Mansoor Ahmed

What is Direct Lake mode in Power BI with Fabric? In Microsoft Fabric, Direct Lake mode is a storage mode for Power BI semantic models that enables direct, on-demand access to data stored in OneLake's Delta tables without importing the data or using intermediate SQL queries, combining the high performance of Import mode with the real-time freshness of DirectQuery for efficient analysis of large datasets. It eliminates data duplication by keeping everything in OneLake, supports automatic metadata syncing, and is ideal for Lakehouse-based workloads where data remains in its native Parquet format for seamless integration across Fabric tools. Direct Lake mode works by loading specific columns or data segments into memory (via a process called paging and transcoding) only when queried in reports, caching frequently accessed "warm" columns for faster subsequent access while falling back to DirectQuery if memory limits are exceeded or for uncached data. You enable it by creating or connecting to a semantic model in Power BI Desktop or the Fabric portal using the OneLake catalog, with features like automatic framing (metadata refresh) to keep the model in sync with Lakehouse changes, and options for pre-warming caches or manual refreshes via APIs for optimized performance. The point is that when building reports in Power BI with Fabric, Direct Lake mode lets you query massive datasets efficiently in real-time without refreshes or copies, making data consumable by BI tools or apps in a scalable and performant way

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The Role of Analytics Centers of Excellence (CoE) in CAS. Client Advisory Services (CAS) is evolving. From compliance execution to strategic advisory. The differentiator? An Analytics Center of Excellence. An Analytics CoE within CAS enables firms to: • Standardize KPI frameworks across industries • Automate reporting and reduce manual dependency • Build reusable data models for AR, AP, cash flow, and working capital • Enhance advisory depth with predictive insights • Improve margin through scalable analytics assets Instead of every engagement starting from scratch, a CoE institutionalizes data capability i.e. tools, templates, governance and talent. Firms that embed analytics structurally and not optionally will own the next decade of advisory. #CAS #Analytics #DataDrivenFinance #Advisory #CenterOfExcellence #DigitalCPA #CPA

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Great illustration and well used-analogy - "Data Management is the engine; Data Governance is the GPS." #beyondthedata #beonthedata #datagovernance #datamanagement

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Data governance doesn’t fail because it’s complex. It fails because teams try to make it perfect before making it useful. For a long time, governance was treated as a documentation exercise. In reality, effective governance starts much smaller. Most organizations delay governance because they believe they need: ● A formal committee structure ● Enterprise-wide alignment ● Months of planning and frameworks But the reality inside most teams looks different. Limited time. Limited headcount. Budgets already committed elsewhere. So governance keeps getting postponed — while teams continue debating which revenue number is correct. Here’s a practical version that actually works for busy data teams: Set up lean governance in minutes, not months. ● Choose one recurring data problem ● Focus on one domain (Sales, Finance, or Product) ● Define 3–10 critical metrics Then do the essentials: ● Assign a clear owner ● Agree on simple metric definitions ● Document decisions briefly and visibly No immediate need for: ✗ Governance councils ✗ Heavy frameworks ✗ Organization-wide rollouts Instead, implement: ● 30-minute weekly data office hours ● One-page documentation people will actually read ● Simple access rules (default: least privilege) What success starts to look like: ● Fewer “which number is right?” conversations ● Faster and clearer data access ● More stable and trusted dashboards Data governance isn’t about perfection. It’s about creating predictable foundations that teams can rely on. Start small. Deliver value. Expand intentionally — quarter by quarter CC: Marcel Dybalski

