Building the data value proposition in Financial Services

Banks, Insurers, Wealth Managers, and all financial services organisations face constant challenges when seeking to drive tangible investment in support of data management goals. There is a ranging balance of motivations that drive many investment decisions within the industry:

• Compliance & Control
• Efficiency
• Innovation

Predictably, compliance and control challenges continue to attract a higher degree of investment priority over work perceived to be more discretionary in nature. However, regulations such as BCBS239 can prove counterproductive by encouraging a focus on minimum compliance outcomes, which result in limited real-world improvements to business data practices.

On driving efficiency, senior management often expect progressive uplifts to be achieved as part of ‘BAU’ rather than meriting exceptional investment. This can drive short-termist focus on quick fixes rather than slow-burn structural improvements, leading to mounting technical debt and leaving organisations facing familiar struggles around fragmented legacy architectures as the major efficiency hurdle. Given such drags on investment appetite, it’s clear this theme will not disappear any time soon.

Opportunities to drive innovation from data management stem less from potential monetisation of new revenue streams and more from leveraging analytics to offer management increased insights in volatile environments, and thereby improving the effectiveness of decision making. In Finance functions in particular, AI/ML-based innovations have yet to be seen to show a return on investment and there is still scepticism about the usage of such technologies in risk management where explainability under scrutiny by regulators remains an issue.

Approaches to organising data management

One can argue and debate the relative merits and applicability of a centralised vs. decentralised organisation and funding of data management capabilities. Many of the use cases of interest to FS organisations (e.g., risk management, financial reporting) depend implicitly on aggregation of disparate datasets which are owned within business lines. There is broad recognition of the benefits of “big-hub” centralisation in smaller organisations; less so in larger, more complex ones where control of change budgets is federated, limiting the ability for central data functions to drive outcomes. However, there is potential for grassroots initiatives by local teams acting unilaterally to serve as trailblazers for improvements across the rest of the organisation.

A key driver in successful change initiatives is the role of individual personalities in shaping the organisation’s approach to data change, with passionate advocacy by leaders able to affect a disproportionate impact on outcomes. This in turn speaks to the importance of culture in helping or hindering an organisation’s data agenda.

Making the business case compelling 

Data management professionals are under no illusion that building business cases for data initiatives is easy. Areas which are abstracted away from tangible business outcomes, such as data governance and metadata management are particularly difficult to land with business sponsors.

Furthermore, business cases for significant data investment need to evolve as they are socialised upward through the organisation. At the lower levels, buy-in from stakeholders is often focused on technical benefits – new efficiencies and new capabilities. As business cases moves upward through management levels, narratives are necessarily simplified and are likely to be ‘sold’ to audiences based on more emotive factors – reinforcing the importance of organisational culture in supporting data outcomes.

Conclusions

Data management continues to be an area where under-investment can severely impact an organisation’s ability to improve business outcomes. Despite this, the task of securing funding for initiatives remains a perennial challenge for data leaders and is likely to remain so, even as the industry as a whole embraces data as a key competitive driver. Accordingly, data leaders need to ensure they design business cases effectively in order to maximise their positive impact within organisations.

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