DataRobot Blog

AUGUST 16, 2022

The survey asked companies how they used two overlapping types of tools to deploy analytical models: Data operations (DataOps) tools, which focus on creating a manageable, maintainable, automated flow of quality-assured data. If deployment goes wrong, DataOps/MLOps can even help solve the problem. Survey Questions.

DataOps 52

DataOps AI AI ML 52

Dataversity

DECEMBER 14, 2020

It was only a few years ago that BI and data experts excitedly claimed that petabytes of unstructured data could be brought under control with data pipelines and orderly, efficient data warehouses. But as big data continued to grow and the amount of stored information increased every […].

DataOps 59

DataOps Data Pipeline Data Warehouse Big Data 59

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 101

Data Silos Data Pipeline DataOps Business Intelligence 101

IBM Journey to AI blog

SEPTEMBER 5, 2024

Master data management (MDM) MDM tools keep an organization’s master data—such as customer, product or supplier information—consistent and up-to-date across systems and departments, preventing data silos and providing a unified view of critical data entities.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Alation

DECEMBER 13, 2022

So feckless buyers may resort to buying separate data catalogs for use cases like…. Data governance. For example, the researching buyer may seek a catalog that scores 6 for governance, 10 for self-service, 4 for cloud data migration, and 2 for DataOps (let’s call this a {6, 10, 4, 2} profile). Self-service.

DataOps 52

DataOps Data Governance Data Silos Data Scientist 52

Alation

OCTOBER 12, 2021

Enterprise data analytics integrates data, business, and analytics disciplines, including: Data management. Data engineering. DataOps. … In the past, businesses would collect data, run analytics, and extract insights, which would inform strategy and decision-making. Subscribe to Alation's Blog.

Analytics 52

Analytics Analytics Data Governance DataOps 52