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

IBM Journey to AI blog

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

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Big Data vs Data Warehouse

Marktechpost

With their own unique architecture, capabilities, and optimum use cases, data warehouses and big data systems are two popular solutions. The differences between data warehouses and big data have been discussed in this article, along with their functions, areas of strength, and considerations for businesses.

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How to accelerate your data monetization strategy with data products and AI

IBM Journey to AI blog

Data monetization strategy: Managing data as a product Every organization has the potential to monetize their data; for many organizations, it is an untapped resource for new capabilities. But few organizations have made the strategic shift to managing “data as a product.”

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A Beginner’s Guide to Data Warehousing

Unite.AI

In this digital economy, data is paramount. Today, all sectors, from private enterprises to public entities, use big data to make critical business decisions. However, the data ecosystem faces numerous challenges regarding large data volume, variety, and velocity. Enter data warehousing!

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Big Data as a Service (BDaaS): A Comprehensive Overview

Pickl AI

Summary: Big Data as a Service (BDaaS) offers organisations scalable, cost-effective solutions for managing and analysing vast data volumes. By outsourcing Big Data functionalities, businesses can focus on deriving insights, improving decision-making, and driving innovation while overcoming infrastructure complexities.

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Attivio Accelerates Business Intelligence and Big Data Projects with New Data Source Discovery Software

Attivio

Featuring self-service data discovery acceleration capabilities, this new solution solves a major issue for business intelligence professionals: significantly reducing the tremendous amount of time being spent on data before it can be analyzed.

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Data architecture strategy for data quality

IBM Journey to AI blog

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.