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Kyligence Empowers Ping An Bank to Build a Centralized Metrics Platform
With Kyligence’s AI-augmented modeling capabilities, Ping An Bank’s Pandora metrics management and application platform has automated the metric creation and management throughout the lifecycle, greatly improving our data development capacity and data governance. The platform makes it easier for us to analyze data and get business insights while serving as a middle platform for other data-dependent systems within the bank.
CTO of Retail Big Data Platforms , Ping An Bank

Building a Centralized Metrics Platform

Building a Centralized Metrics Platform

Cutting-edge technologies are the driving force behind the transformation of digital banking. With the core technologies of AI, big data, and cloud computing, Ping An Bank always embraces innovations to drive decision-making and financial services for digital and intelligent business operations and management. For Ping An Bank, the key to empowering the business with data is to make data easily accessible to users.

Pandora Metrics Platform - One-Stop Data Services

Pandora Metrics Platform - One-Stop Data Services

Ping An Bank believes metric governance is the top priority for making data easily accessible. Metrics are the most important data assets of the company. Kyligence meets the demands for metrics processing and query via distributed computing and storage. By leveraging an AI-augmented engine, it supports intelligent computation and O&M, greatly reducing labor costs and meeting the needs for multi-subject analysis.

Achievements and Customer Feedback

As a result of one year’s effort, the platform has been equipped with middle-platform capabilities centered on the build service, the query service, data governance, and AI capability.

Build service

Automatically builds views for data queries and Kyligence cubes, and provides intelligent task management and resource queues to automate the building of metric models

Query service

Supports asynchronous queries, active/passive caching, down-grade pushdowns, and intelligent aggregation, to provide quick response and flexible analytics for large amounts of data and multidimensional queries

Data governance

Governs metrics, dimensions, and data timeliness to improve data standardization, reduce the ambiguity of metrics and dimensions, and improve data management, enabling “governance while using” data

AI capability

Oriented at business scenarios, focused on metric application, and integrates algorithms for intelligent early warnings, rule-based early warning, intelligent attribution, and metric recommendation, to help users quickly pinpoint problems and perform analytics

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