In the world of financial services, data has long been the raw material for achieving a competitive advantage. It underpins everything from compliance and risk modeling to product innovation and customer engagement. But understanding how to act on that data hasn’t always been easy.
Using data to drive business change
Underlying all of this is the relationship between data, cost, and innovation. To drive both change and results, a connection must exist between all three of these pillars. Importantly, building this connection is not automatic.
At the root of this is a business challenge involving change: how to ensure that democratized data use doesn’t become unaccountable and unsustainable, driving up costs and risk.
How Inter helped drive change using data
Inter, a leading bank in Brazil, faced this problem head-on.
Their solution was to create a new model for internal accountability and visibility based on a democratized view of data, utilizing a data mesh architecture. The result was a chargeback model that leveraged data to achieve results and drive change. This approach offers timely lessons for any organization navigating data scale and hybrid environments.
Their journey provides a timely case study for financial services leaders thinking beyond dashboards and warehouses, toward enterprise-wide data intelligence.
Highlighting the universal value of data
The Inter story is a universal one. It is particularly applicable to other financial institutions, but more generally, it speaks to the universal need for data to drive accountability at any organization. As institutions seek to modernize their operations and comply with evolving regulations, a critical question arises: How can data access be both democratized and responsibly managed without sacrificing control, performance, or fiscal accountability?
This blog will explore the problem in depth and demonstrate how data can be leveraged to drive organizational change from within, fostering both insight and accountability.
From data access to strategic accountability
Like many institutions, Inter had built a robust foundation for data democratization, underpinned by data governance. Teams across the organization had access to rich datasets supporting everything from risk analysis to marketing optimization.
However, as access increased, so did infrastructure consumption, with no corresponding rise in ownership or accountability. This drove up cloud costs, but more importantly, it represented a disconnect between the organization and its data infrastructure.
Efficiency relies on data visibility
All of this changed with a pivot towards greater data visibility. This strategic insight recognized that data access without visibility creates systemic inefficiency. Usage expands, but value becomes harder to measure. Infrastructure costs rise, but without effective governance, optimization becomes a reactive rather than proactive process.
Rather than pull back on democratization, Inter reframed the problem.
They introduced a chargeback model to align usage with accountability, not just to reduce cost, but to increase transparency across the organization.
The chargeback model: A strategic lever
In strategic terms, the chargeback model is more than an internal billing system. It is a business cultural intervention that ties together the technological and the organizational. By tying infrastructure consumption to departments and users, Inter created a feedback mechanism that incentivized better behavior without imposing artificial limits.
The image below shows the approach used by the chargeback model. It connects multiple sources using a data lakehouse, while including discrete, governed data domains. The result is comprehensive visibility into data usage, enabling accountability and establishing an organization-wide cost center.
Data collaboration comes alive
How does it work? In this model, each department can assess its data footprint, link it to business outcomes, and make more informed trade-offs, even with high-velocity data. Using this model, budgeting is evidence-based. Training efforts became more targeted. Operational silos began to erode as teams understood how their data usage fit into the broader enterprise picture.
The image below illustrates how responsibility for the data platform was equitably distributed across domains based on usage.
Direct, measurable value achieved
For leadership, the benefits were clear: shared visibility leads to shared responsibility. This is a principle every data-driven financial institution can apply, especially in environments where cost control, compliance, and performance must coexist.
The following image illustrates the quantitative impact of the chargeback model at Inter. While the examples below use sample data to simulate Inter’s environment, similar impacts were observed in production.
Enabling enterprise analytics through strategic data design
The benefits have also been strategic. While the chargeback model addressed near-term needs, it also catalyzed long-term readiness for future projects. For data to be effective in any organization, it requires governed, context-rich, cross-domain data access.
This is where Inter’s platform transformation becomes strategically significant. Their focus on cost accountability naturally led to:
- A unified view of data usage across cloud and on-premises systems
- Domain-based data ownership and structured access controls
- Infrastructure insights that can inform both human and analytic workloads
This approach sets them up for success. It creates the preconditions for an expansive, versatile operational layer, not just a bolt-on capability. Financial institutions exploring data adoption would do well to recognize this pattern: analytics maturity depends on architectural maturity, which starts with visibility, governance, and interoperability.
Strategic Lessons for Financial Services Leaders
Inter’s transformation yields several actionable takeaways for CIOs, CDOs, and heads of analytics and architecture:
1. Make usage visible before you make it efficient.
Visibility creates the conditions for voluntary optimization. Start with visibility and the rest will follow.
2. Align data consumption with value creation.
Budgeting, prioritization, and training should reflect real usage patterns. Planning should be grounded in reality, and that requires a feedback loop between usage and the data itself.
3. Treat governance as a design principle, not a retrofit.
If you plan for analytics growth, you must plan for governed access and accountability. Data usage cannot scale without the necessary data access and governance to support it.
4. Design for coexistence.
Hybrid infrastructure isn’t a compromise. It’s the necessary operational reality in financial services. This means finding a place for both cloud and on-premises workloads.
5. Use architecture to enable strategy.
Your data platform is not just a system; it’s a strategic asset that determines the possibilities of your business. Investing in a data foundation that grows with you is a prerequisite for your success as you scale.
Building a data-led organization helps drive efficiencies and accountability
Financial institutions are on the verge of a broader shift, from a top-down, detached approach to serving analytics infrastructure to a future of data democratization and engagement led by real need and real usage.
But that shift won’t be powered by ambition alone. It will be powered by infrastructure that supports agility and integrity at scale.
Ultimately, Inter’s story is not about tools or dashboards. It’s about recognizing that data architecture is business strategy, and designing it to support accountability, innovation, and long-term transformation. This transformation occurs both at a technological and organizational level.
For every financial services organization seeking to evolve, this means that the next step is not just technical. It’s strategic.
