Data as a Service
First Things First: How You Can Overcome the Top 8 Challenges of Generative AI
Generative Artificial Intelligence (GenAI) stands as a transformative force in the digital landscape, promising innovative solutions and creative approaches to data synthesis. However, GenAI faces its fair share of adoption hurdles. Organizations committed to leveraging generative AI must navigate through myriad challenges, ensuring both the solution efficacy and ethical application. Let’s delve into the top 8 challenges...
Get Ahead or Left Behind: The Generative AI Trend & It’s Growing Role in Tomorrow’s Workplace
ChatGPT and Bard, DALL-E and Starryai: Generative AI (GenAI) tools are taking the world by storm, igniting conversations, and shaping future possibilities in ways we've only begun to explore. As the World Economic Forum notes, “After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing...
Garbage In, Garbage Out: Why Third-Party Data Sources Matter When Using Generative AI
In a recent LinkedIn post , data and technology transformation consultant Tommy Tang writes, “Generative AI has emerged as a potent tool across various domains, from content creation to bolstering decision support systems.” He warns, however, that “The efficacy of generative AI is intrinsically tied to the quality of its training data.” And therein lies the challenge, aptly summarized by the adage, “Garbage in, garbage...
AI for Business: How to Capitalize on Generative AI to Enhance Decision-Making
The intersection of Artificial Intelligence (AI) and business pulses with potential, especially since generative AI (GenAI) has entered the picture. Google research scientist Oriol Vinyals notes, “Generative models are changing the way we think about machine intelligence and creativity, and have the potential to transform industries from media to finance to healthcare.” Of course, the insights you gain are only as good...
First Things First: How You Can Overcome Top 8 Challenges of Generative AI
Generative Artificial Intelligence (GenAI) stands as a transformative force in the digital landscape, promising innovative solutions and creative approaches to data synthesis. However, GenAI faces its fair share of adoption hurdles. Organizations committed to leveraging generative AI must navigate through myriad challenges, ensuring both the solution efficacy and ethical application. Let’s delve into the top 8 challenges...
Garbage In, Garbage Out: Why Third-Party Data Sources Matter When Using Generative AI
In a recent LinkedIn post , data and technology transformation consultant Tommy Tang writes, “Generative AI has emerged as a potent tool across various domains, from content creation to bolstering decision support systems.” He warns, however, that “The efficacy of generative AI is intrinsically tied to the quality of its training data.” And therein lies the challenge, aptly summarized by the adage, “Garbage in, garbage...
Get Ahead or Left Behind: The Generative AI Trend & It’s Growing Role in Tomorrow’s Workplace
ChatGPT and Bard, DALL-E and Starryai: Generative AI (GenAI) tools are taking the world by storm, igniting conversations, and shaping future possibilities in ways we've only begun to explore. As the World Economic Forum notes, “After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing...
AI For Business: How to Capitalize on Generative AI to Enhance Decision-Making
The intersection of Artificial Intelligence (AI) and business pulses with potential, especially since generative AI (GenAI) has entered the picture. Google research scientist Oriol Vinyals notes, “Generative models are changing the way we think about machine intelligence and creativity, and have the potential to transform industries from media to finance to healthcare.” Of course, the insights you gain are only as good...
The Four Major Developments Every Business Should Know About Anti-Money Laundering Regulations
The Four Major Developments Every Global Business Should Know About Anti-Money Laundering Regulations Anti-Money Laundering regulations have changed rapidly in recent years–from Switzerland to Singapore, from Brazil to Bahrain. Building on our whitepaper, ‘ AML Compliance: A Global View’, we identify and summarise four major developments which are driving regulatory changes across the world. This blog summarises those...
Enriched Patent Data: The Secret Weapon in Powering Investment Decisions and Closing the ESG Reporting Gap
ESG investing strategies have been among the most profitable in recent years. But investors find it difficult to assess companies’ claims to be following Environmental, Social and Governance (ESG) principles. Patent data has proven very effective in testing these claims and closing the ESG reporting gap. Closing the ESG data reporting gap Global ESG assets are expected to exceed $53 trillion by 2025 , reports the...
Decision Intelligence: What Is It & Why Is Alternative Data an Essential Piece of the Puzzle?
Organizational consultant and author Idowu Koyenikan writes , “Speed is not your enemy; hesitation is.” He’s right. The ability to make highly informed decisions in a timely manner can be the difference between the success or failure of your business. Particularly during times of uncertainty or market volatility, doubt can hold organizations back. And data overload can contribute to this doubt. So how do businesses...
4 Uses for Text-Based Data that Turns Insurance Market Intelligence into Valuable Insights that Enhance Decisions Across Your Business
A McKinsey article on use of AI in the nsurance industry calls data and analytics capabilities “table stakes” in the sector in Europe and North America. The article notes that “External data are the ‘fuel’ that is unlocking the value of artificial intelligence.” What types of text-based data can help you derive actionable insights? Current and historical news data and social commentary Market and industry data ...
4 Reasons for Integrating Third-Party Data in Financial Services Risk Management Workflows
Recent volatility in the financial services sector only reinforces how interconnected and complex global markets have become. Geopolitical and economic uncertainty, coupled with expanding regulation, makes navigating the turbulence even more challenging. Global consultancy EY notes that “Data has an increasingly important role within financial crime risk management.” To get ahead of the curve, financial institutions increasingly...
Everything to Know About Generative AI in Media Production
If you’ve logged onto the internet in 2023, you’ve probably heard about AI advancements. From appealing AI filters on social media to the March 14, 2023 release of GPT-4, artificial intelligence (AI) has become all the rage, and that’s not likely to end anytime soon. But what does AI have to do with film and media production? Generative AI, a category of intelligence that creates content like text and imagery, could...
The Unseen Cost of Low-Quality Internal & Third- Party Data in Decision Intelligence
In the quest to achieve unrivaled business growth, organizations show increasing interest in Decision Intelligence (DI). Whether you use DI to augment, recommend, or automate decisions, the effectiveness of your DI endeavors heavily depends on the quality of data that powers it. As financial services and global businesses alike expand the use of DI, weeding out poor quality—within both internal and alternative data—isn...