Wes Springob

"What is a Vegetation Manager?" Generic AI can only get you so far in GRC... Real insights and agent orchestration require AI that actually understands your program's context.

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Obviously I’m biased, but if you have to watch only one session 🙂 this is the one. The core of Fabric is #OneLake. The rest are just implementation details. 😁

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Usage-based MSP revenue doesn’t disappear.  It drifts.  • PSA updates that don’t reach billing  • Seats and licenses that change mid-cycle  • Vendor usage that arrives too late  • Manual fixes that quietly become permanent If usage is growing faster than revenue, it’s not a pricing issue.  It’s an enforcement issue. #MSP #UsageBasedBilling #RevenueLeakage #PSASync #BillingAutomation  #RevenueOperations #ManagedServices 

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Migrating to Snowflake? Don’t just move faster. Move smarter. 👇    Too many migrations copy yesterday’s complexity into tomorrow’s platform. Legacy reports. Duplicated KPIs. Unused dashboards. Technical debt — just relocated.    At BlueCloud, we’re seeing a different pattern emerge: AI-guided migrations that clean, rationalize, and modernize before scale.    Here’s what that looks like in practice:    ➡️ Rationalize your BI landscape before migration  ➡️ Align metrics and eliminate redundant logic  ➡️ Prioritize key data sources   ➡️ Build a phased roadmap tied to measurable outcomes    For executives, this means lower cost, clearer governance, faster time to value, and a foundation designed for AI from day one.    Learn more: https://lnkd.in/dGdyXx-s    #Snowflake #CloudMigration #EnterpriseAI #DataStrategy #ModernData 

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Data provisioning shouldn't be the bottleneck holding back your AI or analytics strategy. But too often, it is. This guide break down how to deliver secure, scalable data access–without all the friction. If your'e responsible for getting the rigth data to the right people, this is your blueprint. What you'll learn: - How to operationalize secure data access across domains - Why traditional data catalogs fall short–and how to fill the gaps - Build vs. buy: what it really takes to stand up a data marketplace - Proven steps to drive ROI and deliver business impact Get the guide: https://lnkd.in/gsdxJ7pj

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Federal data pipelines should sprint, not shuffle. We’ve teamed up with Carahsoft to show how a modular, composable stack powered by Theseus, our GPU-native SQL engine slashes query time and cost. Watch the on-demand briefing to learn: ➡️ Why composability is overtaking monoliths across federal programs ➡️ How Theseus bolts into your current tools and trims cloud spend ➡️ What “specialized by nature, efficient by design” really looks like inside an agency pipeline Catch the session here: https://lnkd.in/g7ykkCJb #FederalIT #DataModernization #ComposableAnalytics #GPUAcceleration #VoltronData #Carahsoft

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What if your Snowflake environment had its own automated data security auditor? The TrustLogix Data Security Scanner, now available in the Snowflake Marketplace, gives you automated visibility into access and compliance risks—no agents, proxies, or data movement required. It detects overly granted roles, missing masking policies, and risky privileged users, mapping every finding to clear remediation steps your team can act on immediately. Built on the AI-powered TrustLogix Data Security Platform, it turns your Trust Center into a real-time command post for policy compliance, least-privilege enforcement, and adaptable data security access across accounts and regions. Set it up in minutes and start seeing actionable results within hours. ⚡️https://hubs.li/Q03TdWy20 #snowflakemarketplace #snowflakedatasecurity

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You think you’ve migrated. But 80% of your business logic is still stuck in that Teradata box no one dares to touch. Now you have two sources of truth. Snowflake is live. Pipelines in products look slick. Cloud migration in progress. But here’s the uncomfortable truth Half your data is still stuck in Oracle, SAP, or that Teradata box no one dares to touch. You didn’t modernize. You just built a duct-taped mess hybrid trap, legacy + cloud Hybrid environments look harmless at first. But duplicating logic, moving everything with no clear priortization, is a dead game. Running duplicated systems is not goal. It's a phase. I’ve seen it across dozens of companies: → Two data teams are maintaining duplicate pipelines. → Two budgets paying to process the same data. → And one VP still asking, “Which number do we trust?” Every month you delay decommissioning legacy systems, you’re paying double. That’s your “hybrid tax.” And most execs don’t even know they’re paying it. This includes 1. Double Spend. Duplicated Licenses, compute, ETL jobs. You’re not saving by “phasing it out slowly.” 2. Compounded Complexity. Your engineers are maintaining SQL and shell scripts, dbt and old Informatica Engineers are babysitting legacy. That’s why your best people quit. 3. Broken Trust. When half your dashboards pull from the cloud and half from legacy, Nobody believes the data. Self-service, compliance reporting, AI become impossible. 4. Lost Speed & Innovation. You can’t run AI or scale insights when half your business logic still lives on an old system. This isn’t about technology. It’s about focus and ruthless simplification. 1. Quantify the Hybrid Tax. Audit total cost of ownership, storage, ETL, maintenance, labor. Show the CFO the overlap. 2. Decouple Before You Decommission. Inventory every report, pipeline, and dashboard touching legacy systems. Then kill them one by one, not “all at once.” Move only the highest impact that relevant now not 12-18 months. The goal isn’t “all in the cloud.” The goal is no dual dependencies. 3. Redesign for Reuse. Hybrid environments expose one ugly truth: Your data models are too brittle to scale, usless and 89% deleted. Lock in your business definitions (semantic layer). Build modular, reusable data products. Document everything. 4. Redefine “Done.” “Cloud migration complete” isn’t done. Replicating everything into the cloud is just duplication at scale. The goal isn’t to move all your tables. The goal is to translate what matters, retire what doesn’t, and prove ROI at each step. 5. Institutionalize the Reset. Every month, review how much of your data still touches legacy. Everything should be priortized based on impact x complexity from the data strategy What % of workloads still depend on legacy? What costs can we retire next? and so on Have seen companies depend on 3 cloud systems + legacy, everything just amplified the chaos.

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Field note #96: — Conversions Need Guardrails, Not Optimism Rules translate SQL, but guardrails prevent breakage. Namespace checks, destructive-DDL prevention, and catalog validation stop silent drift. A converter without guardrails is just a text transformer. #DataQuality #SQL #SmartConvert

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