Data trust strategies for large institutions

🚨Incoming: The Federal Zero Trust Data Security Guide Fresh off the presses - In alignment with M-22-09, the Federal CDO Council and Federal CISO Council gathered a cross-agency team of data and security specialists to develop a comprehensive data security guide for Federal agencies. Representatives from over 30 Federal agencies and departments worked together to produce the Federal Zero Trust Data Security Guide, which: 🔹Establishes the vision and core principles for ZT data security 🔹Details methods to locate, identify, and categorize data with clear, actionable criteria 🔹Enhances data protection through targeted security monitoring and control strategies 🔹Equips practitioners with adaptable best practices to align with their agency’s unique mission requirements Securing the data pillar in Zero Trust has been a challenging endeavor, but it’s foundational to a resilient cybersecurity posture. This guide lays out essential principles and a roadmap to embed security at the core of data management beyond traditional perimeters. Here are a few key takeaways: 🔐 Core ZT Principles: Adopting a data-centric approach with strict access controls, data resiliency, and integration of privacy and compliance from day one. 📊 Data Inventory and Classification: It is crucial to understand the data landscape, and the guide provides insights into cataloging and labeling sensitive data for targeted protection. 🤝 Managing Third-Party Risks: From privacy-preserving technologies to detailed vendor assessments, agencies can better secure shared data and protect it from supply chain threats. I had the privilege of attending a couple of these Working Group meetings before leaving CISA earlier this year, and I congratulate the group on this necessary release. This guide aligns closely with CISA's Zero Trust Maturity Model, providing agencies with a robust framework to secure federal data assets and advance a strong, data-centric ZT security model. #data #zerotust #cybersecurity #technology #informationsecurity #computersecurity #datascience #artificialintelligence #digitaltransformation #bigdata 

📌 The Modern Data Quality Framework for BI Every company wants better dashboards, better insights, better AI. But very few stop to ask the one question that actually matters: Can we trust the data we’re using in the first place? Because the hard truth is this: Most data issues don’t come from tools. They come from unreliable foundations that nobody notices until something breaks in production. When I look at the teams that consistently ship trustworthy data, there’s always the same pattern behind the scenes. Let me walk you through my reasoning. 1️⃣ 𝐓𝐡𝐞 5 𝐏𝐢𝐥𝐥𝐚𝐫𝐬 𝐀𝐫𝐞 𝐒𝐭𝐢𝐥𝐥 𝐭𝐡𝐞 𝐒𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐏𝐨𝐢𝐧𝐭 Accuracy, completeness, consistency, timeliness, and validity. We all know them. But most teams still treat these as “definitions.” On the other hand, the best teams treat them as operational targets. It’s a completely different mindset. Accuracy isn’t “nice to have.” It’s whether your revenue aligns with reality. Completeness isn’t a rule. It’s whether you trust the KPI enough to act on it. Everything changes once you start thinking this way. 2️⃣ 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐂𝐡𝐞𝐜𝐤𝐬 𝐌𝐚𝐤𝐞 𝐨𝐫 𝐁𝐫𝐞𝐚𝐤 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 This is where issues hide. I can’t count the number of times I’ve seen dashboards fail not because the model was wrong but because nobody noticed: → A column changed type → A pipeline skipped 2% of rows → A source table silently dropped a field → A null explosion went undetected for weeks This layer is invisible to most of the business, yet it’s the one that protects trust. If you don’t have anomaly detection or CI/CD tests, you’re relying on luck. And luck is not a data strategy. 3️⃣ 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐌𝐚𝐤𝐞𝐬 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐖𝐨𝐫𝐤 Data catalogs, lineage, ownership, contracts. People talk about them like buzzwords, but the impact is very real. Lineage isn’t a diagram. It’s how you debug issues in minutes instead of days. Contracts aren’t bureaucracy. They’re how producers guarantee stability for downstream teams. Stewardship isn’t a title. It’s accountability. What I’ve learned from my experience is simple: When governance is strong, you don’t spend your life firefighting. 4️⃣ 𝐀𝐭 𝐭𝐡𝐞 𝐂𝐞𝐧𝐭𝐞𝐫 𝐨𝐟 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠: 𝐃𝐚𝐭𝐚 𝐓𝐫𝐮𝐬𝐭 This is the part people underestimate. Trust is not something you “announce” on a slide. It’s something you earn, build, and protect over time. It shows up in adoption. It shows up in business confidence. It shows up in how quickly you can respond when an anomaly hits. Trust is the real KPI. And when it’s strong, everything else becomes easier. Executives stop asking "where did this number come from." Why does this matter so much? Because a lot of companies are scaling GenAI without first fixing data quality. And when AI learns from unreliable data, it becomes unreliable itself. If you want to improve decision-making, data quality is not a side topic. Everything else is built on top of it.

