IBM Journey to AI blog

JULY 3, 2024

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

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Precisely

FEBRUARY 2, 2023

Pipelines must have robust data integration capabilities that integrate data from multiple data silos, including the extensive list of applications used throughout the organization, databases and even mainframes. Changes to one database must also be reflected in any other database in real time.

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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. Let’s explore one of the methods for implementing near real-time (NRT) data vaults using Snowflake Continuous Data Pipelines.

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Dataversity

JUNE 19, 2023

In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.

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The MLOps Blog

JUNE 27, 2023

It integrates with Git and provides a Git-like interface for data versioning, allowing you to track changes, manage branches, and collaborate with data teams effectively. Dolt Dolt is an open-source relational database system built on Git. It could help you detect and prevent data pipeline failures, data drift, and anomalies.

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Precisely

MARCH 14, 2023

Simply design data pipelines, point them to the cloud environment, and execute. Enjoy a much more sophisticated computing environment than what’s available in a standard relational database – with data architectures based on tools like Databricks and Snowflake. What does all this mean for your business? Bigger, better results.

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Precisely

OCTOBER 5, 2023

When we think about the big picture of data integrity – that’s data with maximum accuracy, consistency, and context – it becomes abundantly clear why data enrichment is one of its six key pillars (along with data integration, data observability, data quality, data governance, and location intelligence).

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Precisely

OCTOBER 12, 2023

This includes understanding the impact of change within one data element on the various other data elements and compliance requirements throughout the organization. Creating data observability routines to inform key users of any changes or exceptions that crop up within the data, enabling a more proactive approach to compliance.

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Precisely

FEBRUARY 9, 2024

For instance, a data breach or violation of privacy standards can lead to liability, expensive fines, and a slew of negative publicity that’s a hit to brand reputation and trustworthiness. If inconsistencies and inaccuracies in the customer database can be fixed, the organization’s data analytics initiatives can presumably proceed.

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