Data Lifecycle Management focuses on data governance, data cleansing and quality, and data stewardship. The rubric applies to articles that focus primarily on the high-level preparation, flow, and use of data through an organization, rather than with one single facet such as storage or analysis.
Photo by Ernesto Andrade on Flickr Gartner’s April 2024 IT forecast estimated 8 percent growth from 2023 to 2024, forecasting $5.06 trillion in spending worldwide… Read More »Ten reasons organizations pay more in data integration tax every year
“Our goal is to redefine the interaction between technology and personal data. We envision an AI that is not just a tool used by the masses but an extension of the individual, respecting their privacy and enhancing their autonomy.” -Reza Rassool
Photo by Hermann Traub on Pixabay Losing control of your company’s data? You’re not alone To survive and thrive, data likes to be richly and… Read More »Losing control of your company’s data? You’re not alone
Roundtable Discussion with Rebecca Bultsma and Ahmad Jawad In the latest episode of the AI Think Tank Podcast, we ventured into the rapidly evolving intersection… Read More »Implementing AI in K-12 education
Presentation and discussion with Suman Aluru and Caleb Stevens In the latest episode of the “AI Think Tank Podcast,” I had the pleasure of hosting… Read More »Retrieval augmented fine-tuning and data integrations
Image by Ahmad Ardity from Pixabay The good news is that the data science community is taking more of an interest in knowledge graphs lately.… Read More »What data scientists overlook when it comes to knowledge graphs
Customer success stories illuminate how hardware accelerators speed necessary infrastructure to support all aspects of an accelerated AI and HPC computing datacenter.
Interview podcast with Rahul Pradhan, VP of Product and Strategy at Couchbase Operational and analytics systems are coming together with the help of new database… Read More »Operational, real-time edge analytics for developers
Modern data quality practices leverage advanced technologies, automation, and machine learning to handle diverse data sources, ensure real-time processing, and foster collaboration across stakeholders. They… Read More »Difference between modern and traditional data quality
Introduction Data warehouses are the linchpins of modern data infrastructure, integral for businesses that rely on informed decision-making. They act as centralized repositories where diverse… Read More »Future-proofing advanced data warehouses