𝗙𝗶𝘅 𝘁𝗿𝘂𝘀𝘁 𝗳𝗶𝗿𝘀𝘁, 𝗻𝗼𝘁 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲. A new Head of Data walks in. 𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝟵𝟬 𝗱𝗮𝘆𝘀 𝗮𝗿𝗲 𝗮 𝘁𝗲𝘀𝘁. Many start with dashboards, pipelines, and plans. They rebuild what’s broken and expect trust to follow. 𝗕𝘂𝘁, 𝗺𝗼𝘀𝘁 𝗳𝗮𝗶𝗹. They forget that trust, not tools, is the real foundation. You can fix every schema and still have leaders asking, “Why are we still in this mess?” 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗣𝗵𝗮𝘀𝗲 𝟭: 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲, 𝗗𝗼𝗻’𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿. Meet every key person. Ask what data they trust. Listen to real pain, not just reports. Find your “data superusers.” See where data dies before it reaches the decision. 𝗣𝗵𝗮𝘀𝗲 𝟮: 𝗔𝗹𝗶𝗴𝗻 𝗮𝗻𝗱 𝗗𝗲𝘀𝗶𝗴𝗻. Prioritize quick wins. Rank by impact, complexity, reach, and risk. Set clear ownership for metrics. Share updates every week. 𝗣𝗵𝗮𝘀𝗲 𝟯: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗣𝗿𝗼𝗼𝗳, 𝗡𝗼𝘁 𝗣𝗿𝗼𝗺𝗶𝘀𝗲𝘀. Pick the highest priority. Deliver one visible win in 30-45 days. Align on definitions so everyone speaks the same language. Over communicate wins and issues. 𝗔𝘃𝗼𝗶𝗱 𝘁𝗵𝗲𝘀𝗲 𝘁𝗿𝗮𝗽𝘀: • Don’t rush to buy new tools. • Don’t rebuild dashboards before fixing trust. • Don’t promise AI if you have ten definitions of revenue. The first 90 days decide if data drives growth or stays a reporting chore. 𝗜𝗳 𝘆𝗼𝘂𝗿 𝗖𝗙𝗢 𝘀𝘁𝗶𝗹𝗹 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗯𝘆 𝗗𝗮𝘆 𝟵𝟬, 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. Trust comes first. Visible wins come next. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝘀𝘁𝗼𝗽 𝗯𝗲𝗶𝗻𝗴 “𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗽𝗲𝗿𝘀𝗼𝗻” 𝗮𝗻𝗱 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘄𝗵𝗼 𝗺𝗮𝗸𝗲𝘀 𝗱𝗮𝘁𝗮 𝘄𝗼𝗿𝗸. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘁𝗲𝗮𝗺𝘀?

