Categories: FAANG

Do you know your data’s complete story?

Data is everywhere in a hybrid and multi-cloud world. Enterprises now have more data, more data tools, and more people involved in data consumption. This data proliferation has made it harder than ever before to trust your data: knowing where it came from, how it has changed, and who is using it. Data provenance is a complexity facing many clients engaged in data governance-related use cases. To help our clients overcome these challenges, we are pleased to announce our collaboration with MANTA to bring MANTA Automated Data Lineage for IBM Cloud Pak for Data to market.

MANTA Automated Data Lineage for IBM Cloud Pak for Data is a deep integration between MANTA’s end-to-end Data Lineage platform and Watson Knowledge Catalog on IBM Cloud Pak for Data. Data Lineage is an essential capability for modern data management and is a required aspect of regulatory compliance for many industries. Together with Watson Knowledge Catalog’s business friendly native data lineage, MANTA provides the most complete picture of technical, historical, and indirect data lineage.

How MANTA makes a difference

MANTA helps ease the amount of manual effort necessary for robust data lineage by providing scanners for the automated discovery of data flows in 3rd party tools such as Power BI, Tableau, and Snowflake. This information is then automatically scanned into Watson Knowledge Catalog’s Data Lineage UI and becomes available to view alongside the data quality, business terms, and other metadata previously available to Watson Knowledge Catalog users.

In addition to supporting the high-level summary view appropriate for many business users, clients can also dig deeper to see additional technical, historical, and indirect data lineage within MANTA’s Lineage Flow UI. Collectively this means that the addition of MANTA will provide quicker time to value not only through the automation of previously manual processes, but also through the ability to more rapidly answer questions about whether certain data is trustworthy.

A boost to your data fabric architecture

MANTA Automated Data Lineage for IBM Cloud Pak for Data will be available as an add-on to Watson Knowledge Catalog, further improving the ability of IBM’s data fabric solution to satisfy governance and privacy use cases. Surrounded by existing capabilities like consistent cataloging, automated metadata generation, automated governance, reporting and auditing assistance, MANTA Automated Data Lineage for IBM Cloud Pak for Data will help to bolster the data governance capability of IBM’s data fabric solution.

Of course, trust in data is important across every data fabric use case whether it happens to be building 360-degree views of customers or enabling trustworthy AI use cases. The multi-cloud data integration within the data fabric also helps connect the various data sources MANTA will be scanning. MANTA will simultaneously benefit and be benefited by the multiple data fabric entry points, that help customers on their data management strategy

What’s next?

The partnership with MANTA is just the beginning; we will continue to work closely to add more capabilities to MANTA Automated Data Lineage for IBM Cloud Pak for Data.

Until then, you can learn more about IBM’s data fabric solution by visiting our website, starting a trial, or kick-starting your project with the IBM data and AI elite team.

.

 

The post Do you know your data’s complete story? appeared first on Journey to AI Blog.

AI Generated Robotic Content

Recent Posts

The Surprising MacBook Neo Competitor You’ve Never Heard Of

In many ways, the HP OmniBook 5 is a better budget laptop than the MacBook…

21 mins ago

Tiny cameras in earbuds let users talk with AI about what they see

University of Washington researchers developed the first system that incorporates tiny cameras in off-the-shelf wireless…

21 mins ago

Update: Distilled v1.1 is live

We've pushed an LTX-2.3 update today. The Distilled model has been retrained (now v1.1) with…

23 hours ago

How to Implement Tool Calling with Gemma 4 and Python

The open-weights model ecosystem shifted recently with the release of the

23 hours ago

Structured Outputs vs. Function Calling: Which Should Your Agent Use?

Language models (LMs), at their core, are text-in and text-out systems.

23 hours ago

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts

This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation…

23 hours ago