FAQ

While Agile methodologies have been around for decades, doing Agile right is far from easy.

Role of Business Architecture in achieving objectives

Architecture has often been overlooked in agile transformation, being misinterpreted as a figment of waterfall delivery.

Without business architecture, Business can not be the driver and IT can not be true enabler to business. You will lose focus of business strategy.

Business Architecture enables cross mapping Vision & Objectives to value streams, lines of business, initiatives and capabilities to enable traceability and transparency.

Mature EA plays an essential role in ensuring that business service architecture can be streamlined with IT service delivery. Without this alignment organizations will find themselves with a runaway sprawl of technologies, duplicative competing platforms, and mismatched alignment of IT spending.

Banking as  a service is enabled by integration of financial services and products ino other kinds of customer activities, typically on non-financial digital platforms.

Banking as a Service (BaaS) offers complete banking processes, such as payments or credit, as a service through modern API-driven platforms.

Banking as a Service (BaaS) is the provision of complete banking processes (such as loans, payments or deposit accounts) as a service using an existing licensed bank’s secure and regulated infrastructure with modern API-driven platforms.

By providing bank-critical software as a service, modern banking platforms bring licence holders significantly increased speed to market, Examples (below)

Pay per API call or successful swipe

Flat-rate, agreed in partnership terms

Banks offer a percentage of interchange to partners

GPU accelerated database help run massively parallelise database jobs when run on dedicated GPU hardware. Big Data challenges represent the ability to easily access the huge quantities of risk result information that are generated daily across many financial services firms without losing fidelity through aggregation.

GPU accelerated database offer vast performance increases over both traditional and Big Data style SQL database solution.

GPU accelerated database help run massively parallelise database jobs when run on dedicated GPU hardware. Big Data challenges represent the ability to easily access the huge quantities of risk result information that are generated daily across many financial services firms without losing fidelity through aggregation.

GPU accelerated database offer vast performance increases over both traditional and Big Data style SQL database solution.

One of the practical ways is to gathering and combining data from different parties, applications, servers and technology services and ties those details to the commercial data that acts an input to run the IT landscape.

These costs that are directly attributed to applications and underpinning IT services can then be mapped to business units and business capabilities.

Note: have a very clear distinction between costs directly attributable to applications and those to infrastructure service for realistic estimates.

1. Data silos caused by organizational structures
2. The increasing need for real-time processing
3. Always-on availability and performance
4. In-consistency of data
5. Transition from mainframes to distributed workloads
6. Processing data for AI/ML in real-time

• Not designed for extreme real-time workloads
• Concentrates on data integrity over performance
• Vertical scaling
• Built for persistence and batch processing
• Not in-memory
• Slower response when implemented for real-time feedback
• Caching layers built on top of operational layers

• NoSQL databases are better equipped to handle larger data sets •
• Ease of handling both structured and unstructured data formats •
• High-performance handling thereby designed for real-time feedback at web-scale •
• Built to connect legacy systems with newer and faster front-end systems •
• Built to drive two-paced development of modern architectures •
• Can scale better horizontally as the data grows •
• Easier and faster to implement compared to traditional databases •
• Support for better performance handling

• Improved fraud prevention and financial crime monitoring
• Real-time digital ID validation
• Personalized offerings using improved real-time data analytics
• Targeted marketing
• Pre-trade assistance