Remove Business Intelligence Remove Data Governance Remove Data Observability
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DataOps

Dataconomy

Data transformation: The process of converting raw data into useful formats that serve analytical and operational purposes. Data extraction: This includes gathering data from various sources to integrate into a cohesive dataset. Feedback loops: Incorporating validation processes for data enhances credibility.

DataOps 91
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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Trust ’25 Recap: The Latest in AI, Modernization, and Location Intelligence

Precisely

Precisely CTO, Tendü Yoğurtçu, PhD , sat down with Elia’s Cédric Charlier, Head of Data & Business Intelligence to discuss how the company harnesses AI for everything from document abstraction to predictive maintenance using drones and image recognition. A top takeaway?

AI 72
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How the right data and AI foundation can empower a successful ESG strategy

IBM Journey to AI blog

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

AI 103
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Data Democratization 101

Precisely

Data democratization has become a hot topic lately with advances in technology such as AI and machine learning, cloud storage and scalable server capacity, and improved integration. Then add self-service business intelligence tools that are accessible to virtually anyone.

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Data integrity vs. data quality: Is there a difference?

IBM Journey to AI blog

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches.

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Five benefits of a data catalog

IBM Journey to AI blog

And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.