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.
Phil Simon weighs in on using data to make the most of AI.
Phil Simon says that the downsides of even a few discrepancies can be enormous.
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
Data scientists spend a lot of their time using data. Data quality is essential for applying machine learning models to solve business questions and training AI models. However, analytics and data science do not just make demands on data quality. They can also contribute a lot to improving the quality
Jim Harris says curating AI’s curriculum is the responsibility of data stewards.
Learn why Jason Simon from UNT calls data governance critical.
Expect to lose time if you don't include a data steward in your project until you're reviewing the data model.
By now you’ve seen the headlines and the hype proclaiming data as the new oil. The well-meaning intent of these proclamations is to cast data in the role of primary economic driver for the 21st century, just as oil was for the 20th century. As analogies go, it’s not too
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
Data management gets lost in the enthusiasm around Artificial intelligence (AI) and machine learning (ML). Not surprising, when it's an algorithm that decides what search results to show you, guides the self-driving cars on the roads, and powers the anti-fraud bots that monitor every credit card transaction we make. Charles
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
How should a data trust process work? David Loshin elaborates.
