Remove Data Processing Remove Data Quality Remove Testing
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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.

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The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

Testing 304
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7 types of tech debt that could cripple your business

CIO Business Intelligence

Data debt that undermines decision-making In Digital Trailblazer , I share a story of a private company that reported a profitable year to the board, only to return after the holiday to find that data quality issues and calculation mistakes turned it into an unprofitable one.

Risk 140
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The future of data: A 5-pillar approach to modern data management

CIO Business Intelligence

Manish Limaye Pillar #1: Data platform The data platform pillar comprises tools, frameworks and processing and hosting technologies that enable an organization to process large volumes of data, both in batch and streaming modes. Implementing ML capabilities can help find the right thresholds.

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Accomplish Agile Business Intelligence & Analytics For Your Business

datapine

Your Chance: Want to test an agile business intelligence solution? Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. You need to determine if you are going with an on-premise or cloud-hosted strategy. Finalize testing. Train end-users.

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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes.

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Governing data in relational databases using Amazon DataZone

AWS Big Data

As you experience the benefits of consolidating your data governance strategy on top of Amazon DataZone, you may want to extend its coverage to new, diverse data repositories (either self-managed or as managed services) including relational databases, third-party data warehouses, analytic platforms and more.

Metadata 107