The Rising Importance of AI Governance

AI governance has become a critical topic in today’s technological landscape, especially with the rise of AI and GenAI. As CEOs express concerns regarding the potential risks with these technologies, it is important to identify and address the biggest risks. Implementing effective guardrails for AI governance has become a major point of discussion, with a…

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The End of Agile – Part 6 (Alternatives to Agile)

In the first article, I laid out the basic premise for this series: an examination of how Agile has gone from the darling of the application development community to a virtual pariah that nobody wants to be associated with, and an exploration of the very important question of what we should replace it with. We…

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The End of Agile – Part 5 (Misapplications of Agile)

In the first article, I laid out the basic premise for this series: an examination of how Agile has gone from the darling of the application development community to a virtual pariah that nobody wants to be associated with, and an exploration of the very important question of what we should replace it with. We…

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The Analytics Sandwich: Understanding the Business Value of Data and AI

In discussions with data management professionals, conversations often veer toward the technical intricacies of migration to the cloud or algorithm optimization, overshadowing the core business objectives that originally spurred these initiatives. Yet, conversations with chief information officers (CIOs) and chief data officers (CDOs) reveal a relentless pursuit of concrete business value, a metric that determines…

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The End of Agile – Part 4 (Lessons from Agile)

In my first article, I laid out the basic premise for this series: an examination of how Agile has gone from the darling of the application development community to a virtual pariah that nobody wants to be associated with, and an exploration of the very important question of what we should replace it with. We…

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A Step Ahead: IoT Data Characteristics — Seven Vs

IoT (Internet of Things) incorporates many new and innovative technologies, such as sensors, smart devices, machine-to-machine communication, networking, advanced computing, and data analytics. One of the keys in the success of IoT is the data that flows underneath these technologies. Naturally, the IoT sensors and devices generate a huge amount of data automatically and continuously….

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Crossing the Data Divide: AI Data Assistants — A Data Leader’s Force Multiplier

The focus of my last column, titled Crossing the Data Divide: Data Catalogs and the Generative AI Wave, was on the impact of large language models (LLM) and generative artificial intelligence (AI) and how we disseminate knowledge throughout the enterprise and the future role of the data catalogs. Spoiler alert if you have not read…

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Data Crime: Arizona Is Not Arkansas

I call it a “data crime” when someone is abusing or misusing data. When we understand these stories and their implications, it can help us learn from mistakes and prevent future data crimes. The stories can also be helpful if you have to explain the importance of data management to someone. The Story After a series…

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Do Citizen Data Scientists Add Value or Is the Concept Mere Buzz?

 If you read trade or industry journals or business publications, you have probably noticed that the subject of the citizen data scientist is fraught with controversy. While Gartner and other technology research firms have predicted the growth of this movement and its success, there are those who believe that the concept of citizen data scientists is just a lot of buzz and…

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The End of Agile – Part 1 (A Brief History of Agile)

In recent years, we have seen substantial pushback on many fronts against Agile as a viable and important project management methodology. In my 2016 book, “Growing Business Intelligence”[i] (a book about Agile BI), I quoted from a 2014 article by Dave Thomas, one of the signers of the “Agile Manifesto,” in which he recommended retiring…

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