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Oct 2 2024

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), […]

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 […]

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 […]

The Data-Centric Revolution: Best Practices and Schools of Ontology Design

I was recently asked to present “Enterprise Ontology Design and Implementation Best Practices” to a group of motivated ontologists and wanna-be ontologists. I was flattered to be asked, but I really had to pause for a bit. First, I’m kind of jaded by the term “best practices.” Usually, it’s just a summary of what everyone […]

Eyes on Data: The Right Foundation for Trusted Data and Analytics

Trust. Trust is defined as the assured reliance or belief on the character, ability, strength, or truth of someone or something (Webster’s Dictionary). It’s a term we use often to describe how we feel about the people, the institutions, and the things around us. But I would argue that the term “trust” was used differently […]

Crossing the Data Divide: Framework for Selling Data Initiatives

Deja Vu All Over Again Something interesting has been happening to me over the last few months that I’ve not experienced in a while. Smart and experienced CIOs and their data leaders have been asking me for input regarding how to sell the value of a data program. The question is a clear sign of […]

Data Professional Introspective: Accelerating Enterprise Data Quality

My recent columns have focused on actionable initiatives that can both deliver business value, providing a tangible achievement, and raise the profile of the data management organization data management organization (DMO).(For more on the DMO, a plug-and-play initial organization was proposed in an earlier TDAN column, “Coming in from the Cold.”) In that light, let’s […]

Through the Looking Glass: Data Provenance in the Age of Generative AI

Am I right that you can’t open LinkedIn, read a tech or business blog, or attend a conference for more than 30 seconds without encountering the generative AI hype? It’s been this way ever since last fall when OpenAI unleashed ChatGPT upon a (mostly) unsuspecting world. Depending on who you read or listen to, generative […]

Data is Risky Business: A Wicked Problem This Way Comes

A recent data security incident in the Police Service of Northern Ireland (PSNI) got me thinking about the idea of wicked problems and data. The data security incident was the disclosure of the names, ranks, and job assignments of every officer and civilian support staff member in the PSNI. This happened due to ‘human error’ […]

The Data-Centric Revolution: “RDF is Too Hard”

We hear this a lot. We hear it from very smart people. Just the other day we heard someone say they had tried RDF twice at previous companies and it failed both times. (RDF stands for Resource Description Framework,[1] which is an open standard underlying many graph databases). It’s hard to convince someone like that […]

A Step Ahead: Data in Support of (Zero) Trust

Over the past year, the conversations around Zero Trust have begun to accelerate. Much of the discussion is akin to data mesh and data fabric where business owners and management are hearing the term and asking what a Zero Trust product is. The term is also permeating data professionals’ conversations. This article provides a brief overview of […]

A Step Ahead: Data Fabric and Data Mesh – Similarities and Differences

The terms Data Mesh and Data Fabric have been used extensively as data management solutions in conversations these days, and sometimes interchangeably, to describe techniques for organizations to manage and add value to their data. In this article, we intend to clarify these terms and explain the overlaps and differences to enable the readers to […]

🔴 Red Button Data

🔴 Red Button Data

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