Data Governance

42 posts about Data Governance. Every summary links to the original.

Databricks · Apr 14, 2026
8 AI and data trends shaping financial services in 2026
The post argues that financial-services AI adoption is widespread, but execution—not model capability or strategy—is determining who captures value in 2026. It attributes stalled pilots to fragmented legacy infrastructure, inconsistent data, weak lineage, and insufficient control for governed, real-time workflows such as fraud detection, pricing, and personalization. Firms advancing further treat data as a managed asset, embed governance in data and model pipelines, and align data, analytics, and AI teams around shared definitions, workflows, and metrics. The proposed remedy is a unified lakehouse environment combining storage, compute, governance, lifecycle management, orchestration, streaming, and AI workflows, with Unity Catalog providing centralized access control, lineage, and auditing. The conclusion is that by the end of 2026, firms that embed AI into operational decisioning at scale will pull ahead of organizations still running pilots.
Kim Hatton, Junta Nakai, Marcela Granados, Antoine Amend, Ashraf Safdar, Jennifer Miller, Andrea DeSosa, Rajaram Suresh
Grab · Nov 14, 2024
Metasense V2: Enhancing, improving and productionisation of LLM powered data governance
Metasense V2 addresses automated data governance by improving an LLM-powered metadata generation and classification system. It builds on an initial rollout that scanned more than 20,000 data entries at 300–400 entities per day, but retained human verification to catch misclassifications. Post-rollout owner feedback and manual classifications from the Data Governance Office supplied training and testing data, exposing failures involving PII in business email columns, nested JSON, and passenger communications. The revised approach splits PII and non-PII tagging, reduces the PII tag set from 21 to 8, shortens the prompt from 1,254 to 737 words, and divides tables exceeding 150 columns. LangChain and LangSmith support prompt development, evaluation across accuracy, latency, and error rate, deployment, and monitoring, while threshold-based alerts trigger a model-improvement protocol when misclassification rates rise.
Nick Buhrer

8 AI and data trends shaping financial services in 2026

The post argues that financial-services AI adoption is widespread, but execution—not model capability or strategy—is determining who captures value in 2026. It attributes stalled pilots to fragmented legacy infrastructure, inconsistent data, weak lineage, and insufficient control for governed, real-time workflows such as fraud detection, pricing, and personalization. Firms advancing further treat data as a managed asset, embed governance in data and model pipelines, and align data, analytics, and AI teams around shared definitions, workflows, and metrics. The proposed remedy is a unified lakehouse environment combining storage, compute, governance, lifecycle management, orchestration, streaming, and AI workflows, with Unity Catalog providing centralized access control, lineage, and auditing. The conclusion is that by the end of 2026, firms that embed AI into operational decisioning at scale will pull ahead of organizations still running pilots.

Kim Hatton, Junta Nakai, Marcela Granados, Antoine Amend, Ashraf Safdar, Jennifer Miller, Andrea DeSosa, Rajaram Suresh

Metasense V2: Enhancing, improving and productionisation of LLM powered data governance

Metasense V2 addresses automated data governance by improving an LLM-powered metadata generation and classification system. It builds on an initial rollout that scanned more than 20,000 data entries at 300–400 entities per day, but retained human verification to catch misclassifications. Post-rollout owner feedback and manual classifications from the Data Governance Office supplied training and testing data, exposing failures involving PII in business email columns, nested JSON, and passenger communications. The revised approach splits PII and non-PII tagging, reduces the PII tag set from 21 to 8, shortens the prompt from 1,254 to 737 words, and divides tables exceeding 150 columns. LangChain and LangSmith support prompt development, evaluation across accuracy, latency, and error rate, deployment, and monitoring, while threshold-based alerts trigger a model-improvement protocol when misclassification rates rise.

Nick Buhrer