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What companies get wrong about data transformation

CIO Business Intelligence

For years, IT and data leaders have been striving to help their companies become more data driven. But technology investment alone is not enough to make your organization data driven. A lot of organizations have tried to treat data as a project,” says Traci Gusher, EY Americas data and analytics leader. “It

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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Data Mesh 101: How Data Mesh Helps Organizations Be Data-Driven and Achieve Velocity

Ontotext

In the final part of this three-part series, we’ll explore ho w data mesh bolsters performance and helps organizations and data teams work more effectively. Usually, organizations will combine different domain topologies, depending on the trade-offs, and choose to focus on specific aspects of data mesh.

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Drive Growth with Data-Driven Strategies: Introducing Zenia Graph’s Salesforce Accelerator

Ontotext

In today’s data-driven world, businesses are drowning in a sea of information. Traditional data integration methods struggle to bridge these gaps, hampered by high costs, data quality concerns, and inconsistencies. Unleashing the Power of Data Connections Zenia Graph isn’t just another data solution company.

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Choosing A Graph Data Model to Best Serve Your Use Case

Ontotext

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. Knowledge graphs model knowledge of a domain as a graph with a network of entities and relationships.

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Building Better Data Models to Unlock Next-Level Intelligence

Sisense

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. The goal of DataOps is to help organizations make better use of their data to drive business decisions and improve outcomes.