Errors kill trust. Your insights are a liability the moment your logic fails, regardless of how beautiful your charts look. Most analysts work in a silo. We prioritize speed and (falsely) believe that professionalism means producing bulletproof work in isolation. This is the hallmark of an amateur analyst, not the grizzled pro who has lived through the realities of how difficult this work is (with the battle scars to prove it). Accuracy is not a nice-to-have. It is everything. Institutionalize your quality assurance by borrowing the approach consultants use: The Blue Team Review. Here is how to adopt the Blue Team protocol for yourself: 1. Recruit the Blue Team: Assemble 3-4 peers—a technical lead, a domain expert, and a couple of total outsiders. Diverse eyes catch the "unknown unknowns" you’ve grown blind to. 2. Review Your Work: Walk through the presentation you've planned for your stakeholders, not as a "dress rehearsal" but rather as a "look behind the scenes." Give them the context, tell them the high-level story, then show them how you got there. 3. Attack the Assumptions: Direct your Blue Team to be adversarial. Force them to question every assumption you've made. If they can poke holes in your narrative, your VP definitely will. The first question isn't, "Is this pretty?" It is, "Does this achieve what our stakeholder was promised?" 4. Own the Final Call: The Blue Team provides suggestions, not mandates. You retain the discretion to accept or reject feedback, keeping your defenses low and your focus on the product. The embarrassment of a peer finding a mistake is temporary. The damage of a stakeholder finding a mistake is permanent. #DataQuality #Analytics #BusinessIntelligence #DataStrategy #BlueTeaming Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

Safeguarding information while enabling collaboration requires methods that respect privacy, ensure accuracy, and sustain trust. Privacy-Enhancing Technologies create conditions where data becomes useful without being exposed, aligning innovation with responsibility. When companies exchange sensitive information, the tension between insight and confidentiality becomes evident. Cryptographic PETs apply advanced encryption that allows data to be analyzed securely, while distributed approaches such as federated learning ensure that knowledge can be shared without revealing raw information. The practical benefits are visible in sectors such as banking, healthcare, supply chains, and retail, where secure sharing strengthens operational efficiency and trust. At the same time, adoption requires balancing privacy, accuracy, performance, and costs, which makes strategic choices essential. A thoughtful approach begins with mapping sensitive data, selecting the appropriate PETs, and aligning them with governance and compliance frameworks. This is where technological innovation meets organizational responsibility, creating the foundation for trusted collaboration. #PrivacyEnhancingTechnologies #DataSharing #DigitalTrust #Cybersecurity

Everyone at Davos is talking about AI. Cool. But the bigger story in 2026 is uglier (and more useful): mis/disinformation and societal polarization are now top-tier global risks. WEF is basically saying this out loud: information disorder is now a board-level risk. Which means one thing for leaders: Trust is no longer a brand problem. Trust is an operating model. Here’s the uncomfortable translation: If you can’t answer these in 30 seconds, you don’t have “data.” You have vibes: • Where did this claim come from? • What changed since last week? • Who owns it? • Who can use it, and why? • Can we prove it on demand? This is why “better dashboards” won’t save you in 2026. The winners will be the companies that treat trust like reliability: • SLAs for critical metrics • versioned definitions (what is revenue today) • policy enforcement you can measure • lineage you can show, not narrate Hot take: The most important KPI for 2026 isn’t model accuracy. It’s trust latency. How fast your org can go from claim to source to proof. If you had to pick ONE Trust KPI for your org this year, what’s the most honest? A) Percent of critical metrics with end-to-end lineage B) Policy enforcement rate (access, masking, retention) C) Time-to-proof (claim to evidence) D) Data product SLA breach rate Drop your letter and why. #DataTrust #DataGovernance #DigitalTrust #ResponsibleAI #Davos

