In the capital markets, data is the ultimate competitive weapon. Unfortunately, financial institutions’ legacy data and high-performance compute architectures no longer provide a competitive edge. To meet the demands of the future, firms need a simplified, cost efficient, elastic, and dynamic compute environment. TABB Group head of fintech research Monica Summerville examines the drivers forcing firms to rethink their technology and data architecture, the requirements and challenges, as well as the role of innovative technologies such as cloud, artificial intelligence and machine learning.
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 (see Exhibit 1, below).
Exhibit 1: Compute and analytics demands span diverse workflows and functions
Source: TABB Group
To maximize competitive advantage, firms need a simplified, cost efficient, elastic, and dynamic compute environment. However, the industry’s historical reliance on a collection of point solutions can hinder a financial institution’s ability to meet ever-increasing business and regulatory demands. To break down these historic silos, new approaches are needed to increase scalability and performance while providing access to accurate and real-time data for insights and maintaining operational resiliency.
While financial institutions have invested billions of dollars, pounds and euros on implementing once-state-of-the-art systems, demands from both regulators and clients for ever-greater transparency into data and its analysis can mean tenfold increases – or more – in the amount of data generated. In addition, relying on “start of the day” or “end of day” data often is no longer acceptable. Instead, more functions are requiring near- or fully real-time access to data and analytics, which compounds the data architecture challenge.
On the regulatory front, compute requirements from initiatives such as the Consolidated Audit Trail (CAT) and Regulation Systems Compliance and Integrity (Reg SCI) in the US and, globally, the revised Markets in Financial Instruments Directive/Regulation (MiFID II/MiFIR) and the Fundamental Review of the Trading Book (FRTB) will outstrip the compute capabilities of many existing applications and solution architectures. This, coupled with business pressures, means that financial institutions’ legacy data and high-performance compute architectures no longer are providing a competitive edge.
Financial institutions are increasingly focused on improving data availability, transparency and quality; sourcing more data across different types; and leveraging this cleaner, enriched data both to improve efficiency and find alpha. The problem is that past data architectures and infrastructures were not designed for today’s investment world, which has shifted toward multi-asset and multi-regional approaches and demands real-time decision support and risk management. In addition, the push for greater transparency, efficiency and regulatory oversight has fueled the spread of digitization throughout all asset classes, resulting in the increased proliferation of data, which of course has to be processed by any and all market participants simply to survive. Together, these drivers are leading firms to re-assess their data architectures for both transactional and analytical workloads (see Exhibit 2, below).
Exhibit 2: New requirements are driving firms to re-assess data architectures
Source: TABB Group
While financial firms have always leveraged data and technology to their competitive advantage, capital markets’ technology tends to be fragmented and driven by its close alignment to organically grown businesses or, even more problematic, by companies built through a series of mergers and acquisitions. In either case the result is increased organizational complexity with an infrastructure highly dependent on duplicated data and operations. This creates an overly complex and costly operational structure reliant on excessive amounts of reconciliation and operational redundancy. This is not conducive to the technical and operational agility needed to compete in today’s markets.
After a decade of constrained technology budgets dominated by crisis-instigated regulation, financial organizations’ IT resources have been focused largely on “run the bank” initiatives, not “change the bank” projects. And, while the business and innovation climate is changing, financial infrastructure doesn’t change that quickly. Today’s financial data resides in multi-dimensional silos, meaning data can be aggregated by business line, data type, computational requirement, and/or dozens of other dimensions that need to be constructed at a moment’s notice. Unfortunately for most firms, their data resides within a mix of mainframe, client-server and, lately, cloud applications over both local and wide area networks and is integrated through the use of backend-oriented middleware tools.
As a result, financial firms have been accumulating a technical debt, especially with regard to their data infrastructures. New requirements are often met with a point solution that is integrated with a pre-existing, poorly documented and brittle system. This is a pragmatic approach, as there are very few opportunities for greenfield developments within capital markets; critical systems cannot be taken offline for any length of time, and the risks to reputation and the bottom line are deemed too great.
Data is the catalyst for success
As investment managers move toward more complicated investment strategies – e.g., multi-asset, multi-region, and highly efficient execution – they are increasingly relying on sophisticated platforms for portfolio management, order management, execution management and compliance. Again, data is the catalyst for success. The buy-side front office no longer can rely only on batch data. Instead, positions, cash, open orders and risk need to be calculated continuously throughout the day, ideally in near real time. This can only be achieved with reliable data architectures, with very high performance, that scale seamlessly and ensure data quality and data governance are not compromised for the sake of speed or agility.
A number of trends have encouraged the buy side to ramp up their data infrastructures to support advanced analytics, including cognitive approaches incorporating machine learning (ML), natural language processing (NLP) and other artificial intelligence (AI) approaches (see Exhibit 3, below). It is no wonder the buy side has been an early adopter of cloud technology, as AI approaches rely on the ability to process and analyze vast quantities of both structured and unstructured data types.
Exhibit 3: FS industry trends supporting move to modern data architectures on buy side
Source: TABB Group
The sell-side sales and trading functions meanwhile have drivers of their own, resulting in numerous pain points (see Exhibit 4, below).
Exhibit 4: FS industry trends supporting move to modern data architectures on sell side
Source: TABB Group
In addition to financial industry trends, there are drivers common to all businesses encouraging firms to re-architect their data solutions:
- The democratization of data analytics is fueling a rise of “self-service” approaches.
