IBM watsonx.governance

Score9.1 out of 10

16 Reviews and Ratings

What is IBM watsonx.governance?

The more AI is embedded into daily workflows, the more proactive governance is required to drive responsible, ethical decisions across the business. Watsonx.governance is used to direct, manage, and monitor an organization’s AI activities, and employs software automation to strengthen the user's ability to mitigate risk, manage regulatory requirements and address ethical concerns without the excessive costs of switching data science platforms—even for models developed using third-party tools.

Categories & Use Cases

AI Governance

#1 most frequent

Professional, Scientific, and Technical Services

25.8%

115 installations of 446

#2 most frequent

Finance and Insurance

19.3%

86 installations of 446

#3 most frequent

Information

13.7%

61 installations of 446

Harshal Pachpande View profile

Analyst in Information Technology at Secureview (501-1000 employees employees)

Use Cases and Deployment Scope

As an Analyst working with enterprise enviroment that are rapidly growing and developing AI and ML models wherein IBM watsonx.governance has been usefull platform for managing AI model governance complaince and risks oversights unlike the tradtional tools this tool focuses on end to end lifecycle of the AI model to its risk tracking and its auditability

Pros

Automated monitoring for MODEL HEALTH and BIAS this platform provides tools to continuously asses deployed models for accuracy raisness and potential biases by setting up predefined thresholds we can automate responses to the deviations
Awsome support to integrations with various AI providers

Cons

Navigation challenges Some of the features of the GUI that can be complex for the new users the intercae can be more interactive the live dashboards
Model tracking and its synchronization issues

Return on Investment

Significant reduction in manual effort during internal and external audits security and complaince teams can equally and quickly demonstrate adherence to corporate AI Policies to the auditors
Mitigation on financial legal and reputations risks that could arise from faulty ai models

Sarthak Chopra View profile

Specialist in Research & Development at Gartner (10,001+ employees employees)

Use Cases and Deployment Scope

So basically we are using it to keep our AI models transparent and safe. We use this with wastsonx.ai which we leverage for creating a lot of AI models and chatbots. AI is in the limelight everywhere and we want to ensure that the models we deploy give unbiased results. IBM watsonx.governance doing a pretty well job out there.

Pros

It can flag any biased patterns in AI (a really important feature)
Generate audit reports
Ensures that models are using only approved datasets.

Cons

Connecting with tools outside Ibm, we tried once, can be challenging
Real time can be quicker although it works great
Perhaps interface, if we count it.

Return on Investment

Better accuracy of AI models
Reduction in the biasness
Data integrity

Other Software Used

IBM watsonx.ai, IBM Cloud Pak for Data

Tomi Moolan Souru

Tech Lead in Engineering at General Motors (501-1000 employees employees)

Use Cases and Deployment Scope

A lot of issues and financial loss occurs due to improper governance. The govt. fine is huge due to problems in governance. Last year itself paid more than millions

Pros

Monitoring
AI incorporated to see edge cases

Cons

MCP server process takes bit log
Need to customize to fit into our orgs
lack of extention capabiities

Return on Investment

Save good financial profit
Licencing cost

Other Software Used

CoPilot AI, IntelliJ IDEA, Glean AI

Verified User

Manager in Information Technology (501-1000 employees employees)

Use Cases and Deployment Scope

Our scope of use is wide but deliberate. At first, I introduced it through a pilot in our scrap metal forecasting project, which predicts how much reusable steel can be reintroduced into prod each quarter. There were other multiple layers involved before running the outputs through IBM watsonx.governance to enforce governance on the data lineage. Since then, we've expanded it into supply chain risk modelling.

Pros

The policy management rules are my strongest use case. I set policy rules around our models all the time with no issues
I was also surprised by how good the metadata enrichment feature is

Cons

Right now, we have to use third party script connectors to leverage it alongside our Siemens Opcenter

Return on Investment

It has massively cut down the time our compliance teams spent on preparing compliance packs for EU emissions report. We're talking 4 weeks of manual tracing and spreadsheet validations to just under 3 days now!
IBM watsonx.governance flags anomalies in shipping data 2 weeks earlier than our older system, saving us thousands by renegotiating contracts before spot prices rise

Other Software Used

TeamViewer, CoPilot

Verified User

Engineer in Product Management (501-1000 employees employees)

Use Cases and Deployment Scope

used for data quality and data governance for the data analytics project in the data engineering and data science engineering for the forecast and build models

Pros

data quality
data check
data governance
ease of user

Cons

make it integrate with some of the 3rd party tools
simplify to non-tech users
make it less expensive compared to micrsoft tools

Return on Investment

improved data efficiency
less need for QA testing
no need to monitoring tools

Alternatives Considered

Azure AI Document Intelligence and AWS Auto Scaling

Other Software Used

Microsoft Purview Data Governance, erwin Data Governance, Infor Governance, Risk and Compliance

Tealium Customer Data Hub

Oracle Enterprise Manager

SAP Master Data Governance

IBM watsonx.governance

What is IBM watsonx.governance?

Categories & Use Cases

Most Frequent Users

Professional, Scientific, and Technical Services

Finance and Insurance

Information