Remove Data Architecture Remove Data Quality Remove Unstructured Data
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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Are enterprises ready to adopt AI at scale?

CIO Business Intelligence

The path to achieving AI at scale is paved with myriad challenges: data quality and availability, deployment, and integration with existing systems among them. Another challenge here stems from the existing architecture within these organizations.

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Through the Looking Glass: What Does Data Quality Mean for Unstructured Data?

TDAN

We have lots of data conferences here. I’ve taken to asking a question at these conferences: What does data quality mean for unstructured data? Over the years, I’ve seen a trend — more and more emphasis on AI. This is my version of […]

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio. They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics.

Data Lake 119
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What Separates Hybrid Cloud and ‘True’ Hybrid Cloud?

Cloudera

More than that, though, harnessing the potential of these technologies requires quality data—without it, the output from an AI implementation can end up inefficient or wholly inaccurate. Meaningful results, and a scalable, flexible data architecture demand a ‘true’ hybrid cloud approach to data management.

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What is a data engineer? An analytics role in high demand

CIO Business Intelligence

Data engineers and data scientists often work closely together but serve very different functions. Data engineers are responsible for developing, testing, and maintaining data pipelines and data architectures. Data engineer vs. data architect.

Analytics 131
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Demystifying Modern Data Platforms

Cloudera

Mark: While most discussions of modern data platforms focus on comparing the key components, it is important to understand how they all fit together. The high-level architecture shown below forms the backdrop for the exploration. Luke: Let’s talk about some of the fundamentals of modern data architecture.