The Challenge

When PT Bank Rakyat Indonesia (BRI) executives wanted to access all of their population data to get holistic insight into the business, the Enterprise Data Management team at BRI knew that they needed a new, agile solution that would help them do that. With their data in an on-premises data lake, the team had to either use sample data from large data sets or form multiple data marts on separate servers in order to analyze their data for the executive team.

The BRI Enterprise Data Management Division had three main challenges with data analytics. First, they had a performance issue when they were processing and visualizing their data. Second, they had a storage and load time issue caused by moving data from the data lake into an RDBMS in order to visualize their data. Third, they had an issue with the processing time it took to provide data for queries. As an example, if they gave the wrong instructions, they would have to create, build and run each job over again. This was an inefficient and expensive way to provide executives with the analysis they use to make business decisions.

"We were able to get set up in less than six hours."

It was easy to get started with AtScale. We were able to get set up in less than six hours.

Author Image

Gede Kukuh

Division of Enterprise Data Management, BRI Bank

The Solution

Gede Kukuh from BRI’s Division of Enterprise Data Management saw a demo of AtScale and realized that the AtScale platform could help his team speed their queries and improve time to insight when implemented for BRI. When he installed AtScale into his environment, he immediately saw improvements for data processing times. His team also liked the way that AtScale helped to design datasets, because it made it easy for business users to get self-service access to their data.

With AtScale, BRI’s business users don’t have to extract the data into different target databases for data analytics and they don’t need a dedicated engineer to get them started. AtScale transparently executes queries on raw data without manual processing to shape, move, duplicate, and optimize data with significant performance improvements.

The Results

The BRI team is consuming data quickly without the need to perform ETL on their data. In one case, they were able to reduce the time it took to process 600 million records from 10 minutes to under a minute. AtScale also works well with Tableau so the BRI team is able to build data visualizations easily. As a result, the architecture team has seen significant performance gains to both the investment and banking business operations at BRI with the AtScale platform.

Time-to-insight has also greatly improved. Before AtScale, the BRI team needed to build star schemas using scripting or SQL Server Analysis Services (SSAS) and the average time to build the data was two days (depending on how big and complex the data was). With AtScale, they have been able to build hundreds of insights in hours not days.

The BRI team has also developed dozens of Tableau dashboards and is now piloting a new project for self-service analytics. This effort was initiated to help BRI become a data-driven company with business teams who can perform their own analytics without calling in the data engineering team. The backbone of this initiative is data, but the key enabler is AtScale’s Intelligent Data VirtualizationTM which provides access to the data needed for each stakeholder quickly, effectively and efficiently.


PT Bank Rakyat Indonesia (BRI) is one of the largest banks in Indonesia. It specializes in small scale and microfinance style borrowing from and lending to its approximately 30 million retail clients through its over 4,000 branches, units and rural service posts.


AtScale powers the analysis used by the Global 2000 to make million dollar business decisions. The company’s Intelligent Data VirtualizationTM platform provides Cloud OLAP, Autonomous Data EngineeringTM and a Universal Semantic LayerTM for fast, accurate data driven business intelligence and machine learning analysis at scale. For more information, visit

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