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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. As you would guess, maintaining context relies on metadata.

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Are You Content with Your Organization’s Content Strategy?

Rocket-Powered Data Science

Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. This is accomplished through tags, annotations, and metadata (TAM). Data catalogs are very useful and important. Collect, curate, and catalog (i.e.,

Strategy 267
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Why Is Metadata Discovery Important? (+ 5 Use Cases)

Octopai

Unlike the rock collection or shell collection you may have had as a child, you don’t collect data in order to have a data collection. You collect data to use it. Data needs to be accompanied by the metadata that explains and gives it context. Powering automated data lineage.

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Rethinking informed consent

O'Reilly on Data

The problems with consent to data collection are much deeper. It comes from medicine and the social sciences, in which consenting to data collection and to being a research subject has a substantial history. We really don't know how that data is used, or might be used, or could be used in the future.

Insurance 242
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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO Business Intelligence

Managing the lifecycle of AI data, from ingestion to processing to storage, requires sophisticated data management solutions that can manage the complexity and volume of unstructured data. As customers entrust us with their data, we see even more opportunities ahead to help them operationalize AI and high-performance workloads.

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Deep automation in machine learning

O'Reilly on Data

Data management isn’t limited to issues like provenance and lineage; one of the most important things you can do with data is collect it. Given the rate at which data is created, data collection has to be automated. How do you do that without dropping data? Toward a sustainable ML practice.

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The Struggle Between Data Dark Ages and LLM Accuracy

Cloudera

It could be metadata that you weren’t capturing before. The final hurdle to LLM precision, available data Ray: But to get to a level of precision that your stakeholders are going to trust, there’s not enough data. And the value of the 10% is as much as the 85% and as much as the next 5% to get to 95%.