FCM data and analytics

How FCMs can scale data and analytics like digital natives

FCMs and brokers that take a narrow view of automation and advanced analytics often estimate value creation on the basis of individual use cases.

These may be targeted at expanding the product offering, reducing operating costs to boost return on invested capital, or sharpening the company’s insights and vision. However, if optimally implemented, the value potential of these initiatives can amount to more than the sum of their parts. In this case, financial market participants are able to disrupt the economics of the markets in which they operate and transform processes across entire domains rather than single-use cases. As a result, they boost their productivity and break down siloes to build truly data-driven organisations.

Facing highly-complex data sets in activities such as margin calculations and collateral management, the challenge for many FCMs and brokers in scaling analytics is that they often lack the technology, talent and processes they need. Until very recently, for example, the powerful computational resources required to interrogate large volumes of data in detail were not available at a cost that made sense. The cloud has now solved that problem. In addition, data quality was a real challenge— an issue now being tackled by groups such as the Taskforce on Open Industry Standards.

When it comes to talent and processes, the challenge remains significant, requiring companies to make substantial operating changes that move them away from manually-driven and siloed operating models. Along the way, they need to make nuanced judgements about how to marshal and govern high-quality data, align incentives across the organisation, and measure impact, while ensuring their new approaches remain valid in a dynamic commercial environment.

In a world of rising trade volumes, an absolute precondition for scaling data and analytics is that the change must be embedded across the organisation. This requires a new lens on leadership. Deep transformation is unlikely to be achievable if led by chief data officers or chief technology officers alone. Instead, there must be an inspirational tone from the top, based on a clear vision of the value that may be achieved. Indeed, change should be led by the CEO, with the full backing of the leadership team. To get there, CEOs need to fully appreciate what is possible through transformation, based on a clear articulation of the value potential.

One challenge in measuring the potential of analytics-driven transformation is that it is often difficult to assign specific financial metrics to data and analytics use cases. Indeed, value creation from data-driven operating models is often about more than revenues and operational hurdles. It also emits from creating a culture of data-driven innovation. Still, as highlighted by the Bank of England’s Future of Post-Trade report, change at scale is difficult to motivate and sustain. In response, companies need to take a systematic approach, incorporating tools such as consistent KPIs, so that leaders are able to compare performance data across the organisation and prevent competition or disputes over results. Companies should also ensure that impact tracking is embedded in individual performance assessments and that reporting is conducted in as close to real-time as possible.

When it comes to controlling and managing data, one of the challenges facing the industry is the sheer number of data sources available, both internally and externally. As the Bank of England points out, post-trade data is often incomplete, inaccurate and inconsistent, leading to challenges in areas such as matching economic trade terms with counterparties. Data is often stored in a variety of formats, data fields, and legacy systems, including sometimes old-school spreadsheets. Over the past few years, numerous third-party solutions have come onto the market. The key for firms in this environment is to develop informed relationships with the ecosystem of third-party providers, and a sharpened awareness of where real value can be created.

One of the biggest challenges firms face in building out data-driven organisations is in attracting and retaining the necessary talent, with demand across industries vastly outstripping supply. Indeed, a lack of expertise is often a bottleneck to delivering impact, and companies need to address talent deficits at almost every level. In addition, financial services are no longer the go-to choice for tech-focused graduates. Firms, therefore, need to work hard to acquire and retain the people required, and on creating career paths that deliver. Leading firms have sometimes taken substantial action to ensure they attract the necessary talent, including moving operations closer to large pools of expertise, setting up boot camps, and pooling resources to maximise productivity.

Closely aligned to the talent issue is the question of culture. Transformative change is unlikely to be possible with a sense of collaboration across data functions and business lines. Business leaders need to be closely engaged with the change process, and able to provide feedback effectively so that business logic is effectively integrated into data and analytics priorities. A commonly-cited resource for data governance is the FAIR principles, which are designed to support the “findability, accessibility, interoperability, and reuse” of digital assets. Companies may also wish to align incentives to ensure that data-focused and business units are truly partnering.

FCMs and brokers need to choose how they disseminate data-driven orthodoxies across the business. Companies that have made the transition often go for either establishing a center of excellence or embedding transformation teams in the business. Others opt for a combination of the two, with centralised teams back by co-located or embedded groups of experts. An important principle in these setups is that the centre is able to effectively share information so that group companies do not fall into two-speed development cycles.

Finally, any transformation needs to be backed by a codified decisioning structure. Data standards and governance should be agreed upon centrally and backed by a dedicated data strategy. This means ensuring that information management is aligned with the firm’s mission, that data sources are regularly monitored, cleaned and assessed and that metrics are installed to align data priorities against business objectives and project timelines. These should be backed by glossaries, dictionaries and flow maps, as well as policies and rulebooks. Companies should also build or buy the necessary tools to support data hygiene and data management.

Through these far-reaching initiatives, brokers and FCMs can put themselves on a path to a truly integrated data-driven operating model. Effectively implemented, they can be the building blocks for higher levels of productivity, lower error rates, and an organisation aligned with an increasingly digitalised market landscape.

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