IBM Data Science in Practice

NOVEMBER 28, 2022

Insights from data gathered across business units improve business outcomes, but having heterogeneous data from disparate applications and storages makes it difficult for organizations to paint a big picture. How can organizations get a holistic view of data when it’s distributed across data silos?

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

IBM Journey to AI blog

OCTOBER 22, 2024

The data universe is expected to grow exponentially with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. This situation will exacerbate data silos, increase pressure to manage cloud costs efficiently and complicate governance of AI and data workloads.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

Precisely

JULY 12, 2024

However, simply having high-quality data does not, of itself, ensure that an organization will find it useful. That is where data integrity comes into play. Data quality is an essential subset of data integrity, but it is possible to have good data quality without also having data integrity.

Data Quality 105

Data Quality Data Governance Database Analytics 105

Precisely

FEBRUARY 2, 2023

This requires access to data from across business systems when they need it. Data silos and slow batch delivery of data will not do. Stale data and inconsistencies can distort the perception of what is really happening in the business leading to uncertainty and delay.

Data Pipeline 52

Data Pipeline Data Silos Database Data Observability 52

IBM Journey to AI blog

JULY 3, 2024

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata.

AI 45

AI AI Data Quality Database 45

Precisely

JULY 18, 2024

Here are four aspects of a data management approach that you should consider to increase the success of an architecture: Break down data silos by automating the integration of essential data – from legacy mainframes and midrange systems, databases, apps, and more – into your logical data warehouse or data lake.

Data Lakes 52

Data Lakes Data Warehouse Data Governance Machine Learning 52

Precisely

FEBRUARY 9, 2024

They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud. That approach assumes that good data quality will be self-sustaining.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69