it before anyone asks. Do this for three months. That's how trust compounds. 𝐖𝐡𝐞𝐧 𝐝𝐢𝐝 𝐬𝐨𝐦𝐞𝐨𝐧𝐞 𝐥𝐚𝐬𝐭 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐚 𝐧𝐮𝐦𝐛𝐞𝐫 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠?

Five Questions Every Governance Leader Must Ask Before Defining Their First Business Use Case If your governance program isn’t aligned to business value in the first 90 days, you are already losing executive confidence. Business stakeholders need to see business use cases delivering value and the discipline to achieve that begins before the first use case is drafted. Here are five questions (decision filters) you can ask when starting or rebooting a data or AI governance initiative to assess whether the team is ready to build a use case that matters. 1. What are the top business priorities or initiatives your organization is focused on this year? - How are these priorities aligned with the governance team’s objectives? - Governance succeeds when it anchors to enterprise goals that executives care about. - Who owns the outcome, who benefits from success, and how urgent is the problem? 2. How have data issues been addressed in the past? - Which business units have attempted to solve data challenges (IT, analytics, data engineering, architecture, etc.)? - Understanding previous efforts helps surface historical roadblocks. - What challenges did these teams face and what lessons can be applied to governance today? - This reveals the level of cross-functional support, collaboration friction, and cultural readiness. 3. Can you tell me on a scale of 1–10 how you would rate data users’ experience in the following areas? Data is easy to find. _____ Data is easy to understand. _____ Data is trusted and reliable. _____ Data can be made actionable once discovered. _____ Data consumers are self-sufficient (analysts, engineers, data scientists, etc.) or reliant on subject-matter experts. _____ - Quantifying friction surfaces where to start and how big the value gap really is. 4. What are the three most visible data issues or data-related incidents from the last year? - Which teams, customers, or executives felt the impact the most? - These examples highlight real-world consequences, delayed decisions, compliance issues, revenue leakage, or customer experience gaps and point directly to pain the business will support solving. 5. What do you see as the top three challenges to launching your data and AI governance initiative? - Which constraints, skills, clarity, sponsorship, budget, or execution may slow momentum? - Understanding risk areas early allows governance work to be designed for adoption, not resistance. Who do you report to, and who will need to see value for the program to continue scaling? In an era where AI is moving faster than enterprise guardrails, governance has only one mandate: demonstrate business relevance early and often. Your first use case is your credibility anchor, choose it wisely. What’s the single most important question you would ask at the start of a business use case for a new team? #datagovernance #aigovernance #aistrategy #transformation #collibra Kellogg Network of TEXAS Society for Information Management