• Michael Storey, Director |
4 min read

Over the past decade, we have seen the growth of cloud-based finance systems and complementary technologies such as automation, learning algorithms and analytical reporting tools. The ever-increasing demand for management information at the executive level is challenging finance functions to provide deeper, more valued insights, in addition to delivering on their financial controller responsibilities.

In our 2021 and 2022 Asset and Wealth Management finance benchmarking, we discussed finance capabilities with the CFOs of over 20 of the largest organisations in the sector. We understand that these finance functions are typically in a state of changing priorities, whether it’s a shift in the regulatory agenda, M&A activity or implementing new processes and tools. A common theme amongst these challenges has been the ability for finance to efficiently generate insights and the relationship between data and systems to be able to do so.

We have identified five ways that can help you develop data-driven insight capabilities:

1) Take time to develop an integrated finance data and systems strategy:

Many asset managers have developed a hybrid systems landscape, investing in a single cloud vendor for their core systems capability (general ledger, subledgers and consolidation). This is often supplemented by integrating ‘best of breed’ technologies for specific functionality (e.g. reconciliation tools, payment platforms, reporting and analytics). These systems facilitate core finance processes, but often the data within them is not sufficient to answer the questions being asked of the finance teams. Business analysts usually need to combine finance data with data outside of their immediate sphere of access, such as upstream data from third party administrators to generate insights. These data sets can often come in varying degrees of granularity and quality. Take for example, assets under management (AUM) and flow data – the AUM metric is fundamental when calculating revenue and profitability, used both internally by management and externally by market analysts. Achieving an aligned view across finance, distribution and customer/client masters can be extremely challenging. Initiatives to look holistically at the data and systems landscape are often shelved when business as usual demands peak. A small investment of time in this area can help unearth the current pain points and identify opportunities to free up efficiencies in the production of management information.

2) Assess where the data and systems skillset reside:

Larger financial services organisations have successfully developed dedicated finance data and systems teams that sit within finance but are aligned with the IT function in terms of standards, development approach and collaboration. These teams typically either have finance professionals with systems skills or systems analysts who have spent a significant amount of time in the finance function, gaining a deep understanding of data from source to report. Smaller finance functions are unlikely to be able to justify a dedicated team; emphasising the need to assess where the skillsets sit between finance/IT and the operating model to support execution of a data strategy.

3) Put data at the heart of the finance vision and align employee objectives:

Systems and tools will only move the dial so far. Developing a data-driven culture requires sponsorship, setting out the principles of how employees treat data and interact with it daily. This requires a cultural change and can fall at the first hurdle if you do not clearly articulate and incentivise the link between value and effort.

4) Encourage a learning culture and mitigate key person risk:

Following the COVID-19 pandemic, new ways of working have emerged. Many employees have reassessed their roles and the nature of the work they do. This has increased the risk of key person dependencies in many organisations. Whilst there is no substitute to years of business knowledge, the loss of a single resource can affect business as usual tasks in the finance function.

Training can often be the first thing that gets deprioritised when team members are busy. Investing and committing to development plans, combining functional learning with data and systems skills helps to spread the knowledge of the team and gain early sight of personal ambitions and interests. Fostering a continuous learning culture and providing an environment to innovate with the latest technology can help to reduce these key person dependencies.

5) Obtain sponsorship at the executive level for data initiatives:

Typically, most finance transformation programmes are sponsored by the CFO with support from the CIO/CTO for alignment on corporate technology strategy. As these programmes can often involve data that spans the organisation, it can spark discussion on data governance and ownership. Where finance teams are partnering with other functions to deliver strategic insight and value, having sponsorship at the executive level can help maintain momentum and aid with prioritisation, ensuring business outcomes are achieved in a timely manner.

Strategy, sponsorship, people, culture and continuous learning are all important elements of developing and maintaining a data-driven function. Here are some key questions to consider when assessing your approach:

  • Do you have a clearly defined finance data and systems strategy for the next 2-5 years?
  • Do you know where your functional data and technology expertise resides?
  • Do your teams have the training and support they need to get the most out of technology investments?

To discuss any of the points above or find out how you can get started with shaping your finance function for the future, please reach out to Michael Storey.