With our deep industry expertise in payments transformation, Opus facilitates modern data ecosystems that can leverage new-gen technologies in service delivery and analytics, enabling easy access to data for informed decisions and actionable insights.
Inability to leverage key data for delivering modern solutions, as it is stored in complex and incompatible mainframe systems
Need to support real-time data exchange to offer instant decision-making opportunities, such as credit checks, real-time payments, and fraud management
Failure to innovate and upgrade at the pace of the market due to challenges posed by the traditional tech stack
Restricted data sharing across business functions compromises data quality and leads to inconsistencies
Unable to collaborate with BigTechs and FinTechs that offer a large volume of data on customer preferences and expectations
Concerns over managing security, privacy, and strict compliance while hosting data over cloud infrastructure
Data modernization is a fundamental step towards embracing a digital-first future. Through smart data processing, FinTechs have been able to make big strides in capturing a significant share of the payments industry. While traditional banks and financial institutions are bestowed with a huge volume of rich data, the siloed architecture of mainframe systems makes it impossible to generate business value from that data. Opus enables global banks and financial services to build or deploy a modern data ecosystem. By deploying real-time data processing, data loading, and enrichment, Opus allows its clients to unlock opportunities, explore new revenue streams, and deliver greater value to their customers.
Building a resilient, flexible, and modern data ecosystem that allows a unified data marketplace to promote DaaS capabilities
Enabling real-time data processing capabilities that can be scaled up or down based on demand
Setting up integrated data governance policies to ensure security and compliance with industry regulations
Making data interoperable to allow processing through advanced customer analytics platforms
Integrating with BigTechs and FinTechs to leverage the data ecosystem for enhanced user experience, personalized offerings, and loyalty programs
Allowing modern payment technologies through improved data exchange, real-time decision-making, and faster reconciliations
Facilitating payment data enrichment through vendor collaboration to identify new revenue opportunities and prevent fraud
Improves data consistency through a unified view of data across the organization, removing data redundancy
Strengthens security by encoding safety and governance protocols as part of the software development process
Streamlines compliance with regulatory standards
Enables the storage and processing of large volumes of data by employing scalable infrastructure
Offers access to data and advanced analytical tools and techniques to extract powerful insights
Optimizes cost by eliminating on-premise hardware, enhancing efficiency, and offering a pay-as-you-go cost model
Delivers agility through configurable and scalable data infrastructure
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READ NEWSLETTERVIEW ALLFrequently asked questions
In the world of digital apps and solutions, data modernization is the key to competitive advantage against nimble FinTechs. Since most traditional banking organizations rely on mainframe architecture, access to their data is constrained. The aim of data modernization is to unlock the potential of data and make it accessible, secure, and available for business cases. An effective data modernization exercise will drive innovation, help deliver better services, and reduce the cost of maintenance.
The banking and financial industry is heavily invested in its monolithic applications that run the core business services. While being highly secure, these applications work in silos and are not compatible with modern technologies such as AI, ML, and big data. Migrating the data from the traditional application and making it available on the cloud is one of the biggest challenges. Along with that, data security and regulatory compliance are other challenges that are specific to the financial industry.
The key components of data modernization are data migration, data governance, data quality, and data security. The first component involves moving data from traditional infrastructure to the cloud. The second component defines the protocols to govern data storage and exchange. Data quality refers to ensuring accuracy and eliminating redundancy. Data security refers to measures taken to ensure data privacy and prevent unauthorized access.
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