Dataversity

APRIL 25, 2022

If data processes are not at peak performance and efficiency, businesses are just collecting massive stores of data for no reason. Data without insight is useless, and the energy spent collecting it, is wasted. The post Solving Three Data Problems with Data Observability appeared first on DATAVERSITY.

Data Observability 52

Data Observability Data Silos Data Quality Data Governance 52

Precisely

NOVEMBER 13, 2024

Challenges around data literacy, readiness, and risk exposure need to be addressed – otherwise they can hinder MDM’s success Businesses that excel with MDM and data integrity can trust their data to inform high-velocity decisions, and remain compliant with emerging regulations. Today, you have more data than ever.

Data Silos 64

Data Silos Data Observability Data Governance Data Quality 64

Precisely

JULY 12, 2024

Data integrity is based on four main pillars: Data integration : Regardless of its original source, on legacy systems, relational databases, or cloud data warehouses, data must be seamlessly integrated in order to gain visibility into all your data in a timely fashion.

Data Quality 105

Data Quality Data Governance Database Analytics 105

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

Precisely

MAY 16, 2024

To achieve trustworthy AI outcomes, you need to ground your approach in three crucial considerations related to data’s completeness, trustworthiness, and context. You need to break down data silos and integrate critical data from all relevant sources into Amazon Web Services (AWS).

AI 69

AI AI AWS Data Quality 69

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

Alation

DECEMBER 7, 2021

Alation and Soda are excited to announce a new partnership, which will bring powerful data-quality capabilities into the data catalog. Soda’s data observability platform empowers data teams to discover and collaboratively resolve data issues quickly. Good Data is Just a Search Away. Unified Teams.

Data Quality 52

Data Quality Data Pipeline Data Silos Data Governance 52