Data strategy and governance framework key to extracting value from your investment information

Data and analytics are central to high performing investment management organisations. 85% of financial services firms see data as their key strategic asset, but only 16% rate themselves strong in extracting value from it. A considered data strategy and a robust governance framework is key to realising and exploiting this value.

Why is this so challenging? Because data is generated from multiple sources – custodial data, market data, internal sources, external managers – the data ‘universe’ an investment manager ends up with is becoming increasingly complex. The data is needed to accurately evaluate risks and exposures by drilling down to the level of detail required from large, complex data sets. This can be a major challenge.

So how, on a daily basis, do you bring in 40 to 50 data sets, ensure their accuracy, and then begin to analyse patterns and trends? It could be that, initially, you’re only dealing with 10 data sets from a single custodian to get your core data, but once you layer on to that multi-custodian requirements and market data feeds, internal feeds and look-through data (e.g. for specific asset classes such as loans or unlisted data), you’re quickly facing a more complex challenge.

It all adds up – before you know it, you’re looking at 40 or 50 data sets and it’s much harder to manage.  A lot of time is then required trying to tie Excel spreadsheets and models together so that the data can be effectively analysed.

What it comes down to is this: no matter the size of your business, or how many funds you’re managing, the growth of data has reached a point where systemising it is essential, or you’ll end up buried in it, let alone being able to perform any meaningful analysis on it.

Bringing it all together is the goal, but doing so requires strategy and planning around data growth, so that it’s easier to see the big picture and base decisions around it.

A data strategy includes a data governance framework, which puts a data governance lens over every new feed coming into the Fund and the data they needed to accomplish business goals. The data governance team decide which touchpoints the data will hit during its journey from its source to the consumer, which may be a team of users, a model or a system.

One of our largest customers developed their data strategy by establishing a unit within the fund that focuses on data governance. They ensure that any inbound and outbound data feeds align to the overall data objectives of the fund. Essentially, their strategy hinges on the delivery of all their data into the AlphaCert platform. Data is received using our platform, and it’s then extracted to downstream systems so that our customer can be assured of its cleanliness, integrity and accuracy.

Their strategy for the growth of data and how it’s managed is always aligned to the overall goals of the investment fund itself.

In addition, once data is consumed by AlphaCert, data governance and data management workflow is built in and can be tailored to the individual needs of an investment management business and the data they deal with. In this regard, AlphaCert is a highly effective tool that is used to implement an overall data strategy and data governance workflow, by providing a systemised platform for managing inbound and outbound data feeds and specific functions for compliance and audit (e.g. data approval and daily signoff).

It’s important to remember that data is a strategic asset, and that without a strategic framework of this kind, an investment management business will find the struggle will only increase in managing existing and new data feeds. The complexity of data is not going away; if anything, investment managers are likely to see more data feeds coming through, and like most businesses, will find themselves trying to cope with additional feeds or increased complexity with the same or less resources.

Just bringing in more analysts isn’t sufficient; it’s critical that investment managers recognise the need to manage data effectively, to evaluate what systems and frameworks they have in place, and work out what else is needed. That way, as each new data point comes in – and adds to the growth – it can feed into an overall data strategy. One that’s using a solution to not only manage the data but get it to a point where it’s an asset for decision-making and not a rabbit hole to get lost in.

If you’d like to find out how AlphaCert can help develop your data strategy, schedule a demo and we’ll start a discussion.

Scroll to Top