The Hidden Complexities of ETF Data Management

December 11, 2023

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Navigating the world of ETFs can often feel like finding one's way through a dense forest.

The Hidden Complexities of ETF Data Management

Navigating the world of ETFs can often feel like finding one's way through a dense forest. Each tree, representing a data point, contributes to the larger ecosystem, and understanding this vast landscape is crucial for informed decision-making. But before we delve into the solutions, let's first explore the intricate challenges that portfolio managers face in ETF data management.

The Multifaceted Challenges of ETF Data Management

In the realm of ETF data management, one of the foremost challenges is the sheer diversity of data sources. Different providers come with their own calculation methodologies, distinct data points, unique field names, and varied reference data. This mosaic of information, while rich, can lead to inconsistencies and integration hurdles. This is accentuated by the fact that, despite providers having a desire and regulatory need for transparency, there’s no distinct definition of what ‘quality ETF data is’ which leads to different standards across the industry.

What does this mean for someone trying to pull in this data on a daily basis? There’s all sorts of challenges, from finding the files in the first place to matching reference data, to figuring out calculation methodologies and managing republishes. This is why most firms outsource to third parties to assist them.

The Over-Standardisation of ETF Data

The heart of why some firms don’t use third parties is in the control of the data. Standardizing ETF data is essential for uniformity and ease of interpretation. However, the art lies in achieving standardization without compromising on the intrinsic value of the data. It's a delicate balance, ensuring that the essence of the data isn't lost in the quest for uniformity. Many firms feel like the only way to achieve this balance is with an integral knowledge of their own systems and requirements. “I need clean prices for trading”, “I will join using SEDOLs” and so on. An ideal provider in this landscape would offer a data model that feels internal, providing exactly what the client needs and stripping out complexity without removing crucial details.  

How can you do this? The answer is in the storage of the data. If the data is pulled in to a multi-database structure, with different silos for each provider, then the raw data can be kept intact but normalisation can occur further down the chain. This allows for the normalisation of data while keeping the original data within reach. The beauty of this, of course, is that if the data is needed in the future it can be easily obtained - which might be a struggle for anybody who was immediately trying to normalise the data into one shape. It also means that if a format changes for a source it can be managed in its own environment without impacting other data sets.

Integration with existing software (especially the Cloud!)

Furthermore, the integration of ETF data with existing software systems presents its own set of challenges. Each software has its architecture and nuances, and ensuring seamless integration without disruptions is paramount. This becomes even more complex when considering the dynamic nature of the ETF landscape. With new providers entering the scene, the introduction of new issuances, and periodic changes to data formats, adaptability becomes key. For example, when new Crypto ETFs are entering the scene, how do you deal with staking rewards or new reference data points?  

Simplifying this process for users can take multiple shapes. One key piece is understanding the users requirements and providing a sensible data model that can easily connect to their systems - and again, has the ability to bring in any data point that the source may have. The other piece is in making data cloud-available, whether that be through a dedicated lakehouse or in a Snowflake environment.


Lastly, the timeliness of data is a critical factor. In the fast-paced world of finance, late data can significantly impact decision-making processes. Additionally, republished data introduces another layer of complexity, necessitating systems that can swiftly adapt to these changes and ensure accuracy.

Solutions and Best Practices

Having outlined these challenges, it's clear that ETF data management is a nuanced domain. However, with a deep understanding of these intricacies, portfolio managers can navigate this landscape more effectively. Rimes has played a pivotal role in the ETF ecosystem since its inception by providing the asset management community with the benchmark indices that their funds track. 

  • Data Depth and Breadth: With a repository of over 9500 global ETFs from more than 220 issuers, the sheer volume is staggering. Our approach prioritizes precision, ensuring that each ETF's data is stored in unique environments. This meticulous approach guarantees granularity without sacrificing accuracy.
  • Cloud-Forward Approach: The digital transformation wave has made cloud integration essential. Recognizing this, our service is designed to integrate seamlessly with modern platforms, including Snowflake. This ensures agility in data retrieval and scalability as data volumes grow.
  • Bespoke Data Solutions: The diverse needs of portfolio managers necessitate flexibility in data presentation. Catering to this, our service provides data in the familiar Rimes model or in customized files. From specific validations to tailored calculations, we ensure the data aligns with your unique requirements.
  • Legacy of Expertise: Our foundation in benchmark and index management is robust. This legacy equips us with the expertise to source, validate, and enrich ETF data with unparalleled precision. It's about ensuring you have the most refined tools for decision-making.
  • Holistic Data Spectrum: Data isn't just about surface-level figures. Our offering delves deeper, from Fund Level Data to Holdings and PCF. The result is a comprehensive data spectrum that is validated, normalized, and enriched, ready for analysis.
  • Industry-Centric Team: The complexities of the ETF landscape require a team that understands its nuances. Our professionals, with experience spanning both the vendor and sell-side, bring a holistic understanding of the industry, ensuring that the data solutions resonate with the challenges faced by portfolio managers.

The complexities of ETF data management are vast, but with the right approach and tools, they can be navigated effectively. By understanding the challenges and leveraging specialized expertise, portfolio managers can transform data into actionable insights, guiding their investment strategies with precision. Rimes is uniquely positioned to offer complete transparency into ETFs with the combination of PCF, holdings and underlying index data. There is no layer of transparency that Rimes cannot provide.

Contact us to find out more about Rimes ETF data management services. 

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