Brand Leadership Community jomar@brandleadership.community https://blc.twentythree.com en-us Visualplatform http://blogs.law.harvard.edu/tech/rss Brand Leadership Community episodic no https://blc.twentythree.com/files/rv1.51/sitelogo.gif https://blc.twentythree.com http://blc.twentythree.com/analytics-in-the-age-of-ai-why <p><h2>When answers become cheap, expertise becomes more important</h2><p>The first episode of the Analytics DevNet Webcast tackles one of the biggest questions facing analytics professionals today: <strong>what happens to expertise when AI can increasingly generate code, analysis and answers almost instantly?</strong></p><p>With analytics specialist and educator Simo Ahava joining Gunnar Griese, Steen Rasmussen and Jomar Reyes, the conversation moves beyond simple predictions about whether AI will replace analysts. Instead, it examines what practitioners need to understand if they are going to use AI responsibly and remain valuable as the technology evolves.</p><p>A central concern is the growing temptation to skip the difficult parts of learning. AI can already produce JavaScript, SQL, tagging configurations and explanations that previously required technical expertise. Simo acknowledges the productivity benefits, but argues that manually working through problems builds the mental models required to recognise when something is wrong.</p><p>That ability to question an apparently confident answer becomes increasingly important as AI-generated information spreads faster through organisations.</p><h2>The fundamentals have not disappeared</h2><p>Despite rapid technological change, much of the underlying work of analytics remains familiar.</p><p>Reliable measurement still depends on data quality, correct implementation, sound technical foundations and an understanding of what the organisation is actually trying to achieve. AI may accelerate parts of this process, but it does not remove the need for good inputs or human validation.</p><p>The discussion repeatedly returns to the principle that modelling and synthetic data cannot compensate indefinitely for weak real-world data.</p><p>Simo's position is therefore not anti-AI. He highlights useful applications including infrastructure optimisation, anomaly detection and reducing repetitive technical work. The argument is instead that AI should remove low-value effort while freeing practitioners to focus on more meaningful problems.</p><h2>Analytics is also an organisational discipline</h2><p>The conversation broadens into a critique of how organisations use analytics.</p><p>For decades, companies have invested in analytics while often separating analysts from the teams making everyday business decisions. Steen raises the problem of data becoming "negotiable" when stakeholders seek numbers that support an existing narrative.</p><p>Simo argues that analytics should not simply function as an answer-producing department. Data becomes valuable when it is integrated into organisational processes and decision-making.</p><p>The episode closes by connecting this philosophy to Analytics Dev itself. In a world where information and answers are increasingly available on demand, the hosts argue that <strong>interaction, peer learning and community become more valuable, not less.</strong></p><h3>Key Takeaway</h3><p>AI can make analytics faster, but speed alone does not create expertise. The practitioners who remain valuable will be those who combine AI fluency with strong technical foundations, critical thinking, sound data practices and the ability to connect analytics with real organisational decisions.</p></p><p><a href="http://blc.twentythree.com/analytics-in-the-age-of-ai-why"><img src="http://blc.twentythree.com/64968567/130975359/2a30e940b7c037c1c8bee9bfd4047fae/standard/download-20-thumbnail.jpg" width="600" height="338"/></a></p> http://blc.twentythree.com/photo/130975359 Mon, 31 Aug 2026 07:48:08 GMT Analytics in the Age of AI: Why Fundamentals Still Matter. Featuring Simo Ahava When answers become cheap, expertise becomes more importantThe first episode of the Analytics DevNet Webcast tackles one of the biggest questions facing analytics professionals today: what happens to expertise when AI can increasingly generate code, analysis and answers almost instantly?With analytics specialist and educator Simo Ahava joining Gunnar Griese, Steen Rasmussen and Jomar Reyes, the conversation moves beyond simple predictions about whether AI will replace analysts. Instead, it examines what practitioners need to understand if they are going to use AI responsibly and remain valuable as the technology evolves.A central concern is the growing temptation to skip the difficult parts of learning. AI can already produce JavaScript, SQL, tagging configurations and explanations that previously required technical expertise. Simo acknowledges the productivity benefits, but argues that manually working through problems builds the mental models required to recognise when something is wrong.That ability to question an apparently confident answer becomes increasingly important as AI-generated information spreads faster through organisations.The fundamentals have not disappearedDespite rapid technological change, much of the underlying work of analytics remains familiar.Reliable measurement still depends on data quality, correct implementation, sound technical foundations and an understanding of what the organisation is actually trying to achieve. AI may accelerate parts of this process, but it does not remove the need for good inputs or human validation.The discussion repeatedly returns to the principle that modelling and synthetic data cannot compensate indefinitely for weak real-world data.Simo's position is therefore not anti-AI. He highlights useful applications including infrastructure optimisation, anomaly detection and reducing repetitive technical work. The argument is instead that AI should remove low-value effort while freeing practitioners to focus on more meaningful problems.Analytics is