First published on The Financial Brand, July 15, 2026
The data conversation in financial services has circled the same territory for about a decade: dashboards, business intelligence vendors, extract-transform-load pipelines, and warehouse architecture. While the tools have improved, the underlying capability gap at most banking institutions has not, and it is hampering financial marketing.
Unanswered essentials
Every digital banking team should be able to answer two questions:
1. Did my campaign work?
2. Where are my users getting lost?
But most cannot, and the gap between asking those questions and being able to answer them has less to do with ambition or budget than with architectural decisions made years before the questions even came up.
Every institution runs marketing campaigns through its digital banking platform and email and other outbound channels. Most can tell you how many people received a message, how many opened it, but very few can tell you, with confidence, what those people actually did inside the digital banking experience.
Common unanswered questions:
Did they sign up for the offered product?
Did they change their behavior in a measurable way?
Did the revenue or retention impact justify the marketing investment?
Key marketing breakdown:
Without end-to-end measurement that connects campaign activity to actual outcomes within the platform, marketing spend stays disconnected from the business results it should produce.
Need to know
1
How quickly bank and credit union marketers can move from a passive approach to data to a proactive analysis and application hinges on information technology architecture.
2
Institutions need to assess where they stand in three key stages of data use maturity. That status dictates how well they can use data today.
3
Moving to a more sophisticated stage may require multiple infrastructure upgrades or a wholesale rethinking of the company’s approach.
The same problem appears in the workflow data. Every digital banking platform has standard journeys: bill pay, transfer setup, account opening, loan application, dispute initiation.
Most institutions have rough completion data—how many users started, how many finished. But very few have the behavioral granularity to know where users dropped off within a workflow.
Was it the first screen? A specific field? A confusing piece of language? Did certain customer or member segments struggle in ways others did not?
Key challenge:
Such questions are practical entry points to the bigger strategic issue every financial institution is working through right now: How do we make the digital channel measurably contribute more to the bottom line?
You cannot improve what you cannot measure. Institutions that cannot answer such questions with precision are running their digital banking platforms in partial darkness.
Assessing Your Bank’s Data Maturity
The clearest way to assess where an institution stands on its data journey is a three-stage maturity model. The stages differ in what they enable. The practical implications of each are distinct.
Getting started
Institutions at this stage primarily work with dashboards and standard reports—out-of-the-box KPIs, auto-populated views, prebuilt reporting templates. The data exists, but it is only consumed in packaged form. The questions an institution can ask are limited by what the dashboard supports.
This is where most institutions are, in the early phases of digital banking maturity, and the dashboard layer is where most decisions are made today.
But the ceiling is real. Standard dashboards cannot easily answer the key questions. That’s because end-to-end measurement and behavioral granularity require data structures that the prebuilt view does not deliver.
Building momentum
Institutions at this stage are pulling raw data out of the digital banking platform into their own data warehouse or business intelligence tool, joining it with other institutional data and running their own queries. The data they receive needs to be timely, structured for analysis, and comprehensive enough to support the kinds of questions a curious analyst might ask.
This is the stage where campaign attribution and questions about user friction can be answered. That is because the analyst can shape the data to fit the question, rather than fit the question to the dashboard.
However, at this stage constraints remain. Typically this includes data feeds that are not complete. Another issue is latency—the time it takes for data to become available after it is generated or requested.
A once-daily batch of CSV (comma-separated values) data supports some analysis. But a near-real-time feed of structured data supports considerably more.
Institutions at Stage two are doing real analytical work.
Leading the pack
Institutions here are working with near-real-time data pipelines, direct query access via mechanisms like Delta Sharing, behavior-based personas, and machine learning models that run on their digital banking activity.
The questions these institutions can answer have moved from retrospective to predictive: What is likely to happen, and what should we do about it before it does?
These institutions are starting to operate the way digital-native financial services companies have operated for years, with data as an active strategic input to decisions, not as a quarterly report on them.
Mike Dawson
Director, Product Management, Lumin Digital
