Industrial

Enterprise Data Transformation & Analytics Platform for a Leading Real Estate and Mall Operator

Summary:

VentureSEA Digital partnered with Pakuwon Group to deliver a group-wide data transformation program, consolidated fragmented operational and maintenance data into a centralized analytics platform
Designed and implemented an enterprise-grade data architecture on AWS & enabled near real-time visibility into asset performance and maintenance efficiency
Reduced analysis cycles from weeks to days, accelerating management decision-making

Overview

Pakuwon Group, one of Indonesia’s most prominent real estate and mall management companies, initiated a group-wide data transformation to support more data-driven decision-making and prepare the organization for future AI adoption. Managing a large portfolio of malls and high-rise buildings nationwide, the company faced growing complexity in operational, maintenance, and asset performance data.

Challenge

Despite the scale of operations, the client had no prior experience executing a formal data transformation program. Key challenges included:

Operational and maintenance data fragmented across departments and business units
Data stored in siloed, manual, and tabular formats with limited analytical value
Lack of centralized data architecture and acquisition pipelines
Tight timelines driven by management’s transformation agenda
Limited visibility into asset performance, maintenance efficiency, and cost drivers

The absence of a unified data foundation made it difficult for management to identify underperforming assets, optimize maintenance spend, or gain a holistic view of operations. With annual maintenance budgets exceeding IDR 5 billion, inefficiencies persisted due to unstructured and unmonitored data.

Goal

The client aimed to establish a long-term, scalable data foundation that would:

Consolidate scattered operational and maintenance data into a single source of truth
Enable management to make faster, more informed, data-driven decisions
Improve asset and maintenance performance visibility across the organization

Serve as a foundation for future advanced analytics and AI initiatives

How did VentureSEA deliver the data transformation?

VentureSEA led the engagement as a data transformation partner, delivering the program in structured phases.

Assessment & Gap Analysis

Evaluated the existing “as-is” data landscape across departments
Identified silos, data quality issues, and process gaps
Defined a target-state data architecture aligned with business objectives

Data Architecture & Platform Design

Designed a centralized data architecture on AWS to support analytics and scalability
Implemented a data warehouse and data marts using AWS Redshift
Established governance principles to ensure consistent and reliable data access

Data Engineering & ETL Implementation

Built and provisioned ETL pipelines to ingest and transform data from multiple source systems
Standardized and structured raw maintenance and operational data
Enabled near real-time data availability for analytics use cases

Business Intelligence & Analytics Enablement

Developed interactive BI dashboards using AWS QuickSight
Customized dashboards for different business functions, including management, operations, and maintenance teams
Enabled users to quickly identify trends, inefficiencies, and asset performance issues

Outcome & Impact

Successfully consolidated siloed data sources into a centralized data platform
Deployed an enterprise-grade data warehouse and data marts on AWS
Enabled near real-time visibility into asset performance and maintenance operations
Reduced analysis cycles from weeks to days, significantly accelerating decision-making
Improved identification of underperforming and productive assets, supporting more accurate maintenance and replacement decisions
Established a scalable data foundation to support future advanced analytics and AI-driven optimization initiatives

Business Value

By transforming fragmented operational data into a unified analytics platform, Pakuwon Group gained a clear, organization-wide view of asset performance and maintenance efficiency. This enabled management to make faster, more confident decisions, optimize maintenance spend, and lay the groundwork for data-driven and AI-enabled real estate operations at scale.

Industry

Financial Services & Fintech

Client profile

Consumer loan provider

Core problem

Manual credit assessment and fragmented data

Services Delivered

Data Engineering, Data Platform, Bespoke AI Modeling

Impact

20–25% reduction in non-performing loans, 30–40% faster loan approvals, improved portfolio quality without reducing acceptance rates

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