"Enhanced chart validation and dataset filtering in dashboards"

NEW TO DATA? DO THIS BEFORE YOU BUILD ANYTHING Rushing to wire sources and ship dashboards feels productive, but alignment first will save weeks of rework. CHECKLIST - Step 1: Clarify the decision, the stakeholder, and the time window for action - Step 2: Map entities and events; label grain and keys for your core objects - Step 3: Inventory sources, owners, freshness, null rates, joinability, and caveats - Step 4: Define metric specs with formulas, allowed filters, edge cases, and sample rows - Step 5: Prototype with a tiny slice to validate assumptions before building a full DAG - Step 6: Choose the simplest stack that meets latency, cost, and governance needs today - Step 7: Make it reproducible with versioned code, tests for keys/nulls/row counts, and docs - Step 8: Ship, learn, and iterate; review what to refine, discard, and productize BOTTOM LINE - Nail the question first; the stack can wait

Ignite your data. Empower your decisions. Building dashboards shouldn't be a chore - it should be a catalyst for smarter, faster decisions. Athena makes dashboard creation a breeze with zero code, scalable deployment, and powerful customization. Our latest infographic reveals the 6 essential steps to craft dashboards your users actually love: https://buff.ly/fso9IMF #AthenaAnalytics #DashboardDesign #DataDriven #BusinessIntelligence #AnalyticsMadeEasy #DecisionIntelligence #DataForGrowth #DigitalTranformation

🔎 Data scattered across systems, spreadsheets, and platforms makes it hard to find, trust, or use. ER/Studio’s Enterprise Data Catalog addresses this by bringing every model, attribute, glossary term, and lineage path into one governed, searchable hub. ✅ Faster data discovery ✅ Consistent definitions across teams ✅ Built-in governance and auditability Unify your data landscape and empower every user, from architects to analysts, to find, understand, and trust their data. Read the blog 👉 https://lnkd.in/eMXBzyHs #ERStudio #DataCatalog #DataGovernance #MetadataManagement #DataArchitecture

Data chaos is one of the biggest blockers to analytics and governance. I love how this post breaks down how a unified Enterprise Data Catalog brings structure, trust, and speed back to the data landscape. ER/Studio makes it easier for all teams, including business and technical teams, to speak the same data language and move faster with confidence. Worth the read 👇 👉 https://lnkd.in/e3jYAJgH

When connecting to a data source, a language is needed to describe how the data is related and what data is available. A semantic model offers pre-existing business logic, proven through established table relationships, visuals, and reports. It embodies enough business logic to articulate how data should be communicated and aggregated. Leveraging a semantic model capitalizes on pre-packaged business knowledge. #semanticmodel #datarelationships #businesslogic #datacommunication #dataaggregation https://lnkd.in/gMATdEvh

Most dashboards fail because they're built backwards. Teams pick a tool → connect every data source → create 47 charts → nobody uses it. After building production dashboards for dozens of companies, the ones that actually drive decisions follow 3 simple rules: → Start with decisions, not data → Ask "does this change behavior?" before adding a chart → Test with real users before launch We just published the complete 5-step framework including: platform selection, data quality checks, testing protocols, and real enterprise examples. Read it: https://lnkd.in/dvgc8Q64 #DataAnalytics #BusinessIntelligence #Dashboards #DataDriven

📊 Most teams try to fix BI by building another dashboard. The real win comes from engineering fundamentals that make every dashboard trustworthy and fast.   🔍 5 rules we use when we (re)build BI: 🔹 Single Source of Truth – one warehouse, governed semantic layer, shared metric definitions. 🔹Data Contracts – schema + expectations between producers and consumers to stop silent breaks. 🔹Tested Pipelines – CI for data: freshness, nulls, ranges, referential integrity on every load. 🔹Lineage & Ownership – every KPI traceable to tables and owners; fixes go to the source, not the slide. 🔹Performance SLAs – queries under X seconds, with caching and partitions tuned per use case.   Result: fewer dashboards, faster decisions, less arguing over numbers.

Chapter Five: The Transformation What does this trio enable? ➡️ From manual reporting → to automated insight ➡️ From siloed data → to connected intelligence ➡️ From reactive management → to predictive, proactive decisions The result: stronger NOI, happier tenants, and sustainable portfolios. Question for you: Which shift do you think will have the biggest impact on the industry—automation, integration, or foresight? #Yardi #RealEstateTech #DataMigration #SQL #UAEJobs #ApexTigre

📊 #28DaysOfOracle – DAY 21 Key Notes 📒 - Working with presentation variables, prompting views, and passing parameters to enable efficient, dynamic, and context-aware analysis. - Building master-detail relationships, map views, action links, and other interactive analyses to enhance user engagement and data exploration. - Applying inline actions with hierarchy-level data in the catalog to streamline workflows and support deeper, on-demand insights. Today’s session centered on interactivity; turning static dashboards into living, responsive analytics experiences. #OracleAnalytics #OracleCloud #OAC #DataAnalytics #BusinessIntelligence #CloudComputing #DataVisualization #DataModeling #AnalyticsJourney #DigitalTransformation #28DaysOfOracle

The latest Datalogz release introduces a new level of visibility, control, and automation for enterprise BI environments. With a unified view of all BI assets, you can now monitor health, receive real-time alerts, and take action instantly. The new BI Similarity feature identifies and groups look-alike reports and datasets, helping reduce duplication, lower maintenance, and strengthen data trust. Alongside this, an expanded library of prebuilt monitors detects cost spikes, schema drift, performance regressions, and more - allowing proactive governance from day one. Datalogz 2.8 turns BI oversight into a streamlined, intelligent process built for scale and impact. #BIOps #BISimilarity #BISprawl #BusinessIntelligence