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Most Corporate Data Remains Centralized and Non-Cloud: Forrester Report

The analysts at Forrester Consulting might have liked the bread-slicing machine, but they’re not impressed with most companies’ cloud data migrations.  This study for Capital One finds that nearly three-quarters of companies have yet to move the most of their data to the cloud.  The most common obstacle is unpredictable costs due to usage-based cloud pricing models (82%), followed closely by data quality (80%), cataloging (78%), understanding data (76%), and observability (74%).  Old habits die hard: more than half (56%) say their data management is centralized, 19% say it’s decentralized, 15% say it’s federated, and 9% say it’s outsourced.

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Teradata Launches Cloud-Native Data Lake

August 31, 2022

Are cloud databases the best thing since sliced bread?  Many people think so, although I’d guess most are too young to remember actual unsliced bread.  For the record, so am I, although I do recall the bread slicing machine in my uncle’s bakery, which made a jiggling motion while set of parallel blades descended through the loaf.  Very cool, but I digress.  The news here is that Teradata has launched a new cloud-native database to compete with the likes of Snowflake and Databricks.

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Cost, Self-Service Drive Unstructured Data to Cloud: Komprise Survey

August 31, 2022

Komprise takes a sunnier view of cloud migration, reporting that on-premises-only environments fell from 20% to 12% since last year, although 50% still use a mix of cloud and non-cloud.  Two-thirds of the companies spend more than 30% of their IT budget on data storage and protection, so it’s not surprising that cutting costs is the main reason for cloud migration.  Better news is that self-service data access and greater agility are the next-most-common reasons.  This matters to marketers because user data is the most common type of unstructured data.

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Meta Releases Lying, Offensive AI and Pretends to Be Surprised

November 23, 2022

Like trouble, bad behavior by Meta shows up whether you look for it or not.  The latest is an open-source language model that was supposed to provide reliable search results because it was trained on academic papers.  Alas, it was quickly withdrawn after reviewers found that it returned results that were grammatical and plausible but also incorrect, not to mention filled with “antisemitism, homophobia, and misogyny.”  How can this be a surprise?

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