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Oct 2 2024

Data Leader’s Playbook for Data Mapping

I’ve been thinking a lot about data mapping lately. I know, weird, right? With analytics, AI, cloud, etc., why would someone do that? What’s even stranger is that I’ve been thinking about its impact on data leaders. For clarity’s sake, I’m not talking about geographic maps with data points, I’m referring to the process of […]

Through the Looking Glass: More Metaphors and LLMs

Part I When I finished my last column, “Through the Looking Glass: Metaphors, MUNCH, and Large Language Models,” I stated my intention to follow up with part II. I would cover whether a knowledge graph’s vocabulary of “triples” relates to metaphoric thinking. I even considered challenging an LLM on its ability to understand metaphors to […]

Legal Issues for Data Professionals: Current Leading U.S. AI Laws

There is no nationwide federal law in the U.S. that specifically regulates the development, deployment, and use of AI in the private sector. (This contrasts with AI use in U.S. federal agencies, as discussed below.) This absence of such a federal law contrasts with the recently enacted AU AI law. Instead, in the U.S., there […]

Data Is Risky Business: Scaling Data Governance to the National Stage

I recently started a doctorate. And because I obviously have too much free time after running my business, teaching, and writing columns for august publications like this, I’m looking at data governance. But not at the level of the organization. My doctoral research will be a deep dive into some oft-neglected human factors that need to […]

The Data-Centric Revolution: Dealing with Data Complexity

There are many perennial issues with data: data quality, data access, data provenance, and data meaning. I will contend in this article that the central issue around which these others revolve is data complexity. It’s the complexity of data that creates and perpetuates these other problems. As we’ll see, it is a tractable problem that […]

Eyes on Data: Understanding Data Products and Their Role in Data Marketplaces

In the rapidly evolving landscape of data management, the concept of data products has emerged as a cornerstone for effective data utilization and governance. Industry experts have shed light on the critical nature of data products, their distinction from data assets, and their pivotal role in data marketplaces. As organizations strive to maximize the value […]

A Step Ahead: IoT Sensors – Where Vast Data Comes From

The insight we gain from an IoT system is derived from the data obtained by its sensors. Driven by innovations in materials and nanotechnology, sensor technology has been developing at unprecedented speeds and has resulted in lower-cost sensors that have better accuracy, are smaller in size, and able to detect or measure the presence of […]

Data Professional Introspective: The Data Management Education Program (Part 2)

In my work with the EDM Council’s Data Management Capability Assessment Model (DCAM) 3.0 development group, we are adding a capability that has remained under the radar in our industry, that is, the responsibility of the Data Management Program to determine concept and knowledge gaps within its staff resources. The organization should then plan, organize, […]

The Book Look: AI Governance

Technics Publications has started publishing a line of Data-Driven AI books, and one of the first books in this series is “AI Governance” by Dr. Darryl J Carlton. The goal of the book in one sentence is to enable the reader to gain the knowledge and tools to effectively govern and oversee the use of […]

Through the Looking Glass: Metaphors, MUNCH, and Large Language Models

“What’s a metaphor?” Mr. Biergel posed the question one morning to my high school grammar class. Being typical teenagers, we looked at him with blank-eyed stares. We expected that if we waited long enough, he’d write a paragraph-long definition on the blackboard. “What’s a metaphor?” he repeated. “A place for cows to graze!” We groaned. […]

Legal Issues for Data Professionals: Pros and Cons of AI in Healthcare (Part 1)

The use of Artificial Intelligence (AI) in healthcare provides promises, risks, and unintended consequences. This column addresses the evolving AI issues in connection with the following topics: As used in this column, “AI” covers both generative and non-generative AI, with a focus on machine learning as part of non-generative AI. Reducing Administrative Burdens on Physicians […]

Data Is Risky Business: The Big Picture Ethics of Small Data Actions

Regular readers of this column will have noticed a thread weaving its way through the articles over the past few months. The role of the individual in the execution of ethical data management and in addressing the risks that arise when we collect data, store data, share data, or put data to work is often […]

The Data-Centric Revolution: Who Doesn’t Need Data-Centric?

