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Data Quality Fuels AI Growth
ALSO : Huxe: AI Creates Podcasts for Your Research
Miso PECIREP
September 23, 2025
News of the day
1. Martin Frederik from Snowflake emphasizes that data quality is crucial for AI success. He stresses the need for a unified and governed data strategy to turn AI projects into revenue generators and achieve business goals. → Read more
2. Former NotebookLM developers launch Huxe, an audio application using AI hosts to create personalized podcasts for news and research. Funding round of $4.6 million. → Read more
3. A new AI analyzes crowd movement patterns, not just numbers, to predict and prevent disasters. It achieves an accuracy improvement of up to 76.1% compared to existing methods. → Read more
4. OpenAI CEO Sam Altman states that increasing computing power is the key factor behind AI breakthroughs and the company’s revenue growth. This strategy aims to unlock future innovations and ensure financial expansion. → Read more
Our take
Hi Dotikers!
No AI strategy without a data strategy.
AI is not short on models, it is short on reliable data. As a Snowflake executive recently emphasized, it is not POCs that are missing, but clean, governed, and shared datasets that truly convert experiments into business growth. AI is just the vehicle, the road is data quality. And the companies already measuring tangible ROI are precisely those that invested in a unified and governed platform.
This is no longer just a best practice, it is becoming a regulatory obligation. With the EU AI Act, high-risk systems will have to be trained, validated, and tested on high-quality datasets, with progressive requirements starting in 2025 and fully enforced by 2027. Data quality is therefore no longer a luxury, it is a condition for compliance and competitiveness.
At the same time, the wave of AI agents promises to bring data closer to action. Platforms like Snowflake are already announcing agents capable of reasoning across structured and unstructured data, orchestrating tasks, and accelerating analysis and production. Great, but let’s not skip steps: without governance and observability, an agent will simply automate the mess.
Our position is clear: stop funding gadget POCs and start funding the foundations. Data quality, catalogs and contracts, lineage, bias management, reliability metrics, and continuous model evaluation. These are the pillars on which sustainable value is built.
“Garbage in, genius out, does not exist.”
M.
Meme of the day
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