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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. First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt.

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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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A comparative assessment of digital transformation in Italy

CIO Business Intelligence

We started with an evolution of the CRM to manage the citizen relationship, and the various requests and reports: those who come into contact with the AMA must be recognized on any channel and receive consistent answers in a multichannel perspective,” he says. From there, the actual digitization project can be implemented. “We

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform. Flat files – Other systems supply data in the form of flat files of different formats. Data ingestion Data from various sources are grouped into two major categories: real-time ingestion and batch ingestion.

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The New Normal for FP&A: Data Analytics

Jedox

Other challenges to data analytics include data storage, data quality, and a lack of knowledge and tools necessary to make sense of the data and generate those critical insights. Limited self-service reporting across the enterprise. Self-service reporting. Exception reporting.

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Data Architecture and Strategy in the AI Era

Cloudera

But, even with the backdrop of an AI-dominated future, many organizations still find themselves struggling with everything from managing data volumes and complexity to security concerns to rapidly proliferating data silos and governance challenges. The benefits are clear, and there’s plenty of potential that comes with AI adoption.