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Rapid PR & Expert Commentary

Data and analytics deserves an expert voice.

Independent news publications cover the stories shaping your industry. Submit your expert commentary and get cited, with first results in minutes.

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Expert commentary moving from a phone to a digital newsroom

Data and analytics PR works best when expertise answers a real reader question. This page brings together news opportunities relevant to analytics platforms and research technology firms. Focus your commentary on data quality, measurement gaps and responsible interpretation. Choose a story below, or submit a brief based on your own informed observations.

News opportunities for your commentary

Published 2026-09-17

AgriERP by Folio3 Named 'AgTech Data Analytics Solution of the Year' in 2026 AgTech Breakthrough Awards

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Reuters reports on this development. Read the source and add your perspective on what it means for your industry.

Consider: What does this development mean for businesses or customers in data and analytics?

Published 2026-09-17

Data Science Is Moving Closer to the Edge: 5 Trends Changing Modern Analytics

Read the story & commentary question

An industry piece outlines five trends pushing analytics toward the edge: edge AI for local inference, real-time streaming for immediate reaction, smaller models to fit constrained hardware, AI-driven data management, and IoT-integrated analytics. The article links these shifts to privacy, 5G connectivity, and use cases across manufacturing, logistics, healthcare, energy and smart cities.

Consider whether latency, privacy, or bandwidth constraints in your use cases make edge analytics or smaller on-device models a practical complement to cloud-based analytics.

Consider: Which of your analytics workloads have latency, bandwidth, or privacy constraints that would justify moving model inference or pre-processing from cloud to edge nodes?

Published 2026-09-16

Veridion lands $20M to take business intelligence beyond static data

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Veridion announced a $20 million Series A to expand its AI-powered business intelligence platform, which creates a continuously updated business graph covering roughly 640 million companies by analyzing digital signals such as websites, registries, filings, product catalogues and social profiles. The firm serves more than 100 organisations and plans to grow its platform and international presence with the new funding.

Consider whether more frequent, signal-driven updates to firmographic and operational data could change how your risk, commercial intelligence, or market-monitoring processes operate.

Consider: What internal processes would need to change for you to trust and operationalize continuously updated business-graph signals alongside your existing BI data sources?

Published 2026-09-15

Pico’s Corvil Analytics Unlocks Trusted Network Data for AI Workflows with 10.2 Software Release

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Yahoo Finance reports on this development. Read the source and add your perspective on what it means for your industry.

Consider: What does this development mean for businesses or customers in data and analytics?

Published 2026-09-14

ER/Studio 21.1 Expands Data Models Into Semantic Layers for AI and Analytics

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The Des Moines Register reports on this development. Read the source and add your perspective on what it means for your industry.

Consider: What does this development mean for businesses or customers in data and analytics?

Evergreen perspective

Data and analytics: practical changes customers should understand

Read the story & commentary question

Share practical expertise grounded in the linked industry sources and your own experience.

Consider: What should customers understand before making an important decision?

Make your expertise useful

Give readers a reason to quote you

Explain what a development means, who it affects and what they should consider next. Your commentary can be recorded or typed, then reviewed before submission.

Who can contribute: Analytics platforms; research technology firms.

An example to adapt

For a story affecting data and analytics, explain one practical consequence connected to data quality. Identify the audience affected, the decision they face and what your professional experience helps them understand. Include a specific example you can substantiate; avoid turning a personal observation into a claim about the entire industry.

Use examples only when they reflect your own informed view. You can also bring your own announcement or news brief.

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