Data normalisation - standardising the presentation of data to ensure it is consistent across required fields and records - is an essential component of effective financial data management, enabling reference data in particular to be recognised, interrogated and incorporated into required data workflows much more easily, and minimising the risk of reconciliation and compliance ‘fails’.
Data normalisation - standardising the presentation of data to ensure it is consistent across required fields and records - is an essential component of effective financial data management, enabling reference data in particular to be recognised, interrogated and incorporated into required data workflows much more easily, and minimising the risk of reconciliation and compliance ‘fails’.
In the ever-evolving landscape of global finance, accurate and reliable reference data is crucial for executing seamless transactions and reporting them to different stakeholders and transaction lifecycle destinations. Reference data can be considered the backbone of financial markets - the critical information about contracts, entities, and counterparties that ensures that all parties to a trade have exactly the same understanding of the details of the transaction in order that it can proceed without issue to successful settlement. However, the scale, complexity and diversity of reference data can present significant challenges to data managers.
High impact of inconsistent data
One of the primary challenges of reference data management is the number (and proliferation) of data owners and sources. For futures and options derivatives alone, there are multiple and growing numbers of issuers and execution venues, each of whom might ‘label’ instruments and particulars of contracts in a similar but different way.
Beyond trading venues themselves, different data providers can use different data formats and schemas, creating inconsistencies in data representation that have to be normalised by data managers tasked with presenting the data correctly for specific end destinations, for example, to meet prescribed regulatory reporting requirements.
For most enterprises, collecting transaction data efficiently from multiple sources is challenging enough - the far bigger challenge is data management, ensuring that inconsistent and redundant (duplicate) information doesn’t find its way into transaction lifecycle workflows where it may impact trade execution, reconciliation and reporting.
Importance of accurate financial data
Accurate trade reconciliation is an increasing imperative for all financial firms, both from an enterprise management and regulatory reporting perspective. There have always been high costs associated with fixing trade discrepancies and fails, and these can have a significant impact on the overall ‘profitability’ of transactions. With regulators today placing increasing pressure on firms to not only report correctly, but to demonstrate absolute rigour in end to end reconciliation and reporting processes and workflows, there is the potential for firms to incur even greater costs in the form of fines and other regulatory sanctions.
Add to this the fact that regulatory reporting is something of a ‘movable feast’ with disparate jurisdictions and regulators setting their own reporting standards and data requirements, ensuring data compliance becomes even more critical - and challenging.
How can data be normalised?
A comprehensive data model that captures the intricacies of reference data structures and hierarchies is crucial to standardising and normalising reference data. By mapping data relationships and defining consistent rules, financial firms can simplify the data normalisation process and ensure data consistency across various applications and systems. The million dollar question is, however, whether firms should attempt to build and maintain this capability themselves, or whether it makes more sense to source and work with data that has already been normalised and standardised.
While some very large institutions may have the resources and wherewithal to take on the challenge of reference data normalisation directly, for the vast majority of trading firms this may not make the most business sense. That’s where specialist data providers like FOW come in.
Apart from the obvious benefits of not having to manage the complexities of translating huge amounts of data efficiently into the ‘language(s)’ required to support a plethora of enterprise workflows, working with a specialist data service business with a dedicated and proven data normalisation model means, to paraphrase John Maynard Keynes, “as the facts change, we change our formats”, ensuring that our data is always consistent and ‘compliant’ for all required applications.
Improving operational efficiency and an enhancing client experience
Specifically, our cross-vendor symbology mapping data feeds, with full sets of identifiers, enable users to seamlessly on-board and manage clients who may use differing systems for market data, order execution and trade management. Exchange traded and OTC futures and options data is delivered in the precise formats needed for and across multiple trading platforms and workflow systems, reducing processing costs and improving risk management and at the same time raising the quality of firms own service offerings and accelerating go to market times for new business.
“We use FOW to provide an extra layer of consistency for the important fields so we don't have to go to multiple providers and suffer integration and technical headaches. We don’t need to get a field from hundreds of different places when we can get it all normalised through FOW.”
Global Head of Operations, Technology Vendor
The proven FOW data normalisation and standardisation model has been honed over more than 20 years working with the most demanding Tier 1 institutions and partners to enrich instrument data with root and trade series level symbols that are mapped to key market data and back-office vendor identifiers. Through the process of instant ordering mapping we ensure compatibility between client platforms and clearing, settlement, portfolio and regulatory reporting systems.
The importance of data normalisation
The path towards reference data normalisation Nirvana is littered with obstacles in the form of inaccurate, incomplete, or outdated reference data; poor data quality can have severe impacts with respect to operational efficiency, risk exposure and regulatory compliance.
Unlocking the full potential of reference data means overcoming the challenges presented by fragmented data sources, poor data quality and complex data structures. As such, reference data normalisation and standardisation are paramount in the quest for accurate, reliable, and consistent financial information. Our proven and trusted data management expertise, robust data validation mechanisms, comprehensive data models and technologies and most importantly, focus and commitment to delivering ‘right first time’ futures and options reference data can ensure a much smoother ride on your reference data journey.
Contact us to find out how we can normalise and transform F&O data to your specific needs - and you only pay for the data you need.
