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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

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How Metadata Makes Data Meaningful

erwin

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

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You Cannot Get to the Moon on a Bike!

Ontotext

Next, I will explain how knowledge graphs help them to get a unified view to data derived from multiple sources and get richer insights in less time. This requires new tools and new systems, which results in diverse and siloed data. And each of these gains requires data integration across business lines and divisions.

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How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

Cloudera

There’s also the risk of various forms of data leakage, including intellectual property (IP) as well as personally identifiable information (PII) especially with commercial AI solutions. That said, Generative AI and LLMs appear to do all of these things, producing original, “creative” outputs by learning from input data.

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Strategically Approaching Graph Technologies

Ontotext

The goal: a data-driven organization Let’s zoom out for a bit. Every organization wants to be data-driven. The goal is to capture data, convert it into the right insights, and integrate those insights quickly and efficiently into your business decisions and processes. And this includes the AI space.

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Are Data Silos Undermining Digital Transformation?

BI-Survey

The existence of data silos is nothing new. Data-producing applications were once isolated systems. The transactional data was stored in isolated data sets and initially served only one purpose, namely, to document the transaction that had taken place. Over time, enterprises realized that data is worth more.

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Modernizing the Data Warehouse: Challenges and Benefits

BI-Survey

Data warehousing is getting on in years. Concepts and architectures have been applied more or less unchanged since the 1990s. However, data warehousing and BI applications are only considered moderately successful. But what are the right measures to make the data warehouse and BI fit for the future?