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Muhammed Mokhtar stands as a transformative force in enterprise architecture. As Advisory Chief Architect (webMethods & StreamSets) - Middle East & Türkiye at IBM, he is at the forefront of advancing the company’s Automation, Sustainability, Data, and AI portfolios. Mokhtar plays a pivotal role in enabling customers to turn complexity into competitive advantage through next-generation integration, generative AI, data engineering, and digital architecture strategies. His work is instrumental in guiding strategic customers toward future-proof architectures, ensuring seamless alignment between business value, modern data capabilities, and AI-powered innovation. With years of experience across IBM and Software AG, Mokhtar has built a reputation as a trusted authority in enterprise solution design, digital transformation, and large-scale integration programs. Technical and consultative selling with architecture excellence are Mokhtar's main focus areas. He has been supporting clients, globally, to articulate their emerging initiatives, utilizing a sheer positioning of diverse landscapes for Digital Experience Management, Hybrid Integration, API Management, SOA, Microservices, Business Process Analysis, RPA, BigData, Complex Event Processing, IoT, Distributed Ledger Technologies, and Cloud / Edge Computing etc. Through his leadership, deep technical insight, and relentless commitment to innovation, Muhammed Mokhtar continues to influence the regional technology landscape and champion the future of AI-led digital transformation. #AILeadership #DigitalTransformation #AIInnovation #FutureOfTech #TechLeadership #DataDrivenStrategy #IntelligentAutomation #AIRevolution #EnterpriseAI #TechVisionary #AIImpact #SmartSolutions #NextGenTechnology #AIMena

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Behind every Data Governance program is a network of data stewards who turn governance policies into practice. Data Stewardship is not just about assigning roles but about execution. Here are the core Data Stewardship processes that drive good management practices: ➡️ Data Quality Monitoring & Collaboration Stewards ensure data meets expectations by: ➖Validating quality rules ➖Investigating anomalies and exceptions ➖Working with business and technical stakeholders to address defects ➡️ Metadata Stewardship They make data understandable and accessible by: ➖Managing business definitions and glossaries ➖Classifying data (PII, PCI, etc.) ➖Cataloging assets and documenting lineage ➡️ Policy Implementation Support Stewards translate governance policies into action by: ➖Promoting policy compliance ➖Implementing guidance and guardrails ➖Monitoring usage patterns and supporting corrective actions ➡️ Issue Management & Resolution They keep data operations running by: ➖Logging and tracking data issues ➖Conducting root cause analysis ➖Driving resolution workflows and escalations ➖ Coordinating across teams for timely fixes ➡️ User Support & Data Literacy Coaching Stewards empower data users by: ➖Answering questions and providing data guidance ➖Sharing best practices ➖Championing data literacy training across the org ❓Did I miss any other core Data Stewardship processes?

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Data catalogs and MDM platforms are on a collision course. Why? Success in data governance - and management - requires the ability to: 1) Discover and classify your data 2) Define data governance policies for your data 3) Implement and enforce those governance policies across all enterprise use cases, both operational and analytical. The biggest limitation of data catalogs (aka 'metadata mgt') tools has always been that they are essentially silos, largely disconnected from every other mission critical software platform in the organization. You could discover your data and define governance policies for it, but you wouldn't have the ability to enforce those policies in either the analytical or operational systems consuming it. Or put another way, you can manage your metadata in a catalog, but your ability to activate that metadata it is dependent on some serious effort to integrate your catalog to downstream systems. This is where MDM software comes into the picture. As a discipline, MDM has always been about enforcing governance policies in both operational *and* analytical systems for the data that matters the most to your organization. Rules are defined to allow you to identify errors or inconsistencies in your mission critical metadata (customers, products, assets, etc.), and MDM platforms take the steps needed to rectify them in accordance with your policies. And when the system can't (or shouldn't) automatically enforce those rules, data stewardship workflows are triggered to put a 'human in the loop'. This is all enabled through integrations MDM hubs create with downstream consumers of data across the enterprise. This means that MDM systems play a critical role to allow for metadata being managed in a data catalog to be both governed, 𝐚𝐧𝐝 𝐚𝐜𝐭𝐢𝐯𝐚𝐭𝐞𝐝, at scale. This is my way of saying I think it's somewhat inevitable that the barrier between MDM platforms and Data Catalogs will slowly dissolve over time. This will happen through partnerships (like the one Profisee has with Microsoft for their integration with Purview) or it will happen through catalog providers integrating MDM capabilities directly into their platforms. The convergence of data catalogs and MDM isn’t just about tighter integration - it’s about enabling a new capability in the modern data stack. Some (like yours truly) would call this a next-generation semantic layer. I've heard others refer to this as a context layer. Whatever the label, the idea is the same: we need systems that don’t just document data, but ensure that its meaning and quality are consistently applied wherever it flows. It's a powerful combination. What do you think? Do you agree, or disagree? If so, why? #datacatalogs #masterdata #masterdatamanagement

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