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Data Governance

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Community Posts

Any recommendations for data governance software?

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What’s the best place to start when evolving data governance?

The data19%

The people43%

The processes21%

The policies12%

I wish I knew5%

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What do you qualify as "Big Data"? Does it have more to do with the amount of data or the type of data?

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I work for a company with geographically dispersed operations, and we are facing significant challenges with our data collection and reporting processes for HSE (Health, Safety, and Environment). We collect a variety of environmental data (e.g., water quality indexes, equipment training compliance) from multiple sites. Our current data collection application has critical governance gaps. When an indicator shows a deviation, we struggle to trace its origin because the system lacks a clear audit trail. We cannot confidently verify who entered the data or when, which undermines the reliability of our HSE indicators. Furthermore, our data collection forms are rigid. Any request to add or modify fields requires our development team, leading to significant delays and hindering our ability to adapt to new requirements quickly. Questions: 1. How do you ensure robust data governance for HSE indicators? What tools or best practices have you found effective? 2. Have you successfully empowered your business or HSE teams to create and modify data collection forms without developer dependency? If so, what low-code/no-code platforms or solutions do you recommend?

What does the blueprint look like at your org for a minimal viable organizational structure needed to run an AI governance program?

Do you think it is possible for a single data and analytics governance platform to meet all your governance needs?

Yes, but it doesn’t exist yet26%

Yes, and it already exists41%

No, but I wish24%

No, and I don’t think it needs to10%

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Data governance software is the best option for staying compliant.

Strongly agree7%

Agree71%

Neutral17%

Disagree3%

Strongly disagree

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Has anyone contrasted AWS Data Lakehouse vs DataBricks vs Snowflake any insights welcome across the pros and cons of these please!

Our company is considering passing the ISO 42001 (Artificial Intelligence Management System) certification. As a preparation step, we would like to start with a Gap assessment, and our business executive suggested using Modulos Agentic AI Governance Platform. It provides some methodology for fast assessment. Does anybody use it?

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What are your thoughts on O365 retention policies? Do you leave it up to users to purge their own data according to corporate policies or have you implemented a system defined policy that purges data automatically?

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Have there been any technologies or tools that helped you implement adaptive data and analytics governance?

In developing a data governance structure from the ground up, what are the first steps you would take?

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If an organization does not have an MDM platform and isn’t mastering product data well and they do not have a proper PIM… which do you address first? Or is there a scenario that both can be addressed in parallel?

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What are the standard Service level indicators used when creating SLA document for Data Engineering products? I assume indicators like when new data will be populated for data products (all the previous day business transaction will be available by 9am, all source transaction reflected with in 30 mins etc) and accuracy and quality would be important. Is there Industry standard Indicators used to represent data product SLA? It would be amazing someone could share what they are using.

I’m looking for current insights on CluedIn. Their product and pricing model make it appealing for a proof of concept for an MDM system. However, their sales practices including limited access to demos and technical resources give me the gut feeling they cannot deliver.

I've built this framework that my firm leverages for AI and Predictive Analytics Initiatives as a structure for AI adoption/governance

1. Looking for feedback on is anything missing, is it useful.
2. Sharing with the group to leverage for their own needs.

1. AI Strategy
Set Business Goals
Do Opportunity Assessment
Develop Strategy
Develop Roadmap

2. AI Readiness
Audit Data
Evaluate Infrastructure
Assess Organization readiness
Select Use Cases

3. Solution Design
Design Solution Architecture
Select Models
Build Models
Integration Strategy

4. Adoption and Change
Transform processes
Train users
Build Change Strategy
Develop CD/CI

5. Governance
Ethics and Compliance Policy
Model Monitoring and Tuning Framework
Ensure scalability
Design Ongoing support

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