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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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Aiding Architecture & Engineering Firms with Data-Driven Learning

Smart Data Collective

Data analytics is incredibly valuable for helping people. More institutions are recognizing this, so the market for data analytics in education is projected to be worth over $57 billion by 2030. We have previously talked about the many ways that big data is disrupting education.

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Gen AI is a game-changer in bond investment risk assessment

CIO Business Intelligence

Their steady income stream and relatively low risk compared to equities make them an especially important component of pension and retirement planning. Each type of issuer presents a different level of risk and tax treatment. A, head of the data science practice at Hexaware. That can slash thousands of dollars in audit fees.

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A summary of Gartner’s recent DataOps-driven data engineering best practices article

DataKitchen

On 24 January 2023, Gartner released the article “ 5 Ways to Enhance Your Data Engineering Practices.” Or, as one of our customers put it, “How do I increase the total amount of team insight generated without continually adding more staff (and cost)?” Staff turnover, stress, and unhappiness. It’s not been going well.

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Re-imagining Business Workflows with AI-Powered Automation

CIO Business Intelligence

Tungsten Automation is delivering the benefits of AI-driven automation through a comprehensive suite of capabilities that can automate everything from low-value and repetitive data entry tasks through to highly complex actions such as risk analysis and fraud detection.

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Becoming a Data-Driven Organisation in 4 Steps

Alation

But what does it mean for an organisation to be truly data-driven? What foundation needs to be in place at the start, and what journey does an organisation need to embrace to benefit from the forensic insights their data can reveal? But let’s start with the benefits, because they are manifold.

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What Is Model Risk Management and How is it Supported by Enterprise MLOps?

Domino Data Lab

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. What Is Model Risk? Types of Model Risk.