Tim Connor

Analytics Governance isn’t optional in the Public Sector. It’s urgent. From misconfigured dashboards to manual QA bottlenecks, public agencies face mounting pressure to deliver trustworthy data analytics. In a world where data drives decisions and public trust, governance at the Analytics layer is no longer a nice-to-have. 🔍 Discover the 4 key reasons why Analytics Governance must be part of every public agency’s strategy, and how Wiiisdom helps make it happen. 📖 Read the full article at https://lnkd.in/ehpjvPXD #AnalyticsGovernance #governance #decisionmaking #PublicSector

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The strongest data warehouses succeed because modeling, governance, and shared meaning guide every design decision. When metadata and standards drive the process, teams trust their data and scale without confusion. ER/Studio gives organizations a model-driven foundation that keeps definitions consistent, integrations clean, and communication clear across technical and business teams. Benefits to the business: 💡 Faster approvals and smoother delivery ✔️ Consistent KPIs and trusted analytics 🔹 A scalable framework that adapts as platforms evolve Read the blog 🔗 https://lnkd.in/eR-xsFsE #ERStudio #DataArchitecture #DataGovernance #KnowledgeGraph #DataProducts

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Don’t just hire a vendor. Choose a partner. In this episode, I break down what makes a good housing data partner—and what red flags to watch out for. Because the right support will build capability, not dependency. https://lnkd.in/euVNDb6U #VendorManagement #DataPartnerships #renttConsulting #HousingStrategy

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“The Ontology is the substrate through which humans and AI work together to create operational value — rooted in your business, compliant with your norms, and measured against the outcomes you care about.“ — Palantir Chief Architect, Akshay Krishnaswamy 🎤 ⬇️

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Most BI environments do not break because teams lack dashboards. They fail when duplication, unclear ownership, inconsistent metrics, and rising platform costs compound faster than governance can keep up. If you are at the Gartner Data & Analytics Summit 2026, stop by Booth 430 to see how Control Tower helps teams identify duplication, surface unused assets, and operationalize governance across Power BI, Microsoft Fabric, Tableau, and Qlik. #BIOps #BIGovernance #MicrosoftFabric #PowerBI #Tableau #Gartner

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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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Data should drive business impact, not frustration. One of our top senior data scientists authored a simple and succinct guide to help your data projects succeed. In this guide, you’ll learn:  ✅ Why most companies find themeselves in chaotic data projects  ✅ The exact steps to optimize ROI based on data  ✅ A proven framework for making better, swifter decisions Download the guide free 👉 https://lnkd.in/entY8yam 

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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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Underpinning the mortgage industry with needle moving ai powered software and insights. Making the appraiser an AI superhero This new process will also drive better dynamic capture of data, data understanding resiliency, energy efficiency and making the process more streamlined

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AI doesn’t fail at the finish line-it fails at the starting block: your data. AI Readiness: 5 Steps to Data Confidence Silos block adoption Poor labeling wrecks accuracy Weak governance risks bias & compliance Fix it fast: Assess Gain Buy-In Evolve Extend Scale & Govern Data confidence isn’t optional in 2026 -it’s the launchpad for AI success. #AIReadiness #DataConfidence #ArtificialIntelligence #DataGovernance #AITransformation #AsturaConsulting #FutureOfWork #DigitalTransformation #AILeadership #DataStrategy

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One thing I keep seeing in enterprise AI rollouts: the highest-risk data usually isn’t in tables it’s in the messy stuff. Tickets, call transcripts, notes, emails… the place where PII shows up casually. That’s why I liked seeing Snowflake move in two practical directions recently: A clearer path to structuring unstructured data with Cortex AI Functions, so teams can extract consistent signals without building a one-off pipeline every time. AI_REDACT reaching GA automated PII detection + redaction for unstructured text, built into the platform. From an AI & Security Platform lens, this is the kind of “boring guardrail” that actually enables speed: you reduce privacy risk upfront, and you make cross-team alignment (engineering / data / security) much less painful. If you’re working with unstructured data today, where do you enforce redaction at ingestion, at query-time, or right before model access?

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Bad intent data doesn’t just hurt performance. It quietly kills margins. Here’s where money leaks: Reprocessing duplicate data Manual QA Low close rates Client churn at month 2–3 Most data companies never fix this. They just add more data sources. That makes it worse. This is exactly what we fix at Nowasys. DM "B2BDATA" https://lnkd.in/eDF8rcNy #nowasys #b2bdata #b2b #AI #ArtificialIntelligence #Automation #AIAutomation #FutureOfWork #Innovation #AIAgents #AIEngineers #AIEmployee #DigitalWorkers #Agentic #aiagents

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Conferences often compress multi-year transformations into 40 minutes. But behind every slide there are thousands of lines of legacy code, spreadsheets, cases and validation loops. At the 2025 Databricks Data + Al Summit, we shared how T1A supported PacificSource Health Plans in migrating a SAS-driven reporting data mart into Databricks Lakehouse. Using our automation accelerator Alchemist, we focused on explainability and functional equivalence. If you're responsible for enterprise modernization, this session shows what actually sits beneath the architecture diagrams. 🔊Speakers: Matt Adams and Dmitriy Alergant 🎥 Full video recording: https://www.youtube.com/ watch?v=jcBja4PPLQc

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Shell. Comcast. Nasdaq. Walmart. These companies didn't overhaul their data architecture on a whim. They did it because legacy systems became a bottleneck to growth. If your data team spends more time fixing pipelines than delivering insights, you're experiencing the same friction. The data lakehouse isn't emerging technology anymore. 70% of IT leaders expect most of their analytics will run on lakehouse architecture within three years 👇 Swipe through to understand what it is, why it matters now, and what the business case looks like for your organization.   #DataLakehouse #DataEngineering #DataArchitecture #BigData #CloudComputing #CTO #MLOps #Namasys

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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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Today’s RTI session at FABCON & SQLCON - The Microsoft Fabric & SQL Community Conferences reinforced something I’ve seen again and again in real projects: Real-time intelligence only creates value when it’s tied to action. Streaming data, live dashboards, instant alerts
They’re powerful, but only if there’s a clear decision or response behind them. Best practices matter more than ever when things move fast. Model with intent. Design for trust. Keep the business outcome front and center. Lots of good reminders to take back into real-world Fabric implementations. DataMartIn #FabConSQLCon26 #MicrosoftPartner #PartnerSocialSprintSweepstakes

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REdistribute recently launched MLS listings data offering on Snowflake. For data consumers, this means a more modern and efficient way to work with large-scale MLS data. With Snowflake, teams can access data more seamlessly, reduce delivery and ingestion overhead, improve scalability, and integrate MLS listings data directly into their existing analytics and data workflows. This launch is about removing friction. Less time spent managing data logistics. More time spent generating insights, powering products, and supporting better decisions. Excited for what this enables for data buyers, analytics teams, and real estate technology companies looking for a better way to operationalize MLS listings data. Anyone interested in learning more about it or try it out, reach out to me and I'll be happy to talk about it. #Snowflake #MLS #RealEstate #PropTech #Data #Analytics

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"All-in-One" Suite 🆚 "Best-of-Breed" Approach Most organizations aren’t 100% on any single platform. Heterogeneity is the norm. That’s why metadata freedom is becoming a critical architectural discussion. 👉 Full read: https://lnkd.in/gHnrPvxd In this article, we break down: • why vendor lock-in matters more at the metadata layer • how multi-engine environments change governance • the tradeoffs between open catalogs and vertically integrated platforms This topic affects every engineering team operating across Spark, Trino, Flink, warehouses, vector stores, and AI systems.

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We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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