Pragmatic Data Governance for Business Value

Didn’t think we’d be as busy as we are in the governance space but it’s exciting to see organizations take a new approach to pragmatic and realistic data governance. Regardless of maturity level or toolset we have helped build new operating models and successfully rollout enterprise governance that sticks. Reach out if you’re facing challenges or are interested in connecting with our team of experts!

Functionally if the business can really solve some growth or margin problem using AI and data to improve execution ability requires the tool to be considered last - a honest look at the problem and what may be broken - Validate with data that it is indeed broken - re arrange plan and execute must be at the core - tool selection to enable being a part of that execution

Why Boards are refocusing on the data operating model in 2026 ... The thing is, Boards are no longer debating platforms. They are debating why outcomes still aren’t landing. In my opinion, these are the questions Boards will be asking in 2026 ... 🔸Accountability “Who actually owns data and AI outcomes?” Ownership is fragmented across tech, risk and business. Committees proliferate, but no one is personally accountable. Boards will push toward a single senior owner with clear decision rights across value, risk and prioritisation. 🔸Value visibility “Why can’t we see value quarter by quarter?” Use cases are approved, but value is not embedded in the management cadence. Boards want portfolio-level value governance wired into quarterly and monthly rhythms, not retrospective reporting. 🔸Scale “Why so many pilots, but scaling fails?” Data is treated as a rollout problem rather than an operating-model redesign. Boards are forcing redesign of the workflows data touches, not just deployment of models. 🔸Defensibility “Is this safe, controlled and explainable?” Governance and controls are often bolted on after deployment. Boards expect human-in-the-loop, oversight and data controls to be designed into day-one operations. 🔸Cost discipline “Why are costs still rising?” Legacy platforms and duplicated data work are rarely stopped. Boards are or will be demanding explicit decommissioning authority and simplification as part of the operating model. ... In 2026, boards will back leaders who can explain the business operating model, not the tech architecture. .

We’re seeing firsthand how data management is shifting from a back-office function to the foundation of AI, which is making real-time insights and strong governance essential. Stale, duplicated, or fragmented data doesn’t just slow reporting; it undermines AI, decision-making, and operational efficiency. Leading organisations are prioritising real-time architectures, curated high-impact datasets, and simplified platforms such as lakehouse and hybrid models that support continuous innovation without creating new complexity. AI performance is only as strong as the data foundation beneath it. Organisations that embed data quality, lineage, and governance into their core architecture are better positioned to scale AI safely and effectively, while those treating data as an afterthought risk compounding errors at speed. In my experience, modernising and simplifying core ERP and data landscapes is often the first step toward making data truly usable, trusted, and AI-ready – turning data from a cost centre into a strategic asset rather than just infrastructure.

AI hallucinations aren't a new problem. They're an old problem in a new dress. Your dashboard has been doing this for years. So has your reporting. So has every analytics layer you've ever built on top of data you didn't fully trust. The outputs were wrong. You just had lower expectations of the tool, so you caught it, corrected it manually, and moved on. Now you've put something smarter on top of the same foundation, and suddenly the failures are visible, confident, and embarrassing. That's not an AI problem. That's a data problem that finally ran out of places to hide. Hallucinations aren't malfunctions. They are accurate outputs from broken inputs. When a model fills a gap with something plausible but wrong, it is doing exactly what it was built to do. The gap was always there. The model just stopped pretending it wasn't. This is why AI adoption is stalling. Not because the tools are failing but because the tools are succeeding. They are reflecting the actual state of the data underneath them, and most organizations aren't ready for that conversation. The companies treating this as a prompt engineering problem are going to keep struggling. The companies treating it as a data architecture problem are going to pull ahead. If your AI is hallucinating, your dashboards were already lying. You just didn't know it yet. If you're a SaaS company watching adoption stall, or a client not seeing the ROI you were promised, the problem is probably not the platform or the tool. Give Veritera a call. veritera.co #DataArchitecture #AIAdoption #DataGovernance #SaaS #LegalTech #DataStrategy #EnterpriseData #AIImplementation #DataFoundations #Veritera

𝗜𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝘆𝗼𝘂, 𝗼𝗿 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗷𝘂𝘀𝘁 𝘀𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝘁? 1️⃣ Business Alignment: Ensuring your data goals match your business objectives. 2️⃣ Maturity Assessment: Knowing where you stand today to plan for tomorrow. 3️⃣ Architecture & Tech: Building a scalable backbone for your data flow. 4️⃣ The Data Team: Having the right experts to manage and interpret insights. 5️⃣ Data Governance: Ensuring security, quality, and compliance. 6️⃣ Roadmap: A clear, step-by-step path to implementation. 7️⃣ Culture Change: Empowering everyone in the org to be data-driven. Don't let your data sit idle. Let’s build a strategy that fuels growth. #DataStrategy #BeyondAI #BigData #BusinessIntelligence #DigitalTransformation #DataGovernance #AI

