Data is the core of a successful digital transformation strategy. But the extent of that success depends on how easily – and reliably – financial institutions can access and leverage relevant data from across the firm. As the breadth and depth of those requirements grow, it is clear that a modern data architecture is required for survival. While mission-critical systems built on legacy technology cannot be rebuilt or replaced easily, however, simply integrating point solutions only adds to the complexity and cost.
This is especially true in the capital markets, where success increasingly requires incorporating artificial intelligence and machine learning approaches to provide proactive business insight as firms leverage data as a competitive weapon. This, however, can be challenging if firms’ legacy data and compute architectures stand in the way.
Current demands for high-performance computing and big data analytics extend across traditionally siloed asset classes, technologies, and workflows.
- Legacy data and compute architectures are standing in the way of capital markets firms’ need to incorporate AI and ML approaches in order to provide proactive business insights as firms leverage data as a competitive weapon.
- The demands of regulatory (e.g. CAT, Reg SCI, MiFID II, FRTB) outstrip the compute capabilities of many existing applications and solution architectures
- It is time to look at new options to meet firms’ most challenging data requirements including handling different data types while combining advanced in-flight analytics with transactional processing.
- The fees and friction of today will continue to decline but the data and complexity of finding alpha will only increase
- New offerings are combing the best of: in-memory performance; reliability; durability; AI/ML; and, the cloud