Kerangka ETL Berbasis Metadata untuk Standardisasi Data Keuangan

Kerangka ETL Berbasis Metadata untuk Standardisasi Data Keuangan

Dalam transformasi data, integrasi, dan pelaporan peraturan. Perusahaan bermitra dengan Artha... Artha Solutions mengatasi hal ini dengan menerapkan Untuk menerapkan kerangka kerja ETL berbasis metadata, memastikan standardisasi data yang lancar, peningkatan transparansi, dan efisiensi operasional.... menghasilkan Think Data, Think Artha. STUDI KASUS Kerangka ETL

IKHTISAR UTAMA
  • Industri BFSI
  • Daerah Fokus Data & Modernisasi AI
  • Klien Klien Perusahaan Besar
  • Wilayah Global
  • Teknologi
    Talend Integrasi Data

Hasil Utama & Dampak Bisnis

45%
Alur kerja otomatis mengurangi upaya manual sebesar 45%, sehingga mempercepat pelaporan dan kepatuhan.
30%
Penghentian sistem lama memangkas biaya sebesar 30%, sehingga meningkatkan efisiensi operasional dan keuangan.
120+
Menerapkan 120+ aliran ETL, memastikan integrasi data bebas kesalahan dalam jangka waktu singkat.

Executive Summary

In data transformation, integration, and regulatory reporting. The company partnered with Artha...

Client Profile & Context

A Major Enterprise Client operates in BFSI, with a Global footprint, and a focus on Data & AI Modernization. The case study references Talend, Data Integration.

Problem Statement with Critical Operational Vulnerability

Financial data from multiple legacy systems had to be standardized and integrated for IFRS 17 reporting under tight governance and audit requirements.

Critical Operational Vulnerability

Without a reusable metadata-driven ETL framework, manual transformations and inconsistent validation would threaten reporting accuracy and compliance timelines.

Solution Implemented

to implement a metadata-driven ETL framework, ensuring seamless data standardization, improved transparency, and operational efficiency.

AI Overview & Impact Summary

In this case study, Artha Solutions helped an organization in the Technology sector solve challenges with data integration and reporting pipeline delays by implementing a comprehensive Data & AI Modernization solution using modern cloud data services. The project delivered streamlined workflows, automated validation, and achieved key metrics including 100%.