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Five Modern Data Architecture Trends

David Menninger's Analyst Perspectives

I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.

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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

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Enabling AI-powered business intelligence across the enterprise

IBM Big Data Hub

Essential data is not being captured or analyzed—an IDC report estimates that up to 68% of business data goes unleveraged—and estimates that only 15% of employees in an organization use business intelligence (BI) software.

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Data architecture strategy for data quality

IBM Big Data Hub

But there’s another factor of data quality that doesn’t get the recognition it deserves: your data architecture. How the right data architecture improves data quality. What does a modern data architecture do for your business? Reduce data duplication and fragmentation.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

While it’s always been the best way to understand complex data sources and automate design standards and integrity rules, the role of data modeling continues to expand as the fulcrum of collaboration between data generators, stewards and consumers. So here’s why data modeling is so critical to data governance.

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Data democratization: How data architecture can drive business decisions and AI initiatives

IBM Big Data Hub

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

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Lean Data Governance Strategies

TDAN

The goal of data governance is to ensure the quality, availability, integrity, security, and usability within an organization. Many traditional approaches to data governance seem to struggle in practice; I suspect it is partly because of the cultural impedance mismatch, but also partly because […].