Rob Goretsky

The Silent Killer in MarTech: Data Debt Everyone talks about tech debt, but very few talk about data debt. Data debt happens when companies accumulate years of fragmented, duplicative, or poorly governed data across platforms. The result? - CDPs and CRMs that don’t talk to each other. - “Single customer view” dashboards that are anything but single. - Teams spending more time cleaning and reconciling data than actually using it. Why this matters: AI, personalization, and analytics are only as good as the foundation beneath them. If your data is riddled with gaps and contradictions, your MarTech stack can’t deliver on its promise. A few ways to start addressing data debt: 1. Audit: Know where your data lives and who owns it. 2. Prioritize: Not all data is worth saving. Focus on what drives customer and business outcomes. 3. Govern: Define rules for collection, storage, and integration before adding another tool. Data debt isn’t just a back-office problem, it’s a growth limiter. I’ve seen organizations pour millions into new MarTech tools, only to end up with fancy dashboards powered by broken data. The stack looks impressive, but the insights can’t be trusted. Fixing data debt doesn’t get the spotlight, but it pays off in every area that matters: more accurate personalization, cleaner reporting, smoother integrations, and ultimately, better customer experiences. Leaders who treat data debt like a strategic priority, not a technical chore, will have a real advantage. When was the last time you audited the data that fuels your MarTech stack, not just the tools themselves? #MarTech #EmergingTech #DataStrategy #DigitalTransformation #MarketingTechnology

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With the growing adoption of Snowflake Semantic Views, we’ve been hearing consistent requests for better versioning, stronger collaboration workflows, and more automation around how teams create and manage their semantic layers. I’m excited to share that we now have a #Terraform package — available in the Snowflake Terraform Registry — that delivers exactly that. Shreyas Arur breaks it down in detail in his blog post: https://lnkd.in/gUvCPxsS In short, the new snowflake_semantic_view resource lets you define Semantic Views (tables, relationships, metrics, and dimensions) as code. This brings semantic logic into the same IaC patterns many teams already use for roles, warehouses, and other Snowflake objects. With Terraform state and CI/CD, you can automate creation, updates, renames, and teardown; ensure changes are versioned and peer-reviewed; and maintain a reliable, testable semantic layer over time. Link to the Terraform package is in the comments.

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🔥 Nothing performs better than the Truth. Big news: Truthset + Databricks are officially teaming up. Starting now, marketers can bring Truthset’s Data Ratings directly into their Databricks environment — making it easier than ever to measure, tune, and optimize consumer data accuracy before it impacts performance. Because let’s be honest… Better inputs = better outcomes. With this integration, you can: • Evaluate the accuracy of your first-party and licensed data • Dial accuracy up or down (AAA → B) depending on your goals • Balance scale and precision with total transparency • Activate with confidence across clean rooms, UID2, and more No black boxes. No blind trust. Just measurable truth built directly into your workflow. Smarter data decisions start upstream and now they start inside Databricks. Because at the end of the day… Nothing performs better than the Truth. 🚀 #Databricks #Truthset #DataQuality #MarTech #AdTech #MarketingPerformance https://lnkd.in/dGhYqg2E

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Another great post from Marc Bowes about SQL performance and scaling in DSQL. Marc says: "In particular, TPC-B sucks on DSQL because it runs into the hot for write key problem. Every transaction touches the single branch entry. If you’re interested in scaling OLTP workloads, you already know not to do this. Hot keys perform way better on single-node systems that on DSQL, but they’re still fundamentally a performance inhibitor. With DSQL, we’ve focused our initial release on patterns that scale well, so that you don’t run into gotchas down the line." Check out the post here: https://lnkd.in/gYwR825u

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Skills over headcount Hiring more data scientists isn’t the only lever. Enabling Citizen Data Scientists (CDS) might deliver value faster. Enter CDS - Domain experts + low/no-code = workable models and insights. - They handle “simple to moderately sophisticated” analytics and pass the best ideas to DS for production. Operating model - Educate: core analytics concepts, basic stats, reading model outputs. 📘 - Equip: AutoML, metadata management, data glossaries, self-service marketplaces. ⚙️ - Enable: sandboxes to test safely without touching production. 🧯 Outcome - Higher throughput of experiments. - Better alignment between business needs and DS work. - DS time shifts to advanced science, not “1+1=2” requests. 👉 Question: If you launched a CDS program next quarter, what’s the first capability you’d teach? Bonus - How it works: 1️⃣ Governance 2️⃣ Controls 3️⃣ Education and limits ... the goal is giving the right tools at the right moment for the right tasks. 🔗 Full article published on Medium (link in comments)

