The cost of poor metrics: How to rescue your operating model

Amid mounting regulatory pressures, shifting customer expectations, and relentless competition, many financial services organisations are pursuing ambitious efforts to refresh their IT estates. These initiatives are not just about adopting the latest technology – they are intended to enable leaner, more agile operating models that can deliver greater value.

Yet, progress often stalls. Delays and diminishing executive support are just the tip of the iceberg. The real complication? Poor-quality data. Without reliable insights, financial leaders struggle to make the decisions that lead to meaningful results and justify further investment.

So, how can CIOs and transformation directors within financial services strengthen their data quality to underpin transformation and build an operating model that’s supercharged not superficial?

Assess business value

In a fast-evolving business environment, understanding the value delivered by your transformation agenda is critical. Yet leaders often find evaluating an entire operating model an intractable problem, with endless metrics causing decision-paralysis.

Distil what matters

Rather than tracking every metric, target those directly linked to strategic outcomes. For organisations attempting to progress their agile operating models, selecting indicators that tangibly demonstrate increased agility, efficiency, and customer impact is the key to unlocking value. Leaders should start by organising core metrics by outcome area:

• Business agility: time-to-market reduction for new products or features
• Operational efficiency: flow efficiency, bottleneck clearance
• Customer satisfaction: Net Promoter Score, customer retention rates 
• Employee engagement: adoption of agile behaviours
• Financial impact: post-transformation ROI.

Go beyond measurement – drive outcomes

One of the most effective ways to create actionable insight is to then leverage an investment model – a structured approach that links targeted investments in the operating model directly to value drivers. Leaders should research successful transformation initiatives to quantify the business impact of specific transformation initiatives, rather than relying on broad assumptions.

By integrating an investment model with the above five outcome metrics, leaders gain a direct line of sight from investment decisions to operational change and measurable business results. This enables them to identify where to focus efficiency improvements, prioritise activities for automation or redesign, and reallocate resources to maximise return on investment. 

The result is an actionable investment-to-value narrative, grounded in proven best practice. And confident, evidence-based decisions that support robust business cases and sustainable transformation.

Learn from across industries

While financial services’ operating models have unique characteristics, lessons from other industries can offer valuable inspiration. For example, we worked with a European retailer and used this targeted metrics approach to reshape its digital operating model, dramatically reducing time-to-market for digital products, improving end-to-end efficiency, and aligning better with customer-centric strategies.

Build a data-driven culture

After identifying what needs changing, leaders must establish the right data foundation. When it comes to addressing poor data, most organisations instinctively reach for the latest off-the-shelf tools. However, addressing poor data extends beyond buying tools. Our research across more than 30 Nordic organisations shows that there is little correlation between business value and new data management solutions. The real differentiator? Culture.

Embed curiosity, learning, and psychological safety

Ensuring the right governance guardrails are in place is key to building trust, and a workplace culture of experimentation, iteration, and psychological safety. Learning from experience (including failures) should not only be encouraged, but it should also be expected. To create this environment, leaders must proactively embolden team members to voice issues or concerns, building a no-blame culture, and consistently hold team members to account in an equitable way. In an industry where conduct drives culture and decision-making, data becomes the backbone.

Together with Rabobank, we catalysed a shift in their HR department, moving from reactive to proactive workforce management by embedding automation and intuitive dashboards. Through new data science, data engineering, and business intelligence capabilities, the HR team now has real-time visibility of the right data feeds, freeing up time for cross-functional collaboration and strategic thinking. This shift to a data-led culture is just one example of how intelligent enterprises’ can extract maximum value from their data by investing in technology to elevate human decision-making.

Intervene with the right tooling

With precise investment focus and a data-driven culture established, optimising the operating model also requires the workforce to have the right tools to generate meaningful insight at the pace needed to enable effective decision making.

Build the right products with the help of AI

In delivery, there can be a disconnect between product and technology teams, with poor data being passed between the two. AI can address this challenge by taking new ideas from product owners, and policy documentation from compliance departments, and creating user stories to satisfy developers’ ‘definition of ready’. Although this requires human review, it significantly reduces back and forth between these two teams. Similarly, language processing and AI-driven analytics can automate the extraction and monitoring of regulatory requirements, making compliance checking and reporting more efficient.

Manage delivery effectively

A single source of truth is essential for effective delivery enabling meaningful insights, reducing silos, and clarifying dependencies. To manage delivery effectively, prioritise consolidating your build, test, and release management into a unified toolset. Create connections between your teams’ data – linking test reports to user stories, and bugs to releases, to enhance visibility across continuous delivery.

Integrated data makes approval processes smoother, enabling faster, more informed decisions. This is especially important with financially regulated products and services, where the ability to evidence releases, their content and associated sign-off evidence is crucial. As visibility and data flow improve, some organisations find that teams can even perform ‘self-serve’ releases, dramatically reducing manual wait times and accelerating delivery cycles.

Deliver with a productive workforce

The above initiatives are of no value without a performant workforce.  Use codebase monitoring tools to quantify engineering performance and identify if recent changes enhance efficiency, yielding insights in weeks rather than months. The workforce should also be supported to harness AI-powered insight to get actionable and targeted suggestions that improve code quality and maintainability. Monitoring code quality and deploying AI-assisted recommendations will allow teams to improve the reliability, scalability, and security of payment systems – a critical area for banks and fintechs. By adopting these approaches, organisations can accelerate learning cycles, reduce technical debt, and empower teams to deliver results faster and adapt quicker to change.

For example, at a global benchmark and index firm, we addressed key delivery challenges, such as delivery predictability, legacy technology issues, and collaboration among product, operations, and technology teams. By introducing the right tooling and measures we drove a 30 percent increase in productivity over 12 months, resulting in annual efficiency savings of £9 million.

Continuously review metrics that matter

An effective feedback loop, embedded with metrics via automated dashboards, will minimise value management office overhead and provide a holistic view of the impact of each initiative. This allows leaders to have objective conversations with their teams about what’s working well, course correct if necessary, and reward performance.

We embedded this approach in a global bank’s risk function, automatically highlighting where data points strayed outside of pre-agreed tolerances, ensuring timely action was taken. Feedback is the central nervous system of a product operating model – helping teams stay focused on outcomes, align with strategy, and continuously improve.  Without it even well-designed models can drift into ‘feature factories’ that often build the wrong things and don’t deliver value.

Embed dashboard reviews into governance and steering meetings

Integrate the regular review of automated dashboards into governance and steering meetings to ensure that data-driven insights are front and centre in performance discussions. This creates accountability, enables leaders to make objective, evidence-based decisions, and ties directly back to fostering a data-driven culture. Embedding these reviews into formal governance processes helps reinforce continuous improvement, supports strategic alignment, and encourages a workplace culture where data guides learning, adaptation, and skill development.

In today's ever-evolving landscape where incumbents are being threatened by nimble start-ups, agility is a necessity. Organisations must take a focused, culture-centric data-driven, and continuously reflective approach to their existing transformations to drive successful outcomes. The firms that take this seriously will be able to pinpoint their investment cases, learn faster than their competition, and achieve sustainable growth.