Systems | Development | Analytics | API | Testing

thoughtspot

Why Your Customers Hate Your Analytics (and What To Do About It)

Jul 7, 2026 By Team ThoughtSpot In ThoughtSpot
Monthly active user rates stuck at 23%. A Slack message about another client who can't find the data they need. A support ticket your team has started to joke about: “Can you export this to Excel?” Once upon a time, embedding dashboards inside your product was a differentiator. Today, this is what the feedback looks like when your analytics stop working. AI agents can now respond to complex questions with meaningful insights in seconds.
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xray

How SoftComply and Xray Enable Risk-Based Testing in Jira Cloud

Jul 7, 2026 By Mariana Santos In Xray
For organizations developing regulated products, risk management and testing are closely connected. Every identified risk must be assessed, mitigated, verified, and documented to support compliance requirements and release decisions. Yet these activities often happen in separate systems. Risk assessments may live in dedicated risk management tools or spreadsheets, and testing is usually managed elsewhere.
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kong

Shadow AI Detection: The Enterprise Governance Guide

Jul 7, 2026 By Kong In Kong
Shadow AI detection is the practice of finding and governing unsanctioned AI tools, models, and API integrations that employees deploy without security approval. It has become urgent because these tools route live enterprise data to external models in real time, and traditional security stacks cannot see them. The 2026 Cordyceps disclosure, which exposed identical AI-generated vulnerabilities across 300+ GitHub repositories, showed how fast ungoverned AI can turn into a supply-chain crisis.
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The Future of Data Engineering & AI with Henry Clavo

Jul 7, 2026 By Hevo Data In Hevo
In this episode of Data Builders Club, Henry Clavo shares lessons from over a decade in data engineering across healthcare and government, exploring what it really takes to build reliable data systems in the age of AI. From ETL best practices and data quality to AI hallucinations, observability, and the future of data engineering careers, this conversation is packed with practical insights for modern data teams.
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Real-Time AI: How to Move & Process Data Anywhere with Cloudera

Jul 7, 2026 By Cloudera, Inc. In Cloudera
Unlock the full potential of your data fabric and accelerate your AI journey with Cloudera Data in Motion. Many organizations struggle with massive amounts of diverse data spread across different formats, vendors, and locations—whether in the cloud or on-premises data centers. Cloudera provides the scalable, performant data services needed to move and process this information in real-time.
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Unified Data Governance for Safe & Trusted AI Agents

Jul 7, 2026 By Cloudera, Inc. In Cloudera
Hey, did you know your AI agents could be making decisions based on data they were never meant to see? When enterprise data governance is fragmented across separate tools, it creates severe blind spots. Rogue AI agents can over-index, modify, or even accidentally delete production databases simply because proper data guardrails weren't uniformly enforced. In this video, we tackle the root cause of why 79% of enterprise AI initiatives stall and show you how to build a unified data fabric that secures your hybrid estate.
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appian

Solving Agent Sprawl: Why AI Agents Need an Operational Context Layer

Jul 7, 2026 By Donna Namorato In Appian
Since its inception, agentic AI has felt like a distant aspiration. Today, agents are here, and enterprise adoption is accelerating. Gartner predicts that by 2028, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, up from fewer than 15 in 2025. Agents arrive with incredible, broad intelligence, but lack the knowledge of your operating model: your customers, policies, approvals, exceptions, business rules, systems, and operational history.
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appian

Automating the Exception: How a Second LLM Judge Drives Straight-Through Processing

Jul 7, 2026 By Yin He In Appian
Document-centric workflows have been difficult to automate and required human intervention. Attempts to automate document handling often failed or did not scale, because legacy intelligent document processing (IDP) systems were fragile. They often required manually retraining models on dozens of documents just to identify specific fields—only to repeat the process whenever a format changes. The result was a costly cycle of maintenance and manual data entry.
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katalon

How to Build a Scalable Enterprise Testing Strategy for Engineering Teams

Jul 7, 2026 By Arvind Kumar Choudhary In Katalon
Enterprise software today isn't just complex, it's mission-critical. A single production issue can disrupt operations, impact revenue, and erode customer trust overnight. Yet despite years of investment in enterprise test automation and growing QA headcount, many organizations still ship broken software and miss release windows. The uncomfortable truth? Enterprise software doesn't fail because teams aren't testing enough.
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integrate

How to Consolidate Multi-Bank Transaction Data With Low-Code ETL

Jul 7, 2026 By Donal Tobin In Integrate
Every finance team managing multiple banking relationships knows the pain: downloading statements from six different portals, copying transaction data into spreadsheets, and spending hours reconciling figures that should match but don't always align. With businesses losing significant productivity due to manual data handling and delayed system synchronization, multi-bank data consolidation has become a critical operational challenge.
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