• Mark Hermeling, CTO at Alveo


• 16.08.2021 10:00 am
#data #Transformation

Market and reference data on financial products, clients and trading venues is the contextual information to all financial transactions. It provides the identification, descriptive and economic information to settle financial transactions. Market and reference data has to be shared across business areas including front, mid and back-office and needs to be of high quality to prevent any trade breaks and have accurate and complete customer, financial and risk reporting.

Yet, with business processes becoming data-intensive and increased regulatory scrutiny on data lineage, firms need to up their game in data management capabilities. Historically, many have taken a reactive and business-line-based approach, leading to silos based on product lines, customer segments or geographies. Legacy systems and frequently undocumented point-to-point connectors add to the complexity.

This scattered approach has led to high software maintenance costs as well as long and frequently unpredictable change cycles. Standardising market reference data, securing its quality and making it accessible and reusable is therefore a significant exercise.

First and foremost, data should be treated as any other asset, i.e. something that needs proper care and maintenance and that is easily accessible. Ideally, business users should be able to self-service requests without the involvement of IT. Second, data and metadata should be managed together as the contextual information defines its use cases. Third, data management and analytics are inseparable capabilities. Analytics is no longer the exclusive domain of data scientists operating at the end-point of data flows but is increasingly embedded in day-to-day business processes.

The more automation, the stricter the requirements on data quality and unambiguous data modelling. Documenting and tracking data usage is critical and financial services can no longer approach data management and analytics as separate disciplines. This will likely also help facilitate team collaboration and integration of quality data into user workflows. Data needs to be readily accessible from multiple locations and the metadata should clarify who does what.

Moving to the cloud

Data management is a foundational discipline that, when done properly, puts business users and applications on a common footing and prevents redundant data sourcing as well as underusage of data assets. When discussing cloud strategies, it makes sense to start with data management. This will not only reduce infrastructure and maintenance cost and increase scalability and elasticity; it should also help reduce market data cost enabling better management of market data to appropriate-sized infrastructure and centralising licensing. This can be achieved through the utilisation of vendor-managed solutions with a ‘one-stop-shop’ for the end-to-end provision of market data from vendor feeds all the way to distribution to their customers.

Improved transparency of data demand and usage will enable better controls to be in place and ways to meter and monitor real time usage/cost across data sources, categories and user groups.

Data lineage will be improved to ensure source data and any transformation in its lifecycle are captured. A movement to cloud will also enable greater scalability.

Data-as-a-service

Managed data services typically start with hosting and application management services. In terms of a service catalogue, data-as-a-service is the next service layer after cloud infrastructure, storage, computation and application management of the data management platform. Data-as-a-service covers the preparation and supply of data sets required by business applications and acts as a data operating system to the business. The associated metadata will provide context and permitted use cases.

Improved quality and control will increase trust in and adoption of these standard services. Aspects of this include data quality control and governance to meet regulatory requirements and improved control and governance on technological operational processes.

Business user self-service will speed up change cycles, lower the cost of change and better equip the firm to thrive in today’s agile environment. Firms should opt for a cloud agnostic solution with a ‘lift and shift’ pattern to abstract away from infrastructure specifics and get closer to the user. They  should avoid lock-in to specific cloud providers.

The need to act: how to tackle mounting data management and analytics requirements

Financial services firms face an unprecedented squeeze in margins. Being early adopters of the previous wave of automation, they are often mired in constraints imposed by legacy infrastructure. A scattered and haphazard approach to data management translates into high fixed cost and complex change cycles when it comes to onboarding new data sets or meeting new business or regulatory reporting requirements that cut across existing silos.

All this means is many firms are not well-prepared to take advantage of the opportunities in new data analytics technologies, serve their business users poorly when it comes to meeting their data demands or even struggle to meet new regulatory requirements both on reporting as well as ondata quality.

Firms need a comprehensive market and reference data transformation plan that focuses on creating recurring value and developing collaborative and sustainable relationships among market data vendors, IT, and business units.

However, prior to committing to a large-scale transformation, our recommendation is to conduct an internal market data and platform maturity assessment as the first step in understanding the issues with the current state such as identifying usage duplication, underused data and underserved parts of the business. Any financial firm that is embarking on a market data transformation journey should seize the opportunity to do things right, focusing on managing data as an asset that needs to be maintained to it can deliver maximum mileage.

A structured approach to identifying improvement areas and shifting data management to the cloud as a common resource for all departments can drastically simplify data operations, reduce BAU cost and speed up change cycles as well as reducing the cost of change, enabling a solution that will put all business users on a common footing, provide transparency as to available data and its quality, facilitate the onboarding and integration of new content and clear the path for new business initiatives.