Industry insights

Why SFDR places greater emphasis on your data taxonomy

New regulatory changes indirectly affecting the UK fund management industry will make robust data taxonomy a critical requirement in your organisation. Are you set up for success?

Jamie Nascimento

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July 12, 2021

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min read

Data taxonomy has always been an important part of any data operation and strategy. Keeping your data organised is essential for basics such as reporting and auditing, through to more complex analyses when extracting enterprise value from deeper insights into business performance. However, because of new regulatory developments to which EU and UK fund managers are rushing to respond, it is more important than ever.

In this article, in partnership with Reframe Capital, we’ll consider why a strong data taxonomy is critical in any fund management organisation, and how changes in the regulatory landscape will place even greater pressure on such firms to manage their data. We’ll then look at the type of challenges managers may face, and how the right partner can offer solutions.

What is data taxonomy?

Imagine a shared storage area for personal or business documents and data where there is no clear naming policy, only one folder and everyone uses a different file format.

Now imagine asking one of the team to:

quickly locate, retrieve, analyse and update the latest market data versus your underlying portfolio for one client;
pull together an audit trail of the changes made to a valuation methodology developed 12 months ago, again for an investor request; and
finally, use a specific collection of key data points versus your latest IRRs to analyse performance metrics for a board presentation and audit sign-off.

You’re all thinking the same thing, it’s almost impossible, right? And the pressure is only greater if it has to be done to meet strict investor, board or audit deadlines.

This may be an extreme example, but there will be elements to which most people who have ever faced such requests can relate.

Data taxonomy is the category description of having a clear and relatable vocabulary, around which you can structure single and linked items of data. Taxonomy keeps your data organised by using set criteria, which makes it simpler to find, but also locate related information.

Your data taxonomy must:

Work as a hierarchy
Show how properties of each piece of data relate to others
Follow unambiguous, specific rules to classify each piece of data – a new piece of data can only fit into one category

A robust data taxonomy in your organisation will help you:

Find useful data faster for more informed decision-making
Maintain data quality
Identify patterns and relationships

The Arrival of SFDR

For those who have just arrived from another planet, SFDR (Sustainable Finance Disclosure Regulation) is a relatively new EU regulation that came into effect earlier this year and aims to ensure the fund management industry takes account of sustainability and ESG factors in their workflow. As the name suggests, SFDR imposes standard and comparable product-level classifications and requires disclosures which are designed to support sustainability and inform investor and fund-allocator investment decisions. Simply put, it establishes a framework for the application of harmonised ESG metrics by asset managers and others who operate in and offer their products across the European market.

Rightly or wrongly, SFDR is a regime that the UK have decided not to adopt from EU law post-Brexit. The regulatory requirements do not just disappear however, and apply in full to any UK manager which offers their funds and other capabilities to investors across the EU. For example, a UK firm who acts as a delegated investment manager for an EU fund or markets funds into specific European countries via national private placement regimes will be obliged to make SFDR disclosures.

Fund manager impact

SFDR will place a significant pressure on asset managers as the breadth and quantity of non-financial data required in relation to their underlying investments will drastically increase, as a basis for categorising their products and making investor disclosures. Such data will include, for example, that to determine asset-level carbon intensity and management information to assess “good governance”.

Unfortunately, this just becomes another burden, hurdle and cost-pressure for managers to overcome in operating their business. The impact is keenly felt by the entire industry, from asset management giants to new entrants.

There are several challenges that come into play:

There is no consistent source of data – some managers will pay to access third party databases to streamline their SFDR compliance but others will be required to collect information directly from a business or in respect of an instrument. No doubt, the challenge is greatest for private market firms where the investment landscape is opaque and reliable data is not widely available.
Sourcing data on a “bottom up” basis is uncertain, slow and time-consuming.
The data will be not be standardised or structured as between different types of business and instrument.
Storing such varied data sets on currently available platforms is tough – either they won’t be flexible enough, or you need to rely on another vendor to provide the solution.
Reporting on this information is challenging unless you have the right degree of flexibility and expertise to deliver it error-free.
You will also need to tie the data (and the cost of acquiring it, managing it, monitoring it, applying it and storing it) back into your finances.

The solution

There are five key stages - and questions you should ask yourself - in ensuring you are set-up effectively to deal with these regulatory challenges;

Be clear on the data you need to collect. Ask yourselves: Which data is mandatory based on current guidelines? What data may become mandatory in the future, so you can be set-up for success?

○ Take the time to review this data set and consider user cases. It’s not just about ticking boxes. Are there gaps which may be beneficial to fill now for your own purposes, such as to support trend or performance analysis?

○ Recognise that some data sets may be specific and structured (ie. carbon impact) whilst others will be unstructured (such as a company’s climate change mitigation plans or social factor engagement) and even a great challenge to capture (for instance, data to assess “Do No Significant Harm” criteria).

Where can you collate this information?

○ Will you automate the collection process through an online portal? Is that even possible? Do you need to create a data collection template? Which parts will be manual?

Work with portfolio companies once you have a clear view on the data you require. It is in their benefit to have this information available and the delivery automated.
Where and how will this information be stored?

○ Will you be using an offline model? Who will own this structure? How will the data be reviewed and input?

○ What is the data taxonomy to ensure it is relevant and relatable?

What reports will you need, on what frequency and who will own these?

○ How do these reports pull the data from your structure?

Even with highly unstructured data sets which are typically gathered and ordered manually, utilising technology to help automate the collection, management and application process will drive greater efficiencies.

One solution is to use a data analysis and reporting platform that recognises and enables you to harness the value of taxonomy, ensuring all the data you will need in this new environment is collected and assigned correctly for easy retrieval and analysis.

Our suite of Enterprise Data tools, as a core part of the LemonEdge private capital solution, supports clients data requirements in the age of ESG and SFDR:

Taking varied and bespoke data – whilst tying it back to your financial records
Allowing you to understand, measure and monitor and benefit from the impact of ESG integration over time on your financials
Simplifying and automating data collection from third-party providers through an API portal.