How can Data Privacy become your Strategic Asset of enabling high value business outcomes? In 2026, data privacy has evolved from a regulatory "cost of doing business" to a fundamental driver of customer trust and operational resilience. For financial institutions, the stakes have never been higher, with regulatory penalties for data governance failures exceeding $3.6 billion annually. Key Insights for Leadership: The ROPA Advantage: I find that leveraging the Record of Processing Activities (ROPA) as a living blueprint to identify hidden risks across legacy systems and complex data flows. This data mapping and discovery exercise must be conducted across high value asset workstreams and functions across an enterprise (no matter the size) to include HR, Finance, Legal, Privacy, Ethics & Compliance, Information Security, IT, Marketing, Sales, Supply Chain, Operations, Business Groups that interface with day-to-day customers/clients, Environment Health, Safety and Sustainability. DPIA Integration: Utilizing ROPA to streamline Data Protection Impact Assessments (DPIAs), transforming a mandatory hurdle into a high-speed diagnostic tool for new AI and fintech deployments. DPIAs tell you exactly what the impact maybe for data exposure and then enable teams to plan for appropriate data security controls to protect sensitive and personal data. Mitigating Third-Party Risk: Addressing the vulnerabilities of a sprawling vendor ecosystem—a critical lesson learned from recent high-profile industry breaches. The Governance Shift: Adopting modern compliance frameworks like SOC2, ISO, NIST CSF 2.0 to align technical fortifications (Zero Trust, MFA) with overarching business strategy. The Bottom Line: Financial institutions that prioritize privacy by design, DPIA, ROPA and align these frameworks to appropriate set of compliance controls, don't just avoid fines—they secure a competitive advantage in a digital-first economy. This article outlines a practical roadmap for leadership to move beyond reactive compliance and build a proactive, privacy-first culture.

Data governance is crucial for developing AI systems that are reliable, compliant, and ethically sound. Without clearly defined policies, roles, and controls, organizations face risks such as poor model outcomes, regulatory penalties, and reputational damage, especially in regulated sectors with stricter data sovereignty and cross-border regulations. The fundamentals of successful data governance include: - Aligning data strategy with business outcomes by prioritizing datasets for high-value, low-risk AI use cases. - Securing executive sponsorship; a governance charter and visible leadership can help eliminate obstacles. - Defining roles and implementing controls by assigning owners, stewards, and custodians, while automating lineage and quality checks. - Mitigating top risks by managing shadow AI through approval workflows, enforcing data quality gates, and mapping data flows for sovereignty and compliance. - Measuring and iterating using maturity assessments, KPIs, audits, and training to maintain progress. Why it matters: Data governance transforms raw data into a governed asset, ensuring quality, lineage, access controls, and policy enforcement so models learn from accurate, auditable inputs rather than noise. Effective governance aligns technical controls with legal and compliance requirements (privacy, sovereignty, sector rules), reducing regulatory risk while fostering safe innovation. The business impact of robust governance includes diminished compliance risk, accelerated safe AI adoption, and establishing your firm's trust ability, particularly in regulated industries. Practical next steps include: - Defining your top 3 AI use cases and required datasets. - Conducting a 2-week maturity assessment and developing a 90-day roadmap. - Establishing an executive sponsor and governance charter. - Assigning data owners and stewards; implementing lineage and quality checks. - Mapping data flows for sovereignty and compliance; adjusting architecture as necessary. Sources: DATAVERSITY; Kyndryl; Forbes.

Everyone says they want “better data.” What they usually mean is: cleaner data. Clean data matters. But it’s not the finish line. You can scrub every field and still have leaders (and investors) quietly thinking: “Do we actually believe these numbers?” That’s why data trust is a talent strategy, not just a tooling project. Investors don’t underwrite your dashboards. They underwrite decision-grade truth: metrics that hold up in a board meeting, during diligence, and in a bad quarter—without a 30-minute argument over definitions. The difference is leadership. Specifically, who owns the truth: Operator — creates a real operating cadence, forces consistent definitions, and won’t let meetings turn into a debate club. Commercial Leader — protects revenue truth (pipeline, retention, pricing reality) and shuts down “spreadsheet optimism” early. Governance-minded Leader — assigns metric owners, locks definitions, and makes it hard to game the system without slowing the business down. Quick interview test: “Show me the 5 numbers you’d demand in your first 30 days—and what decisions each will unlock.” If you’re scaling right now: which metric do you least trust today?