- Digital transformation at a corporate-wide level is creating hyper-connected enterprises that have access to more data than ever before.
- Desire for proactive (predictive and prescriptive), as opposed to reactive, analytics means firms are looking to cognitive analytic approaches that require access to both historical and real-time data as well as deeper and wider data sets across the enterprise.
Financial institutions hobbled by legacy technology will always struggle to keep pace with changes in their market or profit from the savings and opportunities new technology offers. Legacy architectures are being pushed beyond their capabilities, falling short on business service-level agreements (SLAs), while increasing both operational support costs and business costs. This lack of flexibility impacts the time to market for new ideas and competitive positioning in the market.
The future is now
In today’s fast-changing financial businesses, there is no patience for multi-year technology refresh programs or monolithic replacement approaches. Financial firms have embraced DevOps and highly modular architectures in order to be as nimble and agile as possible, while also meeting high standards for reliability and uptime. They are looking to augment and rationalize their technology stacks, with a preference for commodity hardware that can be elastically scaled and that also offers lower total cost of ownership. With a further need to rein in operational costs while reacting to changes in markets and demands, financial firms are looking for data architectures that can quickly adapt, while utilizing commodity hardware that can be quickly scaled.
Modern data architectures need to support greater business agility, interoperability, flexibility and cost efficiency by transitioning to elastic, dynamic compute infrastructures that can support both analytics and transactional workloads, and allow data to be managed in multiple ways, including supporting multiple types of relational and non-relational access. A modern architecture employs an elastic infrastructure, leverages microservices and containers, and provides unified and secure access to all data from across the organization.
A common approach to data architecture in financial services has been to utilize a data technology stack comprising a collection of approaches – for example, historic data in a relational database, recent data in a columnar database, and real-time data in some sort of event processing software engine. To obtain a consolidated view, connections must be made directly to various systems; an approach that gets increasingly complex over time.
While technologists would love to rethink their architectures with a clean-slate approach, a “rip and replace” strategy is not typically practical or indeed realistic. Ideally, firms, when integrating or adding new features to existing systems, try to reduce the reliance on existing monolithic systems over time. In this way, legacy systems near end-of-life do not need to be ripped out, but rather can be left to retire gracefully as new functionality and features are added incrementally via newer approaches.
One way firms are solving these problems is through new cloud infrastructures. Firms are embracing elastic computing approaches. For Tier 1 front-office functions, this is likely to mean private cloud; however, Tier 2 firms are thinking more about public cloud, although there will continue to be workloads with hosted or managed solutions in a co-location facility or third-party data center. Firms therefore are still faced with trade-offs when deciding how to scale solutions.
AI and ML
Front-office transactions and analytics, which increasingly span diverse use cases (see Exhibit 5, below) are increasingly incorporating artificial intelligence (AI) and machine learning (ML). These technologies are often supported through specialized software and hardware for performance reasons and include in-memory databases. In-memory databases typically save time by avoiding the need to write/read from a physical disk. While firms increasingly are leveraging these platforms, they are not without their own challenges, especially around data persistence, data duplication, scalability and reliability if memory is exceeded. Data stored in-memory can be lost when the system closes or goes down unexpectedly, so strategies for handling overflow data must be implemented. Accommodating unexpected spikes in traffic in a reliable and cost-effective way is also challenging with in-memory architectures, as the cost of supporting rarely used resources has to be weighed against the potential cost of performance and reliance issues to the business.
Exhibit 5: Sample front-office use cases for high-performance data architectures
Source: TABB Group
In-memory solutions which handle both transactional and analytical data workloads allow advanced analytics to be run “in flight” on transaction data (as opposed to after the fact, once data is written to disk). However, these still require the use of a considerable amount of expensive specialist hardware. Balancing the expense of full persistence and reliability against the performance of in-memory has been challenging for firms.
Lately, though, new technologies have become available that combine transactional and analytical workloads, AI and ML, and cloud and commodity compute resources, but without the cost and reliability issues of traditional in-memory approaches. It comes at a good time, with firms evaluating their compute requirements with a view to managing a transition to elastic compute infrastructures.
The asset management and capital markets industries are facing an existential challenge as fee, revenue and regulatory issues seem to be pressuring virtually all aspects of the business. But all is not lost; there are significant opportunities as well. It is not hyperbolic to say, however, that realizing these opportunities will depend largely on a firm’s data capabilities.
There are various data architecture approaches available to address the most challenging data requirements. While the point solution has been to look at in-memory database approaches, it is time to look afresh at new options in the space that offer greater efficiency at lower operational costs, meeting the need to handle different data types while combining advanced in-flight analytics with transactional processing. Firms need to approach new offerings combining the best of in-memory performance (without the limits imposed by memory), reliability and durability, AI and ML, and the flexibility and scalability of cloud, as the industry grapples with managing in a world tipped toward lower costs and increased agility.
While the industry focus has been to respond to post-crisis regulation, the world has changed. Customers want innovation, new products, and new services. Unfortunately, these new demands are difficult if not impossible to provide with todays’ legacy and limiting heterogenous data technology infrastructures. While firms can survive with outdated approaches for a limited time, the clock is ticking.
If financial firms do not start rethinking their technology and data architecture plans now, it will be even more challenging to meet the demands of the future. The fees and friction of today will continue to decline but the data and complexity of finding alpha will only increase. This will force a restructuring of virtually all financial businesses. To survive, firms need to get started today.