Remove 2019 Remove Data Models Remove Data Quality
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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Analytics 203
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Data Integrity: The Foundation for Trustworthy AI/ML Outcomes and Confident Business Decisions

ODSC - Open Data Science

These are critical steps in ensuring businesses can access the data they need for fast and confident decision-making. As much as data quality is critical for AI, AI is critical for ensuring data quality, and for reducing the time to prepare data with automation. How does this all tie into AI/ML?

ML 98
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At the Top of Everyone’s List

Alation

IDC Innovators: Data Intelligence Software Platforms, 2019 Report. In the latest IDC Innovators: Data Intelligence Software Platforms, 2019 3 report, Alation was profiled as one vendor disrupting the data integration and integrity software market with a differentiated data intelligence software platform.

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What Data Practitioners Need to Know (and Do) About Common Language

Dataversity

Weinberg [1] In March 2019, one of us (Thomas C. Redman) served as the judge in a mock trial of a data architect (played by Laura Sebastian Coleman) […]. The post What Data Practitioners Need to Know (and Do) About Common Language appeared first on DATAVERSITY.

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Data Fabric & Data Mesh: Two Approaches, One Data-Driven Destiny

Heartbeat

Data should be designed to be easily accessed, discovered, and consumed by other teams or users without requiring significant support or intervention from the team that created it. Data should be created using standardized data models, definitions, and quality requirements. What is Data Mesh? How does it?

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Best Machine Learning Datasets

Flipboard

Nowadays, with the advent of deep learning and convolutional neural networks, this process can be automated, allowing the model to learn the most relevant features directly from the data. Model Training: With the labeled data and identified features, the next step is to train a machine learning model.