We often say AI’s mistakes are "by design," but they’re really not. AI wasn’t built to fail in these specific ways—its errors emerge as a byproduct of how it learns.
But what if we actively use them as a tool instead of just tolerating AI’s weird mistakes or trying to eliminate them?
Here are some unexpected but potentially valuable use cases where treating AI mistakes as a form of bias—rather than just failure—could lead to new insights and innovations.
Read MoreA student recently asked: “How can AI transform the relationships between the U.S. and African countries?” The premise is compelling—AI has the potential to drive transparency, trade, and governance reform, resetting relationships on healthier grounds.
But we’ve seen this movie before.
Read MoreWe’ve become numb to the headlines. Data breaches happen almost daily, making cybersecurity a top priority for insurers. With its vaults of personal and financial data, the insurance industry is a prime target for cybercriminals. This blog post will explore effective cybersecurity strategies for insurers, highlighting real-world cases and spotlighting new technologies reshaping the cybersecurity landscape.
Read MoreMastering data governance has become a critical challenge for insurers in today's rapidly evolving insurance landscape. Many companies struggle to balance fostering innovation and maintaining regulatory compliance. This blog post will explore the complexities of insurance data governance, highlighting the pitfalls and best practices.
Read MoreAs organizations rush headlong into digital transformation initiatives, a critical factor often gets overlooked: data quality and the resistance to support ongoing data quality efforts. In the race to implement cutting-edge technologies and overhaul business processes, many companies fail to recognize that the success of these efforts hinges on the accuracy, completeness, and reliability of their underlying data. This oversight can lead to disastrous consequences, undermining the very goals that digital transformation aims to achieve.
Read MoreFor decades, we've heard the same refrain: “Change management is crucial for project success.” Yet leaders have nodded politely and ignored this advice, particularly in data and technology initiatives. The result? According to McKinsey, a staggering 70% of change programs fail to achieve their goals. So why do we keep making the same mistake, and more importantly, why should we care now?
Read MoreReal estate focuses on governance and value derivation from assets. The Marketplace facilitates value-driven transactions while maintaining order, quality, and trust throughout the ecosystem. BOTH must work TOGETHER.
Read MoreOne of the biggest ideas in Driving Data Projects (the book) is that "all technology projects are data projects." Yet data is still an afterthought in many organizations—even with AI on the horizon (or now, in many firms' backyards).
Author of Data Quality: The Field Guide, Tom Redman, popularized the idea that the most important moments in a piece of data's lifetime are the moment it is created and the moment it is used. These moments often occur outside of IT. The business consumes vast amounts of data, emphasizing the importance of business involvement in data quality management. Those who have provisioned and consumed data know from experience that bad data dies hard. It will get rid of you if you don't get rid of it.
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Data teams should be regarded as intentional business partners because they provide the underlying technology that enables business strategy and maintains data as a corporate asset. They can help educate business partners on the upstream and downstream impacts of poor data quality, and they can help cultivate more effective ambassadors for data governance across the organization.
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Data analytics is filled with complexity. Anyone saying otherwise is selling products. Knowing the data sources, data sets, general lineage, and behavior of the numbers are table stakes for the average data consumer. We must know where our data comes from. Much like we need to know where our food comes from and how it's processed. Is it safe to consume?
Lately, I’ve heard many stories about early career folks with data analyst titles turning to ChatGBT for help because they don't know where to go with questions. ChatGBT should only be used when the output can be rigorously challenged, which can only happen if you have the foundational knowledge of how the output was generated. Here are some handy Do’s and Don’ts to remember before turning to ChatGBT.