Peer-Driven Insights

Data Governance

About this topic

Data governance defines decision rights and creates an accountability framework to ensure appropriate valuation, creation, consumption and control of data and analytics.

Community Posts

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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Any recommendations for data governance software?

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How have you adjusted the architecture of your controls for data, identity and access to better support AI governance and security?

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?

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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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.

What are the key responsibilities of a governance manager or head of governance?

In relation to Data Governance, how did you start? Did you start this initiative with a platform or just with a new process?

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.

Those working with PCI DSS applicable data (payment card data): are file sharing services generally PCI compliant? Any recommendations/best practices for sharing files in such circumstances?

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 are your thoughts on the "Twitter Files" and how this might interact with data privacy regulations?

Which tools are you using, if any, to catalog non-tabular data assets such as video, audio, binary files, xml?

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Data governance software is the best option for staying compliant.

Strongly agree6%

Agree70%

Neutral18%

Disagree3%

Strongly disagree

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We are currently looking for a company based in Germany that specializes in performing a comprehensive database audit. The audit should primarily focus on redundancy, stability, and the overall infrastructure architecture of our mixed database landscape. The technologies involved include Microsoft SQL, Oracle SQL, PostgreSQL, and IBM DB2. If you have any recommendations or suggestions for companies that can effectively perform this audit for all the listed technologies, we would greatly appreciate your input.

Which method do you prefer for data storage?

Tape drives19%

SSD58%

NAS20%

Other (please comment below)

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