In our previous article, we made the case for Finance to take stewardship of enterprise data. The benefits are compelling. Data is the raw material of Finance. Quality assured enterprise datasets managed under the proven stewardship of Finance can be used to generate insights to inform and energise business decision making.

So how does Finance build and operate such a capability? Here we define four pillars that a Finance data strategy should consider, and define the steps needed to realise the benefits across the bank.

Trends and innovations in banking

In Switzerland and throughout Europe, banks are going through the most innovative times anyone can remember. Increasingly, consumers are embracing the convenience of digital channels. Digital innovation generates opportunity − customers leave an electronic trace of all their interactions with their bank. The immense volumes of available data are a never-before-seen resource with which to build fresh insights into customers’ financial behaviour and tailor new personalised products and offerings.

The economic winds are changing. After a decade of benign inflation and low or negative interest rates both are now rising. Customers are responding by adapting their financial behaviour, and for bank Finance teams forecasting has become harder. Experience and instinct continue to play their part, but for smart banks the data trails of their customers reveal tell-tale signs of where they are heading and inform the bank’s response.

The traditional skills of Finance in data stewardship mean that it is in the best position to capture the benefits for their organisation of the advances in data analytics. And the opportunity extends beyond existing business lines into new markets in data. We see five main areas of opportunity:

Figure 1: Finance data leveraging opportunities

Realising this opportunity requires a structured approach. A Finance data strategy is the roadmap to becoming an insightful bank.

The four pillars of Finance data strategy

We believe there are four key pillars for the design of a successful Finance data strategy:

• The scope of products and services for which insights can add most value
• The stakeholders operating along the data value chain
• The data architecture
• The enabling technologies

Figure 2: Four pillars of a Finance data strategy

Don’t drive blind. Decide from the outset the decision-support insights that Finance needs to generate. Collaborate across the  business to identify where fresh data-driven insights could yield the highest return.

The list of insights can be extensive. For example:

• Is there an untapped revenue stream in our product offering?
• Can we learn more about our clients?
• Can we monetise our data by collaborating with fintechs?
• What insights can further inform our AML capabilities?

We have identified multiple use cases which, taken together, could form the scope of a Finance data strategy:

Figure 3: Finance data strategy use cases

Determining which insights are needed shapes the design of the remaining three pillars of the strategy.

Three key factors are needed to support data stakeholders in delivering the outcomes of a winning Finance data strategy:

• a well-defined process framework along the data value chain
• data-centric governance
• a strong data culture

Well-defined process framework

Acquire, integrate, analyse. Each step in this process requires a distinct skillset. The acquire-and-integrate phase depends on the attention to detail which is a hallmark of financial stewardship and underpins accuracy. Analysis requires a mindset of questioning and curiosity to hunt for fresh insights and apply a storytelling skill to convey them to decision-makers. Technology has a role − interactive, connected planning dashboards are an example of how Finance is able to rapidly adjust its projections of the bank’s performance. However the real step forward is in the new skill of the Finance team to bring the numbers to life for the business and to provide insights for taking action, for client reach-out or more precise planning.

Data-centric governance

People along the entire data value chain should be given clear responsibilities. From identification to publication, actors must understand the processes within data management, communicate their importance to executing teams, and close gaps that could lead to inaccurate or incomplete data.

Strong data culture

In an insightful Finance team, data management skills are at a premium. Prowess in data management will be a hallmark of the successful finance teams of tomorrow. A sustained focus on data management as a key Finance capability is needed to nurture these skills.

Appropriate data architecture is a foundation for capturing and integrating data from multiple sources. The challenge often lies in the sheer scale of the architecture required to manage enterprise-wide volumes of information. Whilst different design options are available, rigorous analysis is needed to avoid potentially costly mistakes in selecting the wrong architecture, tool decommissioning, process redefinition and governance redesign.

We identify three distinct architectural models which would enable banks to consolidate enterprise-wide data into usable resources and to scale their analytical capability.

Figure 4: Finance data architecture models

Technology is a key enabler of insightful Finance, and the pace of innovation accelerates, the focus must shift from finding any technology to finding the right technology. Banks which invest resources into finding the right tools to analyse, identify and deliver insights will build a competitive edge over their peers.

A list of all the available technology would be too long to include here.  We can narrow the scope to key areas relevant to data analytics which fit in with the mission of insightful Finance. Powerful tools can be found along the entire data value chain, with advanced innovation particularly in the areas of predictive planning and AI-enabled data visualisation.

Figure 5: Examples of Finance data technology

The four pillars of a winning Finance data strategy are interconnected parts of the same strategy. A decision in one area will impact the others, and a comprehensive approach is needed to maximise the chances of success.

We can help you assess your data needs and define a solution that best fits your vision for Finance. We can assist you in implementing a data strategy which transforms your Finance function into an insights-driven partner for the business.

In a one-day Data Strategy Lab, we can help you define the insights that you would like to build, and in a four-week Data Strategy Assessment, we can assess your current state and define your transformation path to an insightful finance function.

The goal of an insight-generating Finance capability is nearer than you might think.