It started with a simple request: “We need better tools.” But here’s the truth: tools alone weren’t the answer. The real issue? Data maturity. Let’s take a look at the journey: Level 1: Data is everywhere, but it’s scattered. People are stuck doing things manually. Level 2: Dashboards are here, but trust isn’t. The same metric gives different answers, turning meetings into debates. Level 3: Reliable pipelines are now in place, with clear data owners and quality checks running. SLAs are real. Level 4: Teams run on data. Self-serve works. Governance is built-in. And AI scales safely. As maturity grows, so does the ROI. Less chaos. Faster decisions. Lower risk. And finally, the results speak for themselves. #DataMaturity #AnalyticsLeadership #DataCulture #DataTrust #ComplereInfosystem

One of the big mistakes I see organizations pursuing Data & AI ROI make is focusing too much on foundations and not enough on products. Don't get me wrong; foundations are absolutely essential, but often organizations dive into building an expansive data foundation without asking the most important question: What are we building a foundation for? A foundation for a hundred story skyscraper is not the same as a foundation for a single-family home. Organizations that build foundations without asking this question can spend months or years building a foundation that ends up being a poor fit for the products it needs to support. The key to avoiding this: sequence foundations to serve near‑term products. If a foundational component isn’t helping a real product team this quarter (or next), it’s not a foundation, it’s a science project. A few principles I keep coming back to in terms of right-sizing and sequencing foundation builds: 1. Contracts before catalogs. Define the data contract first: owners, SLAs/SLOs, schemas, quality expectations, access patterns, and change management. If you don’t have clarity at the edge, the catalog just documents chaos. 2. Catalogs before committees. Make assets discoverable and usable before you create layers of governance meetings. Let real usage shape what governance needs to be. 3. Observability + FinOps from day one. If you can’t see reliability and cost in production early, you won’t fix it later. Instrument pipelines like products: freshness, completeness, latency, failures, and unit economics. 4. No big‑bang cutovers. Cutovers are where timelines go to die. Reduce risk with parallel runs, incremental migrations, and measured deprecations. 5. Progressive hardening wins. Start with “works and is safe enough,” then harden based on actual load, incidents, and adoption. Reliability is a roadmap, not a launch event. 6. Quarterly product impact is the filter. If a step won’t materially help a product team this quarter or next, question it. The outcome to optimize for: trustworthy data that compounds, without slowing down teams that are trying to ship. #DataLeadership #CDO #DataStrategy #DataGovernance #DataEngineering #Analytics #FinOps #Observability #ProductMindset

🔥 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗶𝘀 𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹𝗹𝘆 𝗼𝘃𝗲𝗿. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆. 🔥 For years, we've watched the same movie play out across organizations: A business stakeholder has a sharp question. A workflow to automate. A process to optimize. They bring it to the data team. The data team is buried. Weeks pass. The moment is gone. That era is ending — and 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗔𝗽𝗽𝘀 and 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗚𝗲𝗻𝗶𝗲 are two of the most underrated catalysts behind the shift. Here's what excites me most: 𝘁𝗵𝗲𝘀𝗲 𝗮𝗿𝗲𝗻'𝘁 𝗴𝗲𝗻𝗲𝗿𝗶𝗰 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗯𝗼𝗹𝘁𝗲𝗱 𝗼𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝘀𝘁𝗮𝗰𝗸. They're 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘶𝘢𝘭. They inherit your governance. They respect your data contracts. They're configured to the language and logic of 𝘺𝘰𝘶𝘳 business processes. That means a supply chain VP can build a live exception-monitoring agent tuned to her team's KPIs. A credit risk manager can deploy a self-service workflow aligned to his exact decisioning framework. A marketing ops lead can query campaign performance in plain English — without waiting in a ticket queue. 𝗕𝗲𝘀𝗽𝗼𝗸𝗲. 𝗚𝗼𝘃𝗲𝗿𝗻𝗲𝗱. 𝗙𝗮𝘀𝘁. The organizations winning the AI race right now aren't the ones with the biggest data teams. They're the ones who've 𝘥𝘦𝘮𝘰𝘤𝘳𝘢𝘵𝘪𝘻𝘦𝘥 the ability to act on data — while keeping governance intact. If you're thinking about your data platform strategy in 2025, this deserves a hard look. 💬 What's your biggest bottleneck when it comes to getting data into the hands of business stakeholders? Drop it below — curious to hear what the community is seeing. #DataStrategy #Databricks #AIAgents #DataMesh #ModernDataStack #EnterpriseAI #DigitalTransformation #DataGovernance #LakehouseArchitecture #Deloitte