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Data governance use cases – 3 ways to implement

Collibra

Data lake management: Prevent a data swamp. A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured and unstructured data. The data structure and requirements are not defined until the data is needed.

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Integrating Structured and Unstructured Data; Are we there already?

Everteam

“By 2022, 50% of organizations will include unstructured, semistructured and structured data within the same governance program, up from less than 10% today.” Gartner Market Guide for File Analytics. Much of the data we manage today is semi-structured, so why have separate solutions to manage each one?

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Synthetic data generation: Building trust by ensuring privacy and quality

IBM Big Data Hub

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

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You May Soon Be Told to “Go Jump in a Lake” for Your ESI: eDiscovery Trends

eDiscovery Daily

A data lake is an architecture for storing high-volume, high-velocity, high-variety, as-is data in a centralized repository for Big Data and real-time analytics. And the technology is an attention-getter: The global data lakes market is expected to grow at a rate of 28 percent between 2017 and 2023.

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Seamlessly discover and extract metadata from your ERP and CRM systems

Collibra

Your organization has invested heavily in these systems, and they continuously generate valuable operational and transactional data. This data is essential to power analytics and support critical business decisions. You also need to be fully aware of the data structures of data catalogs to ensure correct mapping.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. appeared first on IBM Blog.

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The business value of operating core insurance solutions on the cloud

IBM Big Data Hub

To accelerate speed-to-market, grow the business with new innovative products and services, gain new and deeper risk insights, and improve customer experience, most companies are also emphasizing digital transformation. Private equity has entered this market and set up consolidator subsidiaries.