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What Tools Do You Need To Manage Unstructured Data?

Smart Data Collective

Unstructured data represents one of today’s most significant business challenges. Unlike defined data – the sort of information you’d find in spreadsheets or clearly broken down survey responses – unstructured data may be textual, video, or audio, and its production is on the rise. Centralizing Information.

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8 tips for unleashing the power of unstructured data

CIO Business Intelligence

With organizations seeking to become more data-driven with business decisions, IT leaders must devise data strategies gear toward creating value from data no matter where — or in what form — it resides. Unstructured data resources can be extremely valuable for gaining business insights and solving problems.

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Streaming Edge Data Collection and Global Data Distribution

Cloudera

With the rapid increase of cloud services where data needs to be delivered (data lakes, lakehouses, cloud warehouses, cloud streaming systems, cloud business processes, etc.), controlling distribution while also allowing the freedom and flexibility to deliver the data to different services is more critical than ever. .

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Outdated business apps can cloud your AI vision

CIO Business Intelligence

The data retention issue is a big challenge because internally collected data drives many AI initiatives, Klingbeil says. With updated data collection capabilities, companies could find a treasure trove of data that their AI projects could feed on. of their IT budgets on tech debt at that time.

Insurance 108
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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 the leader in unstructured data storage, customers trust NetApp with their most valuable data assets.

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Cloudera - The ASEAN Appetite for Data in Motion

Corinium

The early days of Big Data were defined by building massive data stores, or data lakes of unstructured data that were searchable in ways and at speeds that were not previously possible.

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Data Mining vs Data Warehousing: 8 Critical Differences

Analytics Vidhya

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Both are associated with data usage but differ from each other.