For many Malaysian enterprises, the Starburst data platform helps teams answer important business questions when data already exists but remains scattered. That data may sit across core systems, cloud platforms, data lakes, operational databases and departmental applications. The challenge is not simply collecting more data. Instead, organisations need authorised teams to access and analyse the right data quickly, securely and cost-effectively.
Starburst supports this need through data federation. This approach lets users query data across distributed sources without copying every dataset into one central platform. Consequently, banks, insurers, takaful operators, government agencies, GLCs and large enterprises can reduce delays, complexity and costs linked to traditional integration projects.
The Business Challenge: Valuable Data Trapped in Silos
Organisations often use a mix of legacy and modern technology. For example, customer transactions may sit in an on-premises database. Service interactions may live in a contact-centre platform. Meanwhile, digital activity may sit in cloud storage, while finance or risk data remains in separate enterprise systems.
When teams need a consolidated view, they often extract, transform and load data into a warehouse or lake. Centralisation remains valuable for many workloads. However, moving every dataset can create new challenges:
- Long lead times before analysts can access newly required data
- Duplicated storage and data pipelines that increase operating costs
- Stale information when data movement cannot keep pace with business activity
- Spreadsheet-based reconciliations and manual reporting workarounds
- Inconsistent definitions of customers, products, accounts or operational metrics
- Governance gaps when teams copy sensitive information across environments
These issues affect more than IT efficiency. For example, a CFO may wait longer for management reporting. Similarly, a risk team may struggle to investigate unusual activity promptly. Likewise, operations leaders may lack a timely view of service performance or supply-chain conditions. Furthermore, regulated organisations must control access without blocking legitimate business use.
How the Starburst data platform Supports Data Federation
Starburst is a data access and analytics platform built around Trino, an open-source distributed SQL query engine. It lets teams use SQL to query data where it resides across supported sources. Therefore, teams do not need to relocate all data before analysis.
In practical terms, the platform can provide a single query layer across selected data environments. As a result, users can analyse data from a lake alongside information in operational or cloud systems. Of course, each query remains subject to the organisation’s security and access policies.
Rather than treating every request as a migration project, teams can decide which data should stay in place. They can also identify data that warrants centralisation for performance, operational or governance reasons. Consequently, this approach is particularly relevant for large, fast-changing or tightly controlled data.
Starburst data platform Customer 360 Example
Consider a financial services organisation seeking a more complete customer view. For instance, customer profiles may sit in a CRM platform. Transactions may remain in a core banking system. In addition, cloud storage may hold digital interactions, while a service-management application holds complaints.
With a well-designed federated access layer, authorised analysts can query relevant datasets across these environments. Curated data products can then provide reusable, governed views for customer experience, risk, finance and marketing teams. As a result, teams can avoid creating separate extracts and spreadsheets.
Starburst also supports governance capabilities such as role-based access control (RBAC) and attribute-based access control (ABAC). These capabilities help organisations apply access decisions based on user roles and relevant attributes. Additionally, the platform includes caching and indexing technologies to improve query performance. Learn more about query performance and acceleration for Starburst workloads.
When a Federated Data Approach Makes Sense
Data federation does not replace every data warehouse or data lakehouse investment. Instead, it can complement existing platforms when an organisation needs faster access to distributed information.
Common use cases include:
- Risk and financial crime analytics: Bring SQL analytics closer to data that may remain on-premises or in controlled environments.
- Customer and operational intelligence: Combine relevant transaction, service, digital and customer data for a more current business view.
- AI and data application development: Help data teams discover and explore distributed datasets before committing to extensive data movement.
- Supply chain and partner analytics: Create governed datasets for approved users while applying appropriate access controls.
- Large-scale exploratory analytics: Enable analysts to investigate high-volume data without creating multiple unmanaged copies.
Published customer examples show how the Starburst data platform can improve access to distributed data, accelerate queries and support analytics at scale. However, individual outcomes depend on data architecture, query patterns, workload volumes, governance requirements and implementation design. For further product and customer context, refer to Starburst’s overview of why data teams choose Starburst.
How ORTECH Helps Put Data Access into Practice
Technology alone does not resolve fragmented data. Instead, successful implementation needs clear business priorities, sound data design, suitable controls and practical adoption. In other words, people and processes matter as much as the technology.
As The Analytics Engineering Company, ORTECH helps organisations design and implement analytics solutions that connect business needs with dependable data processes. For a Starburst-based initiative, ORTECH can support activities such as:
- Requirements assessment: Identify priority decisions, users, data sources, reporting pain points and performance expectations.
- Architecture review: Evaluate warehouses, lakes, cloud platforms and operational systems to determine where federation adds value.
- Data source integration: Plan and configure access to selected sources while considering connectivity, data quality and operational dependencies.
- Data product design: Define curated, reusable datasets with clear business definitions for finance, risk, customer operations or supply-chain functions.
- Governance and controls: Establish access policies, ownership, documentation and appropriate handling for sensitive data.
- Query and workflow optimisation: Design analytics workflows that balance responsiveness, cost and reliability.
- User enablement: Train data, IT and business teams to use governed data and repeatable analytical processes.
The objective is not to federate every dataset by default. Rather, ORTECH helps create a fit-for-purpose data access model. That model should support priority use cases while maintaining operational discipline.
Starburst data platform Outcomes and KPIs
A properly scoped implementation can make data more available for analysis. It can also reduce avoidable manual work and unnecessary duplication. Consequently, potential outcomes include:
- Faster turnaround for cross-system reporting and investigation requests
- Improved analyst productivity through direct, governed access to relevant data
- Reduced reliance on manual extracts, spreadsheet consolidation and duplicate datasets
- More consistent metrics through curated data products and shared definitions
- Stronger visibility into who can access data and how they use it
- Better support for AI, analytics and operational decision-making initiatives
Organisations should define useful KPIs before implementation. Depending on the use case, they may track report turnaround time, query response time and manual data extracts. Additionally, they may measure reconciliation effort, data product adoption, storage duplication, incident resolution time and user satisfaction.
Explore More ORTECH Use Cases
Every organisation has a different data landscape, from legacy on-premises systems to multi-cloud environments. The Starburst data platform can support a practical access model across that landscape. Therefore, explore ORTECH’s Starburst solutions to see how governed access, automation and analytics can support finance, operations, risk, reporting and decision-making initiatives.
Talk to ORTECH
Whether you are evaluating Starburst data federation, modernising analytics access or reducing reporting delays, ORTECH can help assess a practical path forward. First, start with your business, technology and governance priorities, then define the right solution.
