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 95

AI AI Data Governance Data Observability 95

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 Observability Data Pipeline 52

IBM Journey to AI blog

DECEMBER 16, 2022

The solution also helps with data quality management by assigning data quality scores to assets and simplifies curation with AI-driven data quality rules. AI recommendations and robust search methods with the power of natural language processing and semantic search help locate the right data for projects.

Data Quality 52

Data Quality Data Governance Data Wrangling Data Scientist 52

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

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 69

Data Quality Data Profiling Data Governance Machine Learning 69