also an organisational disciplineThe conversation broadens into a critique of how organisations use analytics.For decades, companies have invested in analytics while often separating analysts from the teams making everyday business decisions. Steen raises the problem of data becoming "negotiable" when stakeholders seek numbers that support an existing narrative.Simo argues that analytics should not simply function as an answer-producing department. Data becomes valuable when it is integrated into organisational processes and decision-making.The episode closes by connecting this philosophy to Analytics Dev itself. In a world where information and answers are increasingly available on demand, the hosts argue that interaction, peer learning and community become more valuable, not less.Key TakeawayAI can make analytics faster, but speed alone does not create expertise. The practitioners who remain valuable will be those who combine AI fluency with strong technical foundations, critical thinking, sound data practices and the ability to connect analytics with real organisational decisions. When answers become cheap, expertise becomes more importantThe first episode of the Analytics DevNet Webcast tackles one of the biggest questions facing analytics professionals today: what happens to expertise when AI can increasingly generate... Brand Leadership Community 48:38 <p><h2>When answers become cheap, expertise becomes more important</h2><p>The first episode of the Analytics DevNet Webcast tackles one of the biggest questions facing analytics professionals today: <strong>what happens to expertise when AI can increasingly generate code, analysis and answers almost instantly?</strong></p><p>With analytics specialist and educator Simo Ahava joining Gunnar Griese, Steen Rasmussen and Jomar Reyes, the conversation moves beyond simple predictions about whether AI will replace analysts. Instead, it examines what practitioners need to understand if they are going to use AI responsibly and remain valuable as the technology evolves.</p><p>A central concern is the growing temptation to skip the difficult parts of learning. AI can already produce JavaScript, SQL, tagging configurations and explanations that previously required technical expertise. Simo acknowledges the productivity benefits, but argues that manually working through problems builds the mental models required to recognise when something is wrong.</p><p>That ability to question an apparently confident answer becomes increasingly important as AI-generated information spreads faster through organisations.</p><h2>The fundamentals have not disappeared</h2><p>Despite rapid technological change, much of the underlying work of analytics remains familiar.</p><p>Reliable measurement still depends on data quality, correct implementation, sound technical foundations and an understanding of what the organisation is actually trying to achieve. AI may accelerate parts of this process, but it does not remove the need for good inputs or human validation.</p><p>The discussion repeatedly returns to the principle that modelling and synthetic data cannot compensate indefinitely for weak real-world data.</p><p>Simo's position is therefore not anti-AI. He highlights useful applications including infrastructure optimisation, anomaly detection and reducing repetitive technical work. The argument is instead that AI should remove low-value effort while freeing practitioners to focus on more meaningful problems.</p><h2>Analytics is also an organisational discipline</h2><p>The conversation broadens into a critique of how organisations use analytics.</p><p>For decades, companies have invested in analytics while often separating analysts from the teams making everyday business decisions. Steen raises the problem of data becoming "negotiable" when stakeholders seek numbers that support an existing narrative.</p><p>Simo argues that analytics should not simply function as an answer-producing department. Data becomes valuable when it is integrated into organisational processes and decision-making.</p><p>The episode closes by connecting this philosophy to Analytics Dev itself. In a world where information and answers are increasingly available on demand, the hosts argue that <strong>interaction, peer learning and community become more valuable, not less.</strong></p><h3>Key Takeaway</h3><p>AI can make analytics faster, but speed alone does not create expertise. The practitioners who remain valuable will be those who combine AI fluency with strong technical foundations, critical thinking, sound data practices and the ability to connect analytics with real organisational decisions.</p></p><p><a href="http://blc.twentythree.com/analytics-in-the-age-of-ai-why"><img src="http://blc.twentythree.com/64968567/130975359/2a30e940b7c037c1c8bee9bfd4047fae/standard/download-20-thumbnail.jpg" width="600" height="338"/></a></p> analyticsdev.net Webcast http://blc.twentythree.com/webcast-the-future-of-2 <p><p><a href="http://brandleadership.community/Insights/webcast-the-future-of-organisational-transformation/" target="_self">Click here to see the full program</a>&nbsp;</p> </p><p><a href="http://blc.twentythree.com/webcast-the-future-of-2"><img src="http://blc.twentythree.com/64968567/65227018/195ad1df06dac583688098cb00cb1d24/standard/download-9-thumbnail.jpg" width="600" height="338"/></a></p> http://blc.twentythree.com/photo/65227018 Thu, 19 Nov 2020 11:46:04 GMT Webcast: The Future of Organisational Transformation Click here to see the full program Click here to see the full program Brand Leadership Community 46:41 <p><p><a href="http://brandleadership.community/Insights/webcast-the-future-of-organisational-transformation/" target="_self">Click here to see the full program</a>&nbsp;</p> </p><p><a href="http://blc.twentythree.com/webcast-the-future-of-2"><img src="http://blc.twentythree.com/64968567/65227018/195ad1df06dac583688098cb00cb1d24/standard/download-9-thumbnail.jpg" width="600" height="338"/></a></p> Future Organisation webcast