AI is only as powerful as the data behind it. You’ve probably heard the phrase “garbage in, garbage out.” That’s always been true in analytics, but in today’s AI-driven world, the consequences of poor data are greatly amplified. Flawed models, biased predictions and opaque decision-making all trace back to one
Back when I was in banking, I always looked forward to certain annual vendor meetings and reports. These key partners provided valuable insight into what was happening beyond the four walls of our institution. Their input was essential to helping us assess our market position and refine our strategy. Some
What if integrating your data for AI didn’t take weeks or months – but happened in minutes, without ever moving your data at all? That’s the vision guiding the development of a new data mapping agent at SAS. It’s not just a feature – it’s a potential shift in how
With a packed agenda of sessions, navigating a conference like SAS Innovate can feel overwhelming – especially for first-time attendees. Where to start? What do you mean I'll hear from inspiring and knowledgeable speakers and business leaders? There's hands-on experiences, too? No worries. After combing through the schedule, I’ve identified
Synthetic data has become a valuable resource in data science and machine learning. Superior quality, reliable synthetic data facilitates analysis and iteration at scale while mitigating privacy concerns associated with real data and can fill gaps where real data is scarce. Note, however, that “good” synthetic data is not defined
UK banks are under growing pressure to manage risks more effectively, whether from economic shifts, technological advances or geopolitical uncertainty. The Bank of England’s Prudential Regulation Authority (PRA) sent a letter to the CEOs of UK deposit holders outlining its supervisory priorities for 2025. The letter sets expectations for stronger
Where were you when the global banking crisis of 2008 hit? I was in banking – marketing, to be specific – and I supported our mortgage department. I vividly recall our bank’s chief risk officer repeatedly refusing to enter the adjustable-rate mortgage subprime lending business. Many banks were eating our
Learn how insurers can combat AI-driven fraud, secure data and build trust in an increasingly digital world.
Synthetic data has emerged as a powerful tool for overcoming the limitations of real-world data. The future holds great promise for accelerated innovation. With synthetic data, companies can now generate financial transactions, medical records or customer behavior patterns that maintain statistical relevance like real data. This emerging technology can help
At the very heart of the financial world lies a commodity so vast it’s almost immeasurable ... and it’s growing exponentially with potential yet unrealized. Big data – complex structured and unstructured datasets arriving from innumerable sources – is reshaping the global banking industry. Used effectively, big data can support the delivery
En la actualidad, los modelos analíticos son herramientas esenciales para tomar decisiones basadas en datos. Desde prever tendencias hasta optimizar operaciones, los modelos analíticos dependen en gran medida de la calidad de los datos de entrada. La precisión, integridad y relevancia de estos datos son cruciales para obtener resultados confiables
The search to maximize our own productivity is never-ending. We all want to be more efficient in our work and carve out more time with loved ones. As you assess data and AI technology to automate processes and maximize efficiency, you may wonder if it’s truly possible for you or
The global hype cycle of AI, driven in large part by ChatGPT, is dying down and real-world artificial intelligence (AI) adoption and application are taking hold. Early adopters are reaping rewards, and AI leaders are driving significant change in their business models. Banking as a sector was quick to grasp
Fearmongers would have you believe that AI will replace us, but I truly believe there has never been a better time in history to be a developer. With the advance of artificial intelligence into generative AI (GenAI) and enhanced computing power, we stand on the brink of a new era
Halloween is one of my favorite times of the year – I'm a Halloween enthusiast. It’s a time for spine-tingling thrills, haunted tales and lots of candy corn. But there’s one fear lurking in the shadows that sends shivers down the spines of data scientists, IT leaders and executives alike,
With all the technology changes coming in the next five years, what should organizations invest in first? The innovations keep coming and so do the 3 a.m. night sweats for decision makers. “How will we catch up when technology seems to change overnight, nearly every night?” It’s a surprisingly common
Data quality is a cornerstone for integrating large language models (LLMs) into organizations. The adage "garbage in, garbage out" holds particularly true here. High-quality data is the lifeblood that ensures the accuracy, relevance, and reliability of the model's outputs. In a business context, this translates to insights and decisions that
In the digital age, the adage "knowledge is power" has evolved into "data is power." It reflects the immense value of high-quality data and a strategic approach to data management. At the heart of any successful modern enterprise lies a robust data strategy coupled with stringent data quality standards. For
I see the term resilience in a lot of business literature these days. Intuitively, it makes sense. After a pandemic, global supply chain disruptions and resulting economic fragility, executives understandably consider adaptability, durability and how best to operate with a strength of character – all attributes that define resilience. Many
Analytics can get you answers from data. With an ever-increasing volume of data and new regulatory pressures, you need to know more than knowing where and how it is used. It's critical to comprehend the data: Is it relevant? Does it contain sensitive or private information? Can we detect bias?
My recent work has focused heavily on migration, especially onto the SAS Viya platform and cloud more generally. Rather unexpectedly during this process, we have found that data observability is becoming increasingly important to customers. They start simply by looking at tracing files, but soon find that it has a
Have you ever heard the phrase “beggars can’t be choosers”? Basically, it means that if you ask for something, be grateful for what you get, especially if you don’t have the means to acquire it yourself. This phrase can be widely applicable to most areas of our lives, but when
Higher education institutions are some of the most data-rich entities in the world. Postsecondary leaders need high-quality, consistent and accurate insights to make the best decisions for their institution, students and constituents. Data governance is a topic that may seem technical in nature and perhaps important to only the IT
I was recently told that an organization had tried to implement AI for forecasting in supply chain but had failed due to poor data. This got me thinking about exactly what the impacts of poor data would be. And whether the approaches I had applied elsewhere could help. It's probably
Learn why integrating EHR data with pharmacy and claims data improves patient care.
Learn the nuts and bolts of how to measure data quality from expert Jim Harris.
Jim Harris shows how data, analytics and humans work together to form the "insight equation."
Read about the value of data tagging and learn best practices for doing it effectively.
Surprise! The data team does more than you think to implement certain legislative actions.
Jim Harris shares three more examples of how data quality improves AI in Part 2 of his series.
