Ben Wild’s Post

Hot off the heels of the Big AI Conversation at #QlikConnect - Our AI Council issued a clear warning to businesses: adopting AI without ensuring data integrity is a risky gamble. They emphasize the need for accurate, diverse and secure data for successful AI initiatives. Read more here:

Hot off the heels of the Big AI Conversation at #QlikConnect - Our AI Council issued a clear warning to businesses: adopting AI without ensuring data integrity is a risky gamble. They emphasize the need for accurate, diverse and secure data for successful AI initiatives. Read more here:

Hot off the heels of the Big AI Conversation at #QlikConnect - Our AI Council issued a clear warning to businesses: adopting AI without ensuring data integrity is a risky gamble. They emphasize the need for accurate, diverse and secure data for successful AI initiatives. Read more here:

🚨 Announcement from #QlikConnect: Our AI Council issued a clear warning to businesses: adopting AI without ensuring data integrity is a risky gamble. They emphasize the need for accurate, diverse and secure data for successful AI initiatives. Read more here: https://bit.ly/3VruXjD

"We all have a role to play here" - Our AI Council live at #QlikConnect. Read more about the council's outlined risks for businesses as they implement AI strategies - both for internal processes and within their customer facing offerings. #AI #Data #artificialintelligence #embeddedanalytics #dataquality #datagovernance #dataintegration #qlik

⏰ 72% of leading organisations see data management as a key blocker and challenge in scaling AI programmes ⏰ Surprising statistic? I don't think so... This came from a McKinsey study and it forms part of 90% of my discussions right now, not just from a information governance or data protection perspective but more out of the fact that in many organisations, information just isn't ready to maximise investments in GenAI! There's no doubt in my eyes that building a strong foundation of data quality is fundamental in succeeding in AI programmes and at EncompaaS we're utilising AI in a transformative way within our classification and enrichment capabilities that enables our clients to understand their information landscape. There are many benefits of doing this but the two key areas I see are: 1. Reduced risk by creating guardrails around information that shouldn't be accessed by AI, whether sensitive or otherwise 2. Improved efficiency and quality of response by expanding the data pool through deep information enrichment Expanding and restricting the data pool sounds like an oxymoron, however it really does work and by doing this foundational practice to improve data quality and management, it allows you to enhance many business processes on top of the ability to scale AI. I'm interested to hear your own thoughts and experiences on this. Let me know in the comments or via DM if you'd prefer! #GenAI #AI #InformationGovernance #dataprotection

Recently, I came across a thought-provoking post discussing whether AI could single-handedly address the complexities of Data Governance. The argument made was compelling - AI has the potential to tackle common issues in data governance, such as inconsistent product descriptions, material specifications, and the notorious presence of duplicates in various datasets. Yet, in my humble opinion, the true challenge of Data Governance lies beyond these technical feats—it's about accountability. AI can be a powerful ally in sorting and enhancing data, identifying potential quality rules, and even rectifying errors in addresses. However, when the dust settles, a crucial aspect emerges. Someone must validate the accuracy of what AI produces, and someone must shoulder the responsibility of allowing AI to identify and enforce data quality rules, or to complete product description. The ultimate objective of Data Governance is to enable data, ensuring high-quality information for data consumers. However, this goal remains elusive unless accountability for data is effectively distributed within the organization. Data Governance is synonymous with accountability. As far as my understanding goes, AI, while immensely helpful, cannot assume this role. It can assist, it can streamline processes, but it can't be held accountable. AI is a tool, a brilliant one, but the responsibility rests on the shoulders of those guiding its actions. AI is not magic. #DataGovernance #AI #Accountability #DataQuality #DataManagement

Get Truly Ready to Harness Gen AI’s Substantial Economic Value! 🚀 We all agree that being data-ready for generative AI is challenging. Overcoming these data challenges requires a strategic approach, advanced data engineering, and a commitment to ethical AI development. Here’s a look at some of the key hurdles Yantra data experts see every organization face in their Gen AI journey: ➡ Data Quality & Quantity ➡ Data Integration ➡ Data Privacy & Security ➡ LLM Selection ➡ Scalability ➡ AI Expertise Want to know how can we tackle AI data challenges? Stay tuned to learn about the best-used approaches to innovate and lead in the AI-driven future! #GenerativeAI #DataStrategy #AIInnovation #EthicalAI #DataAnalytics #AI #MachineLearning #TechLeadership #DataReadiness #netsuite #yantra

Practical challenges organizations face while implementing Gen AI - a must read for understanding the key hurdles in this field!

Excitement is buzzing about the potential of generative AI in business, but are you ready for it? Remember, its success is deeply rooted in the quality of your data. #DataFirst High-quality data isn't just a technical asset; it's the backbone of every successful AI initiative. Invest in robust data management practices to avoid the pitfalls of poor-quality inputs which can lead to biased decisions and lost opportunities.