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“the new round is about $1 billion and was wildly oversubscribed” This new round, however, was raised to pursue two specific projects — a database for AI agents and its AI agent platform — Databricks co-founder and CEO Ali Ghodsi told TechCrunch in an interview. The company will invest heavily in its database for AI agents, making it generally available to all customers. It launched the product, known as 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲, in June at its annual tech conference. 𝗟𝗮𝗸𝗲𝗯𝗮𝘀𝗲, which is based on the open source database Postgres, is enterprise grade and supports corporate developers’ vibe-coding projects. The second project Databricks will be investing heavily in is AI agent platform 𝗔𝗴𝗲𝗻𝘁 𝗕𝗿𝗶𝗰𝗸𝘀, also launched in June. “Everybody’s super focused on superintelligence,” Ghodsi said. “But that’s not what we need in organizations.”

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Databricks Lakebase just dropped! It finally bridges operational and analytical databases - something data pros have wanted for years. By bringing separation of compute and storage to Postgres-like operational systems, it enables AI agents to spin up sandboxed, versioned databases in seconds. Imagine your coding agent testing new features on a full product DB clone - safely, instantly. Total game changer.

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I don't think AI has killed SaaS (yet) but I do wonder if it's killed "value-based pricing" I've been doing a lot of software shopping the last month, particularly in the conversational AI space, where there's a Cambrian explosion of vendors figuring out business models in real time. You can find it all: conversation-based, outcome-based, platform + usage-based, and tier-based tied to some specific platform feature. Overall, "value-based" is the framing I'm most allergic to these days. Time was, a vendor could win with a high sticker price by anchoring to the outcome or business impact the solution would drive. This is generally a sales best practice. But at a time where the line between build and buy grows ever thinner, when customers have built their own agents, understand inference costs, and have seen how thin some product wrappers actually are -- value-based feels untethered. The age of AI doesn't mean I'm going to build every tool in Claude Code. But higher buyer literacy DOES mean that the relationship between your sticker price and your underlying cost-of-goods-sold is going to get more scrutiny. I'm happy to rent your application layer (and the expertise it encodes) and pay a reasonable cost for maintenance and support. But charging a disproportionate premium that's not moored in variable costs like inference and compute seems increasingly hard to justify. If your pricing can't survive a conversation with a buyer who's been tinkering in Claude Code for six months, you have a problem.

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Coalesce.io is a launch partner of Snowflake Intelligence ! What does this mean? Semantic layer & context are key for success with Snowflake Intelligence. Without a strong semantic layer you can’t reliably surface the right business meaning, definitions and relationships. The AI will work on your data, but if your models don’t embed business context the answers risk being off. How it works? With Coalesce we deliver two core capabilities that help build Snowflake Semantic Views: • Coalesce Catalog for governance & extraction of BI context: owners, definitions, relationships, tags. • Coalesce Transform for data modelling and transformation: turning raw sources into semantic-ready models that feed business logic This pairing means you give Snowflake Intelligence a well-prepared environment. It give Snowflake Intelligence trusted models, context-rich layers and governance built in, so that when business users ask questions you get meaningful answers, not guesswork.

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The incumbent SaaS vendors who survive will be those who make all of their primitives and data models easily accessible via agents. I want to rip-and-replace vendors that require using lots of in-app UI, low-code, drag-and-drop workflows. I want to keep the vendors that let me work through Claude Code as an interface. Assume non-engineers adoption of Claude Code (and equivalents) will go to 100%. Assume they will be frustrated if they have to click on things. If your product makes users click on things, you're vulnerable. Corollary: as users get Claude Code pilled, they won't automate UIs. They will push work down the stack until they hit something agents can operate on directly with code. Take a risk or finance org: if today's workflow requires dropping a pdf into a Drive, clicking around Google tabs, checking a policy doc, clicking in a Retool, they won't automate those very human steps. They will replace the workflow entirely and redefine it in a lower abstraction layer. PDFs + UIs + click-heavy workflows will move into python, SQL, and APIs.

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