How Financial Institutions Can Master Data‑Driven Transformation in 2024

Since 2003, the Chinese financial industry has experienced two full decades of data warehouse and big‑data development, moving from MPP technologies to Hadoop, then to cloud‑native storage‑compute separation and AI‑driven analytics. Despite this progress, many institutions still struggle with fragmented platforms, unclear data assets, poor data quality, and slow data delivery.

Alibaba Cloud has spent five years helping Alibaba Group build a unified data middle‑platform and has worked with hundreds of financial customers to define the technical and business value of data, analyze the data lifecycle, and suggest future cloud‑native compute strategies.

01 Challenges Facing the Financial Data Sector

Digital maturity is measured by how data moves from “following” business (post‑analysis) to “accompanying” (real‑time) and finally to “leading” (intelligent services). Some institutions excel, while others hinder business progress.

Key pain points include:

Data platforms cannot keep up with rapidly growing data volume, variety, and timeliness.

Data standards exist but are not enforced, leading to inconsistent metrics and metadata silos.

Insufficient data asset inventory—many reports, few reusable assets.

Low efficiency of data services; obtaining data can take months.

02 Breaking the Bottlenecks of Financial Data Development

Future financial firms will be “data‑driven” enterprises, shifting data’s role from historical record to real‑time guidance. A six‑dimensional reference model—top‑down design, business value, data services, governance, intelligent compute, and talent—guides capability building.

Key actions:

Adopt a top‑down blueprint that aligns data, business, technology, and organization.

Transform data services into a self‑service portal, moving from on‑demand to interactive usage.

Embed data standards throughout the production‑to‑consumption chain for full‑link governance.

Upgrade platform architecture to cloud‑native, multi‑compute fusion to handle massive near‑real‑time data and AI‑driven decisions.

Empower data product managers as the “breakthrough agents” of data‑driven business.

03 Core Views on Building Financial Data Capability

(1) Global‑Perspective Driving Force

A “global data view” framework consists of one blueprint, a 3+1 data system, and six core capabilities.

Blueprint: Top‑down design aligns data strategy with business goals, solving fragmented construction.

Three Drivers: Compute‑driven (storage‑compute separation, multi‑engine fusion), data‑driven (full‑domain data and governance), and value‑driven (digital‑operational “people‑goods‑place” model).

One Mechanism: Work‑target management, organizational support, and talent ecosystem.

Six Capabilities: Efficiency, cost reduction, quality, innovation, team strength, ecosystem.

(2) Layered Core Strength

The “3+1” data system includes digital infrastructure, digital assets, and digital applications, supported by operational safeguards.

Digital Infrastructure

Provides compute and storage for massive data, following five principles: cloud‑native extensibility, multi‑layer intelligent storage, unified scheduling, heterogeneous engine collaboration, and SRE‑style operations.

Digital Assets

Builds a full‑domain data asset pool, intelligent analysis, and diverse data services, delivering four core and four extended capabilities (acquire, build, manage, use).

Digital Applications

Implements a “people‑goods‑place” digital operation model, integrating business, data, and technology across units.

(3) Business‑View Value Chain

Data‑driven digitalization creates value across front‑office (customer acquisition), middle‑office (pricing, segmentation), back‑office (risk monitoring), data management (governance), and R&D (compute upgrades).

04 Success Factors for Financial Data Capability

Capital One’s founder noted that data is the strategic foundation of the company. Strong data capability yields higher marginal effects, easier business expansion, and finer product services.

(1) Key Capabilities & Value Directions

Focus on reducing data construction cost, achieving end‑to‑end integration and governance, providing one‑stop data development and services, delivering diverse user experiences, and cultivating data product managers.

(2) Critical Path & Success Elements

1) Assess data maturity and define a tailored data strategy. 2) Distinguish between data‑warehouse and data‑middle‑platform models to select the optimal architecture. 3) Plan the evolution path of traditional warehouses—migration, upgrade, or reconstruction—while ensuring compatibility and cost reduction. 4) Build a unified data service platform for secure, efficient, and scalable data delivery. 5) Strengthen organization‑wide data responsibility through governance. 6) Enable multi‑dimensional data asset sharing to unlock value.