Kateřina Ščavnická
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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