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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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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. The past decades of enterprise data platform architectures can be summarized in 69 words. Introduction to Data Mesh. Source: Thoughtworks.

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How Knowledge Graphs Power Data Mesh and Data Fabric

Ontotext

Bad data tax is rampant in most organizations. Currently, every organization is blindly chasing the GenAI race, often forgetting that data quality and semantics is one of the fundamentals to achieving AI success. Sadly, data quality is losing to data quantity, resulting in “ Infobesity ”. “Any

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Demystifying Modern Data Platforms

Cloudera

The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.

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Erwin Data Intelligence: A Data Partner’s Perspective

erwin

While the essence of success in data governance is people and not technology, having the right tools at your fingertips is crucial. Technology is an enabler, and for data governance this is essentially having an excellent metadata management tool. Next to data governance, data architecture is really embedded in our DNA.

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What is Data Mesh?

Ontotext

Data mesh solves this by promoting data autonomy, allowing users to make decisions about domains without a centralized gatekeeper. It also improves development velocity with better data governance and access with improved data quality aligned with business needs.

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How Metadata Makes Data Meaningful

erwin

Here are some benefits of metadata management for data governance use cases: Better Data Quality: Data issues and inconsistencies within integrated data sources or targets are identified in real time to improve overall data quality by increasing time to insights and/or repair. by up to 70 percent.