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Dec 4 2024

Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?

I go to data conferences. Frequently. Almost always right here in NYC. We have lots of data conferences here. Over the years, I’ve seen a trend — more and more emphasis on AI. I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? This is my version of […]

Data Is Risky Business: Structured, Unstructured (Who Cares?)

The Irish satirist Jonathan Swift wrote “Gulliver’s Travels” almost 300 years ago, but the story of Lemuel Gulliver’s journey to Lilliput and beyond has resonance for data leaders today. There are important lessons to learn from the little people of Lilliput and the challenges encountered by the eponymous Gulliver. While Lilliput is where Gulliver first […]

Data Speaks for Itself: Data Validation – Data Accuracy Imposter or Assistant?

In my last article, “The Shift from Syntactic to Semantic Data Curation and What It Means for Data Quality” published in the August 2024 issue of this newsletter, I argued how the adoption of generative AI will change the focus and scope of data quality management (DQM). Because data quality is measured in the degree […]

The Art of Lean Governance: A Systems Thinking Approach to Data Governance

A systems thinking approach to process control and optimization demands continual data quality feedback loops. Moving the quality checks upstream to the source system provides the most extensive control coverage. Data quality approaches not utilizing these loops will fail to achieve the desired results, often worsening the problem. Data Governance is about gaining trust and […]

The Book Look: Enterprise Intelligence

Every once in a while, a book comes along that contains such innovative ideas that I find myself whispering “wow” and “interesting” as I read through the pages. “Enterprise Intelligence,” by Eugene Asahara, is one such book. Eugene takes three basic ingredients that are not so new (business intelligence, knowledge graphs, and large language models), […]

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 […]

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. […]

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 […]

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 […]

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