Research has shown that many people learn best when they see a story or information depicted in an image. Graphs, charts with colors, lines and shapes can often tell a story and communicate issues, challenges and opportunities in a business environment. According to Forbes, “Almost eighty-thousand scientific studies attest that visual images promote retention.” Let’s…
As we approach the end of another year, we here at DATAVERSITY have been busy analyzing our data and answering the big questions: What was the most popular content on our website and training center over the past 12 months? Which data management topics did you – our readers and students – seek out again…
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),…
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…
Download the slides here>> About the Webinar Vector databases are a type of database that use graph embeddings to represent and compare data, making them ideal for fuzzy match problems. Graph embeddings are created using machine learning algorithms and compress the attributes of data into a low-level representation. The process of creating a new embedding…
Commerce today runs on data – guiding product development, improving operational efficiency, and personalizing the customer experience. However, many organizations fall into the trap of thinking that more data means more sales, when these two factors aren’t directly correlated. Often, executives will become overzealous in their digital transformations and cut blank checks for data collection,…
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…
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…
The work of data engineers is extremely technical. They are responsible for designing and maintaining the architecture of data systems, which incorporates concepts ranging from analytic infrastructures to data warehouses. A data engineer needs to have a solid understanding of commonly used scripting languages and is expected to support the steady evolution of improved Data Quality,…
Eric Siegel’s “The AI Playbook” serves as a crucial guide, offering important insights for data professionals and their internal customers on effectively leveraging AI within business operations. The book, which comes out on February 6th, and its insights are captured in six statements: — Determine the value— Establish a prediction goal— Establish evaluation metrics— Prepare…
