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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ADV Webinar: What The? Another Database Model — Vector Databases Explained

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…

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Granularity Is the True Data Advantage

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,…

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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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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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How to Become a Data Engineer

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,…

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The AI Playbook: Providing Important Reminders to Data Professionals

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…

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2023 DATAVERSITY Top 20

As the year draws to a close, we here at DATAVERSITY have an annual tradition of digging deep into our data and reflecting on the hits and misses. What was the most popular Data Management content on dataversity.net and our training center over the past 12 months? Which core topics did you – our data…

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SambaNova Systems Demo: Generative AI Models – Go Big or Go Home

Download the slides here>> Small Generative AI models were first introduced last November. A year out, where are we in the evolution toward Pervasive AI? Spoiler alert: big. We’re going big. What can you do with a 5 trillion parameter model? We’ll give you food for thought in a demo of SambaNova Suite. How can…

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