Kateřina Ščavnická

Data Governance in the Era of AI

An $18,000/month data warehouse confidently delivered wrong answers. The problem wasn't the AI, but the data.

Data Governance in the Era of AI
#1about 5 minutes

The high cost and failure of ungoverned data

An initial strategy of collecting massive amounts of data led to high costs, complex queries, and AI models that produced incorrect results.

#2about 2 minutes

Establishing clear data ownership with data mesh

The data mesh philosophy makes data producers responsible for their data, creating clear ownership from domain systems to domain datasets.

#3about 2 minutes

Defining the six essential aspects of a data product

A true data product must have six key qualities: ownership, documentation, data quality checks, architecture, a data contract, and security.

#4about 2 minutes

Prioritizing data governance with a tier-based system

Classifying data products into three tiers based on business impact helps focus governance efforts on the most critical assets first.

#5about 1 minute

Using data contracts to manage critical tier one data

Data contracts are formal agreements between data producers and consumers that define responsibilities, SLAs, and procedures for handling issues with critical data.

#6about 1 minute

Overcoming the cultural challenge of data governance

Implementing data governance is not just a technical fix but a long-term cultural shift that requires changing the entire company's mindset around data.

#7about 2 minutes

Measuring the impact of data governance initiatives

Implementing governance dramatically reduced the number of tracked interactions, queries, and dashboards while improving data completeness and analyst productivity.

#8about 2 minutes

Enabling successful AI with a governed data foundation

With a governed data foundation in place, AI can be successfully used for semantic validation, anomaly detection, and powering a natural language query Slack bot.

#9about 1 minute

Why data governance is the key to unlocking value

The key takeaway is that more data often leads to more confusion, and implementing strong data governance is the only way to create clarity and value.

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Matching moments

Why data silos and lack of governance kill AI projects
11:11 MIN

Why data silos and lack of governance kill AI projects

Big Business, Big Barriers? Stress-Testing AI Initiatives.

Ensuring AI reliability with monitoring and data governance
35:33 MIN

Ensuring AI reliability with monitoring and data governance

Navigating the AI Revolution in Software Development

Prioritizing governance and data quality in GenAI products
33:55 MIN

Prioritizing governance and data quality in GenAI products

Building Products in the era of GenAI

Reimagining data governance as a business enabler
04:40 MIN

Reimagining data governance as a business enabler

Unlocking Value from Data: The Key to Smarter Business Decisions-

Q&A on AI adoption, tools, and challenges
37:57 MIN

Q&A on AI adoption, tools, and challenges

Navigating the AI Wave in DevOps

Managing internal risks from employee AI adoption
04:12 MIN

Managing internal risks from employee AI adoption

Tackling the Risks of AI - With AI

How executive micromanagement derails AI initiatives
16:46 MIN

How executive micromanagement derails AI initiatives

Big Business, Big Barriers? Stress-Testing AI Initiatives.

Building the right team and processes to deliver AI solutions
16:42 MIN

Building the right team and processes to deliver AI solutions

AI PowerPlay: Building High-Impact Teams & Transformative Solutions

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