Pete Kelley, LION

Everyone is racing to make data ready for AI. Far fewer are asking the harder question. Is AI ready for the business? We have become obsessed with cleaning data, structuring data, labeling data, connecting systems, and building pipelines so models can consume enterprise information. Such effort matters. But it is only half the job. Enterprise value is not created when AI understands the data. It is created when AI delivers the right output to the right role, at the right moment, in the right form for action. The same underlying signal does not serve everyone the same way. An operator may need an alert. A manager may need a recommendation. An executive may need a decision summary. A compliance team may need an audit trail. A government analyst may need assessed context. A defense operator may need a fast, confidence-tagged cue for action. Same data. Different role. Different mission. Different risk. Different action. This is where many AI strategies quietly break. They are designed around what the model can process, not around what the business or mission must decide. Enterprise AI is not just a data readiness problem. It is a decision delivery problem. If your AI can interpret the data but cannot adapt to the person or system expected to act on it, you have solved the technical problem and missed the business one.

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Fully aligned with this vision, regardless of the data-visualization tool being used, true value can only be achieved when insights are designed with—and for—end users. This means focusing on directly actionable KPIs that exist not just to look good, but to drive real decisions and spark meaningful actions.

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If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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🔍 Did you know that 90% of business decisions rely on data — but only a fraction of organizations truly leverage it effectively? That’s where Data Visualization comes in. Tools like Power BI, Tableau, and Python libraries don’t just show numbers — they tell stories. ✅ A well-designed chart can save hours of analysis. ✅ Clear dashboards empower teams to make faster, smarter decisions. ✅ Good visuals drive alignment across tech & business teams. In my training sessions, I focus on turning raw data into meaningful insights — skills every professional needs in today’s digital-first world. What’s your favorite data viz tool — Tableau, Power BI, or something else? 👇 #DataVisualization #Analytics #PowerBI #Tableau #AI

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Your best data people didn't sign up to be firefighters.  Every month, it’s the same scramble.  Patch the pipelines. Clean the spreadsheets. Fix the broken dashboards.  The more manual your fixes, the less strategic your team becomes.  Thats not innovation. Thats survival.  We break down the real cost of reactive data management – and how to escape the cycle.  

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Snowflake Intelligence: From Data to Decisions - Atrium's AI: AI projects fail when data quality is poor and siloed. At its core, the Snowflake platform solves this by giving access to unified insights with ...

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You trained the model. You trusted the output. But can you trace the data that led to the decision? 👀 AI in analytics isn’t dangerous because it’s wrong. It’s dangerous because it’s convincing, even when it’s wrong. 📰 This article breaks down the real risks of AI-powered insights and how to design for trust with traceability, governance, and human oversight. Read it here: https://lnkd.in/eKuQnv7J

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What’s the biggest challenge when evaluating Data Engineering roles? 🫵 Hard to gauge real experience! Experience vs data volumes! Tech stack fit is unclear!

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Palantir Technologies recently released a new Foundry update that lets you build directly on your source tables, removing the need for manual syncing. As Ontologize’s Ben Thomas explains, most teams used to have to move data into Foundry before they could start modeling. With this update, you can create ontology object types backed by virtual or iceberg tables, so your data stays where it already lives, whether that’s in Snowflake, S3, or another external system. It’s a small change that removes hours of setup and makes it easier to model directly on your source systems. Try it out, and let us know what you think!  

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Please stop calling every pipeline a “data product.” I see it a lot, teams proudly calling their dashboards or ETL pipelines “data products.” But a real data product is way more than just moving or storing data. It’s something people can trust, use, and actually make decisions with. It has clear ownership, context, and guarantees for the businesses relying on it. From my experience running enterprise analytics projects, the difference between a pipeline and a true data product is night and day. Here’s what sets it apart: - Purpose drives design: A true data product solves a specific business problem, not just generates numbers. - Context is key: Metadata, lineage, and business meaning make data understandable and actionable. - Accessibility is non-negotiable: It should be discoverable, self-service, and seamlessly integrated into workflows. - Operational guarantees: Reliability, freshness, and consistent behavior are what separate a product from a pipeline. Pipelines are the engine, but the data product is the experience for the user. Without the human-led strategy, governance, and adoption focus, AI or automation alone cannot turn raw data into decision-making power. When was the last time your team built a “data product” that people actually relied on, and not just admired on a slide?

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“It’s not whether you can build the system. It’s whether you can build the right one.” Modern tooling makes it easy to stand up production-grade data systems. The harder problem is defining shared concepts across an entire firm and ensuring those definitions hold as the organization grows. In our latest Substack post, we write about how data strategy has shifted from a technical challenge to a business one, and what that means in practice. 🔗 https://lnkd.in/ej3caCXp

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