Lessons from Managing and Normalising Fixed Income Data

?? The Challenge of Data Normalisation and Cleaning

In the realm of fixed income data management, we often encounter confusion between data normalisation and cleaning. Traditional methods that transform data into a common format or schema may seem sufficient at first glance, but they often fall short, especially when dealing with fixed income data. The incoming data's quality can be poor, and in some cases, even misleading.

Having dealt with the complexities of MiFID II data for over five years now, we've found that heavy enrichment is necessary to tackle various challenges. Only by mapping the data to a curated reference dataset and leveraging it for real use-cases, is it possible to surface critical issues. It's essential to recognise that a normalised data schema might appear acceptable on a trade-by-trade basis, but when joined and compared with multiple sources over time, hidden obstacles emerge.

?? Key Lessons and Considerations for Fixed Income Data Management

Through our experience managing and normalising countless disparate datasets in the world of fixed income, we’ve picked up a few lessons that we are happy to share.

1. Store all raw data upon ingestion without dropping anything (including data that, at the time - you would classify as invalid)
2. Downstream, clean and enrich data, with an initial level of data masking where data could be classified with one of a number of previously-seen defects.
3. Certain issues only reveal themselves when data is analysed in context over time, when used for core pricing, or compared across various curves and issuers. While individual trades and time-series analytics on a single instrument may appear fine, the underlying data may still harbour critical flaws.?
4. Inevitably over time, there will be a requirement to expand and update your data cleaning and error classification routines. Each time this is done, the downstream data cleaning has to be re-run against the original raw data - to avoid any unintentional regime-changes in your intermediate or output datasets.?
5. Use a platform to automatically maintain consistency. This is to avoid having to manually curate different versions with various levels of cleaning - or worse - reprocessing the entire dataset each time something changes. Any modifications to validation and cleaning logic that do not cause regeneration of previously processed assets will invariably lead to inaccuracies. Storing various updated versions of 'cleaned' data with different behaviours may inadvertently cause complexity and misleading results downstream.
6. Empower your team with an interactive GUI that allows them to track down areas where cleaning may have missed certain issues or where enrichments need updating. This real-time approach to data management is vital for identifying and resolving challenges promptly.

?? Unlocking the Full Potential

This is why we are excited today to announce Ediphy Virtual Data Manager (VDM), letting clients leverage our data management and normalisation expertise for their benefit. A cloud-based data management as a service offering, VDM enables financial institutions of any size to seamlessly integrate and normalise diverse data sources.

Ediphy’s VDM adapts a wide variety of database technologies depending on what suits a particular dataset, full lineage and transparency is maintained without dropping data. Advanced omnitemporal capabilities enable the platform to handle both real-time and historical data seamlessly.?

Data cleaning, normalisation and enrichment in the fixed income space should never be considered a one-time task. The ever-evolving nature of incoming data demands a forward-thinking, proactive approach to data management. Ediphy's Virtual Data Manager lets you navigate these complexities with ease, ensuring that hidden regimes and surprises are a thing of the past.

Experience the future of fixed income data management with Ediphy's VDM. Get in touch with our team today to explore how our innovative platform can revolutionise your data-driven decision-making processes.