Remove 2025 Remove Data Governance Remove Data Warehouse
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An Introduction to Metadata Management

Dataversity

According to IDC, the size of the global datasphere is projected to reach 163 ZB by 2025, leading to the disparate data sources in legacy systems, new system deployments, and the creation of data lakes and data warehouses. Most organizations do not utilize the entirety of the data […].

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How The Explosive Growth Of Data Access Affects Your Engineer’s Team Efficiency

Smart Data Collective

In fact, you may have even heard about IDC’s new Global DataSphere Forecast, 2021-2025 , which projects that global data production and replication will expand at a compound annual growth rate of 23% during the projection period, reaching 181 zettabytes in 2025. zettabytes of data in 2020, a tenfold increase from 6.5

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Optimizing data flexibility and performance with hybrid cloud 

IBM Journey to AI blog

Organizations are increasingly adopting hybrid cloud solutions that blend the strengths of private and public clouds, particularly beneficial in data-intensive sectors and companies embarking on AI strategy to fuel growth. The integration with established data warehouse engines ensures compatibility with existing systems and workflows.

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How IBM and AWS are partnering to deliver the promise of AI for business

IBM Journey to AI blog

The global AI market is projected to grow to USD 190 billion by 2025, increasing at a compound annual growth rate (CAGR) of 36.62% from 2022, according to Markets and Markets. Thus, DB2 PureScale on AWS equips this insurance company to innovate and make data-driven decisions rapidly, maintaining a competitive edge in a saturated market.

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How data stores and governance impact your AI initiatives

IBM Journey to AI blog

Accounting for the complexities of the AI lifecycle Unfortunately, typical data storage and data governance tools fall short in the AI arena when it comes to helping an organization perform the tasks that underline efficient and responsible AI lifecycle management. And that makes sense.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

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AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

The quality and quantity of data can make or break AI success, and organizations that effectively harness and manage their data will reap the most benefits. Data is exploding, both in volume and in variety. But it’s not so simple. With growth comes complexity.

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