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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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Making OT-IT integration a reality with new data architectures and generative AI

CIO Business Intelligence

A number of industry leaders are already experimenting with advanced AI use cases, including Denso, a leading mobility supplier that develops advanced technology and components for nearly every vehicle make and model on the road today. Denso uses AI to verify the structuring of unstructured data from across its organisation.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Therefore, there are several roles that need to be filled, including: DQM Program Manager: The program manager role should be filled by a high-level leader who accepts the responsibility of general oversight for business intelligence initiatives. The program manager should lead the vision for quality data and ROI.

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How to Build a Successful Metadata Management Framework

Alation

This is where metadata, or the data about data, comes into play. Having a data catalog is the cornerstone of your data governance strategy, but what supports your data catalog? Your metadata management framework provides the underlying structure that makes your data accessible and manageable.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Traditionally, such a legacy call center analytics platform would be built on a relational database that stores data from streaming sources. Data transformations through stored procedures and use of materialized views to curate datasets and generate insights is a known pattern with relational databases.

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Data platform trinity: Competitive or complementary?

IBM Big Data Hub

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

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The Modern Data Stack Explained: What The Future Holds

Alation

A typical modern data stack consists of the following: A data warehouse. Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. How Did the Modern Data Stack Get Started? How Can I Build a Modern Data Stack?