Securing Data in Transit for Analytics Operations

Most enterprises today store and process vast amounts of data from various sources within a centralized repository known as a data warehouse or data lake, where they can analyze it with advanced analytics tools to generate critical business insights.  Modern data warehouse platforms such as Snowflake, AWS Redshift, Azure Synapse Analytics, and IBM Db2 are built with…

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Demystifying AI: What Is AI and What Is Not AI?

In recent months, particularly following the release of ChatGPT, there has been an unprecedented surge in interest surrounding artificial intelligence (AI). This heightened attention spans across a multitude of sectors, including business enterprises, technology companies, venture capital firms, universities, governments, media outlets, and more. As the interest in AI is intensifying, some companies have even…

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Beyond the Basics: Advanced Tips for Effective Data Extraction

Data extraction is a cornerstone in data analytics, enabling organizations to extract valuable insights from raw data. While basic extraction techniques are fundamental, understanding advanced strategies is crucial for maximizing efficiency and accuracy. This article will explore advanced tips for effective data extraction, shedding light on automation tools, leveraging APIs and web scraping techniques, enhancing…

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RWDG Webinar: How Generative AI and LLMs Shape Data Governance 

Download the slides here>> https://content.dataversity.net/rs/656-WMW-918/images/May24_RWDG_Slides.pdf?version=0 About the Webinar Dive into the cutting-edge world of Data Governance by spending this hour focused on the impact generative AI and large language models (LLMs) are having, and will have, on Data Governance implementations. However, this addresses only one side of the relationship.   In this webinar, Bob Seiner will…

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The End of Agile – Part 5 (Misapplications of Agile)

In the 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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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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A Brief History of Data Quality

The term “Data Quality” focuses primarily on the level of accuracy possessed by the data, but also includes other qualities such as accessibility and usefulness. Some data isn’t accurate at all, which, in turn, promotes bad decision-making. Some organizations promote fact-checking and Data Governance, and, as a consequence, make decisions that give them an advantage….

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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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Data Intelligence: The Key to Empowered People and Decisions

McKinsey analysts predict that enterprise employees will rely on data for almost every decision come 2025. If true, this development would mark a significant departure from the current business modus operandi. According to our research, only 25% of enterprise data professionals believe their organization’s decision-making process is data-backed or strategic.  How are these two concepts – the perception of data…

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How to Become a Data Product Manager

Becoming a data product manager means taking responsibility for the development and management of data products. Broadly speaking, a data product is any software or algorithms that use data to accomplish a goal. The data product manager is a management position, and requires several years of experience within the data industry to be done well. …

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