Several years ago, as part of some strategic marketing, we decided to target certain companies and industries. The thinking was and is, that certain sectors are inherently more amenable to the economic arguments behind the data-centric approach. We didn’t do any market research, didn’t do any surveys, didn’t purchase any reports or data, we just […]

The Art of Lean Governance: What We Can Learn from HAZMAT Professionals

The collapse of the Francis Scott Key bridge on March 26, 2024, was a tragic loss of life and disruption to the shipping supply chain on a global scale that will take months, if not years, to fully recover. A major shipping artery in and out of the United States was severed, stranding ships on […]

Eyes on Data: Improving ESG Reporting with Established Industry Standards

In today’s rapidly evolving corporate landscape, environmental, social, and governance (ESG) reporting has become a focal point for organizations aiming to demonstrate their commitment to sustainability and responsible business practices and comply with a growing roster of regulations. As companies navigate the complexities of ESG reporting, they increasingly seek guidance on how to effectively manage […]

Data Speaks for Itself: Is Metadata Data?

Well, of course, metadata is data. Our standard definition explicitly says that metadata is data describing other data. So why would I even ask this question in the article title? The reason I ask it is because we seem to think about and manage metadata as somehow different than “normal data” such as business operations […]

Data Professional Introspective: The Data Management Education Program

In my work with the EDM Council’s Data Management Capability Assessment Model (DCAM) 3.0 development group, we are adding a capability that has remained under the radar in our industry: the responsibility of the Data Management Program to determine concept and knowledge gaps within its staff resources. The organization should then plan, organize, and make […]

All in the Data: A Steward in Need Is a Steward in Deed

You’ve probably heard the expression, “A friend in need is a friend indeed.” For this column, I am going to play with that expression to make a point. Instead of a friend, I will refer to friends as data stewards. Instead of indeed (meaning certainly), I am going to insert a space in the word […]

Data Is Risky Business: The Opportunity Exists Between Keyboard and Chair

I’m doing some research work for a thing (more on that thing later in the column). My research has had me diving through all the published academic research in the field of data governance (DG) that deals with critical success factors for sustainable (as in: “not falling over and sinking into a swamp with all […]

Legal Issues for Data Professionals: AI Creates Hidden Data and IP Legal Problems

As data has catapulted to a new and valuable business asset class, and as AI is increasingly used in business operations, the use of AI has created hidden data and IP risks. These risks must be identified and then measures must be taken to protect against both a loss of rights and an infringement of […]

Data-Centric: How Big Things Get Done (in IT)

I read “How Big Things Get Done” when it first came out about six months ago.[1] I liked it then. But recently, I read another review of it, and another coin dropped. I’ll let you know what the coin was toward the end of this article, but first I need to give you my own […]

Through the Looking Glass: Data as Code? Or Data as a Code?

Readers of my column know my aversion to buzzwords.[1] I approach the hot catchphrase “Data as Code” with trepidation. Already, we have to name a few: – Infrastructure as Code (with its own acronym, IaC) – Configuration as Code (Config as Code — why not CaC?) – Environment as Code (EaC is not available, as […]

The Art of Lean Governance: Addressing the Elephant in the Room

Hands down one of the most frequent observations when walking the data factory at different clients is the excessive use of spreadsheets for data collection and purification. These spreadsheets are part of a critical data enrichment process for getting reports out the door on time. However, these same spreadsheets represent a significant control problem exposing […]

Eyes on Data: Transforming Data Challenges into Real Progress

In a world increasingly dominated by data, organizations are grappling with the need to effectively manage and harness this valuable asset. And with increased regulations and compliance, opportunities for innovation and AI, digital transformation initiatives, and data-driven decision-making, the demands for accurate, accessible, protected data are increasing exponentially. At the same time, the data management […]

Data Speaks for Itself: Is AI the Cure for Data Curation?

By now, it is clear to everyone that AI, especially generative AI, is the only topic you’re allowed to write about. It seems to have impacted every area of information technology, so, I will try my best to do my part. However, when it comes to data curation and data quality management, there seems to […]

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Category Archives: Data Management Columns