IBM Journey to AI blog

OCTOBER 22, 2024

Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models. This process is known as data integration , one of the key components to improving the usability of data for AI and other use cases, such as business intelligence (BI) and analytics.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

IBM Journey to AI blog

APRIL 10, 2023

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

AI AI Data Governance Data Pipeline 103

phData

AUGUST 10, 2023

The implementation of a data vault architecture requires the integration of multiple technologies to effectively support the design principles and meet the organization’s requirements. The most important reason for using DBT in Data Vault 2.0 is its ability to define and use macros.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Precisely

MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. Instead of moving customer data to the processing engine, we move the processing engine to the data. Simply design data pipelines, point them to the cloud environment, and execute.

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

IBM Journey to AI blog

JULY 13, 2023

The more complete, accurate and consistent a dataset is, the more informed business intelligence and business processes become. With data observability capabilities, IBM can help organizations detect and resolve issues within data pipelines faster.

Data Quality 74

Data Quality Data Profiling Data Governance Machine Learning 74

DagsHub

AUGUST 23, 2024

TDWI Data Quality Framework This framework , developed by the Data Warehousing Institute, focuses on practical methodologies and tools that address managing data quality across various stages of the data lifecycle, including data integration, cleaning, and validation.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

ODSC - Open Data Science

DECEMBER 3, 2024

While the concept of data mesh as a data architecture model has been around for a while, it was hard to define how to implement it easily and at scale. Two data catalogs went open-source this year, changing how companies manage their data pipeline. The departments closest to data should own it.

Data Pipeline 40

Data Pipeline Data Governance Data Analyst Data Observability 40