{"id":5802,"date":"2025-09-23T21:23:55","date_gmt":"2025-09-23T21:23:55","guid":{"rendered":"https:\/\/www.talkforweb.in\/blog\/data-quality-is-key-to-ai-driven-growth\/"},"modified":"2025-09-23T21:23:55","modified_gmt":"2025-09-23T21:23:55","slug":"data-quality-is-key-to-ai-driven-growth","status":"publish","type":"post","link":"https:\/\/www.talkforweb.in\/blog\/data-quality-is-key-to-ai-driven-growth\/","title":{"rendered":"Data quality is key to AI-driven growth"},"content":{"rendered":"

<\/p>\n

\n

As companies race to implement AI, many are finding that project success hinges directly on the quality of their data. This dependency is causing many ambitious initiatives to stall, never making it beyond the experimental proof-of-concept stage.<\/p>\n

So, what\u2019s the secret to turning these experiments into real revenue generators? AI News caught up with Martin Frederik, regional leader for the Netherlands, Belgium, and Luxembourg at data cloud giant Snowflake<\/a>, to find out.<\/p>\n

\u201cThere\u2019s no AI strategy without a data strategy,\u201d Frederik says simply. \u201cAI apps, agents, and models are only as effective as the data they\u2019re built on, and without unified, well-governed data infrastructure, even the most advanced models can fall short.\u201d<\/p>\n

Improving data quality is key to AI project success<\/h3>\n

It\u2019s a familiar story for many organisations: a promising proof-of-concept impresses the team but never translates into a tool that makes the company money. According to Frederik, this often happens because leaders treat the technology as the end goal.<\/p>\n

\"Headshot<\/figure>\n

\u201cAI is not the destination \u2013 it\u2019s the vehicle to achieving your business goals,\u201d Frederik advises.<\/p>\n

When projects get stuck, it\u2019s usually down to a few common culprits: the project isn\u2019t truly aligned with what the business needs, teams aren\u2019t talking to each other, or the data is a mess. It\u2019s easy to get disheartened by statistics suggesting that 80% of AI projects don\u2019t reach production, but Frederik offers a different perspective. This isn\u2019t necessarily a failure, he suggests, but \u201cpart of the maturation process\u201d.<\/p>\n

For those who get the foundation right, the payoff is very real. A recent Snowflake study<\/a> found that 92% of companies are already seeing a return on their AI investments. In fact, for every \u00a31 spent, they\u2019re getting back \u00a31.41 in cost savings and new revenue. The key, Frederik repeats, is having a \u201csecure, governed and centralised platform\u201d for your data from the very beginning.<\/p>\n

It\u2019s not just about tech, it\u2019s about people<\/h3>\n

Even with the best technology, an AI strategy can fall flat if the company culture isn\u2019t ready for it. One of the biggest challenges is getting data into the hands of everyone who needs it, not just a select few data scientists. To make AI work at scale, you have to build strong foundations in your \u201cpeople, processes, and technology.\u201d<\/p>\n

This means breaking down the walls between departments and making quality data and AI tools accessible to everyone.<\/p>\n

\u201cWith the right governance, AI becomes a shared resource rather than a siloed tool,\u201d Frederik explains. When everyone works from a single source of truth, teams can stop arguing about whose numbers are correct and start making faster and smarter decisions together.<\/p>\n

The next leap: AI that reasons for itself<\/h3>\n

The true breakthrough we\u2019re seeing now is the emergence of AI agents that can understand and reason over all kinds of data at once regardless of structure quality; from the neat rows and columns in a spreadsheet, to the unstructured information in documents, videos, and emails. Considering that this unstructured data makes up 80-90% of a typical company\u2019s data, this is a huge step forward.<\/p>\n

New tools are enabling staff, no matter their technical skill level, to simply ask complex questions in plain English and get answers directly from the data.<\/p>\n

Frederik explains that this is a move towards what he calls \u201cgoal-directed autonomy\u201d. Until now, AI has been a helpful assistant you had to constantly direct. \u201cYou ask a question, you get an answer; you ask for code, you get a snippet,\u201d he notes.<\/p>\n

The next generation of AI is different. You can give an agent a complex goal, and it will figure out the necessary steps on its own, from writing code to pulling in information from other apps to deliver a complete answer. This will automate the most time-consuming parts of a data scientist\u2019s job, like \u201ctedious data cleaning\u201d and \u201crepetitive model tuning.\u201d<\/p>\n

The result? It frees up your brightest minds to focus on what really matters. This elevates your people \u201cfrom practitioner to strategist\u201d and allows them to drive real value for the business. That can only be a good thing.<\/p>\n

Snowflake is a key sponsor of this year\u2019s <\/em>AI & Big Data Expo Europe<\/em><\/a> and will have a range of speakers sharing their deep insights during the event. Swing by Snowflake\u2019s booth at stand number 50 to hear more from the company about making enterprise AI easy, efficient, and trusted.<\/em><\/p>\n

See also: <\/strong>Public trust deficit is a major hurdle for AI growth<\/strong><\/p>\n

\"Banner<\/a><\/figure>\n

Want to learn more about AI and big data from industry leaders?<\/strong> Check out AI & Big Data Expo<\/a> taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx<\/a> and is co-located with other leading technology events, click here<\/a> for more information.<\/p>\n

AI News is powered by TechForge Media<\/a>. Explore other upcoming enterprise technology events and webinars here<\/a>.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

As companies race to implement AI, many are finding that project success hinges directly on the quality of their data. 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