{"id":17274,"date":"2026-06-24T11:04:39","date_gmt":"2026-06-24T09:04:39","guid":{"rendered":"https:\/\/agcommtech.de\/2026\/06\/24\/session-recap-data-insights-insights-amid-the-digital-noise\/"},"modified":"2026-07-16T11:08:39","modified_gmt":"2026-07-16T09:08:39","slug":"session-recap-data-insights-insights-amid-the-digital-noise","status":"publish","type":"post","link":"https:\/\/agcommtech.de\/en\/2026\/06\/24\/session-recap-data-insights-insights-amid-the-digital-noise\/","title":{"rendered":"Session Recap: Data &#038; Insights\u2014Insights Amid the Digital Noise"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong>Session Recap: Data &amp; Insights\u2014Insights Amid the Digital Noise<\/strong><\/h2>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/agcommtech.de\/wp-content\/uploads\/2026\/05\/IG-Data-und-Insights-Vorschaubild-1024x559.jpeg\" alt=\"\" class=\"wp-image-16719\" srcset=\"https:\/\/agcommtech.de\/wp-content\/uploads\/2026\/05\/IG-Data-und-Insights-Vorschaubild-1024x559.jpeg 1024w, https:\/\/agcommtech.de\/wp-content\/uploads\/2026\/05\/IG-Data-und-Insights-Vorschaubild-300x164.jpeg 300w, https:\/\/agcommtech.de\/wp-content\/uploads\/2026\/05\/IG-Data-und-Insights-Vorschaubild-768x419.jpeg 768w, https:\/\/agcommtech.de\/wp-content\/uploads\/2026\/05\/IG-Data-und-Insights-Vorschaubild-1536x838.jpeg 1536w, https:\/\/agcommtech.de\/wp-content\/uploads\/2026\/05\/IG-Data-und-Insights-Vorschaubild-2048x1117.jpeg 2048w, https:\/\/agcommtech.de\/wp-content\/uploads\/2026\/05\/IG-Data-und-Insights-Vorschaubild-900x491.jpeg 900w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div>\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<h3 class=\"wp-block-heading\"><strong>When We Measure What We Don&#8217;t Even Want to Measure<\/strong><\/h3>\n\n<p class=\"wp-block-paragraph\">During our June session, Dr. Marc Jungblut (LMU Munich, IfKW) dissected a core problem in the discipline: We make decisions based on a systematically biased data foundation. His analogy: Data is not gold, but ore\u2014worthless without processing. Two fallacies were the focus of the discussion.  <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>1. Audience: The digital sentiment landscape is not the same as public opinion.<\/strong> <\/h3>\n\n<p class=\"wp-block-paragraph\">Those who speak up are not representative. The likelihood of posting depends on status, personality, involvement, and culture; thus, an engagement rate measures only those who are already active. Furthermore, each platform represents its own sub-public. Negative content generates more engagement and is amplified by algorithms, while quiet majorities remain underrepresented. Common metrics weight every voice equally, even though opinion leaders dominate (example: a post only goes viral after Musk comments on it and CNN picks it up). Add to this the problem of authenticity: trolls, bought engagement, agency-to-agency communication, and\u2014increasingly\u2014AI-to-AI communication create false resonance. Thesis: Online publics are a sea of noise in which signals must be identified.      <\/p>\n\n<h3 class=\"wp-block-heading\"><strong>2. Metrics: We rarely measure what we think we&#8217;re measuring.<\/strong> <\/h3>\n\n<p class=\"wp-block-paragraph\">The scientific link between engagement and purchase intent is weak. The key point: <strong>we don\u2019t really measure sentiment.<\/strong> The approach was designed for distinct emotions, not for simple positive-negative valence. Tools fail when it comes to irony; even AI only achieves correlations of 0.6 to 0.77 with human judgments, which are themselves inconsistent. Sentiment depends on the topic and platform, and negative sentiment does not necessarily equate to a negative impact: The award-winning Edeka Christmas commercial evokes emotion, which the tool\u2019s logic classifies as negative. Especially on social media, what\u2019s often measured is an artifact. Add to that the shift from SEO to GAIO\/GEO: a stable ranking turns into a black box of personalized responses with no access to ground truth.     <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>Solution and Message.<\/strong>  Data donations open up this \u201cblack box\u201d in a GDPR-compliant way from the user\u2019s perspective, but they struggle with self-selection (participation rates around 20 percent). Jungblut\u2019s recommendation: focus not on how many people interact, but on who does; question metrics; combine methods\u2014surveys aren\u2019t dead. The bottom line: Without data literacy, we\u2019re measuring noise instead of the signal. The key lies with the people who know what a number means\u2014and what it doesn\u2019t.   <\/p>\n\n<p class=\"wp-block-paragraph\"><strong>From the discussion.<\/strong>  The \u201cground truth\u201d question struck a nerve; many no longer trust their human-annotated datasets. On the perennial topic of brand values, the unanimous sentiment was: difficult to measure, often dismissed. The consensus: take a more qualitative approach again. A session with fewer ready-made answers and better questions. That is precisely the foundation of a genuine data culture.    <\/p>\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100\"><a class=\"wp-block-button__link has-background wp-element-button\" href=\"https:\/\/agcommtech.de\/en\/engine-room\/\" style=\"border-radius:0px;background-color:#009535\"><strong>READ MORE ARTICLES<\/strong><\/a><\/div>\n<\/div>\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-group has-background\" style=\"background-color:#274856\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h4 class=\"wp-block-heading has-text-align-center has-white-color has-text-color has-link-color wp-elements-01e16cc0ce204735756af7591da657be\"><strong>Become a member<\/strong><\/h4>\n\n\n\n<p class=\"has-text-align-center has-white-color has-text-color has-link-color wp-elements-659baeff9d64c39448e553da35291648 wp-block-paragraph\" style=\"font-size:15px\">Find out about the opportunities to become a member of the AG CommTech<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-fe48e5de wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-50\"><a class=\"wp-block-button__link has-text-color has-background has-link-color has-text-align-center wp-element-button\" href=\"https:\/\/agcommtech.de\/en\/become-a-member-of-the-commtech-ag\/\" style=\"border-top-left-radius:32px;border-top-right-radius:32px;border-bottom-left-radius:32px;border-bottom-right-radius:32px;color:#4d4a4a;background-color:#dfdfdf\"><strong>Learn more now<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n<\/div><\/div>\n<\/div><\/div>\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>During our June session, Dr. Marc Jungblut (LMU Munich, IfKW) dissected a core problem in the discipline: We make decisions based on a systematically biased data foundation. His analogy: Data is not gold, but ore\u2014worthless without processing. Two fallacies were the focus of the discussion.  <\/p>\n","protected":false},"author":2,"featured_media":16720,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[151],"tags":[161],"ppma_author":[123],"class_list":["post-17274","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-room","tag-data-insights-experts"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Session Recap: Data &amp; Insights\u2014Insights Amid the Digital Noise - AG CommTech<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/agcommtech.de\/en\/2026\/06\/24\/session-recap-data-insights-insights-amid-the-digital-noise\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Session Recap: Data &amp; Insights\u2014Insights Amid the Digital Noise - AG CommTech\" \/>\n<meta property=\"og:description\" content=\"During our June session, Dr. Marc Jungblut (LMU Munich, IfKW) dissected a core problem in the discipline: We make decisions based on a systematically biased data foundation. 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