Banks have huge amounts of data and surprisingly few insights. Decisions are often made on manual reports that are weeks old, because the data sits in silos and takes time to pull together. The problem is not a lack of data, but the lack of a single source of truth and fast reporting.
In our experience, banking data analytics only starts to work once data from many systems lands in one place and reports build themselves, rather than being assembled in Excel overnight. We did this for a banking group: from business analytics, through IT support, to generating reports for corporate clients.
Why do banks have lots of data but few insights?
The most common pain points for data decision-makers in banking:
- data silos scattered across systems and departments,
- manual reporting that eats hours and breeds errors,
- inconsistent data quality and no single definition of metrics,
- decisions on stale data, because fresh data arrives too late,
- growing regulatory reporting requirements.
What is a data warehouse in a bank, and why build one?
A data warehouse is one organised place where data from many of the bank’s systems flows in, and from which analytics and reporting draw. We build it to deliver a single source of truth:
- ETL processes that collect and clean data from sources,
- a consistent data model and shared metric definitions,
- efficient reporting and analytics on the same data,
- a foundation for dashboards and predictive models.
How do you build reporting, including regulatory reporting?
Reporting in a bank is not only convenience but an obligation, so we design it to be repeatable and auditable:
- automated reports instead of manual assembly,
- schedules and versioning of reports,
- export in required formats, including for regulatory reporting,
- an audit trail showing where each number came from.
Which dashboards and KPIs actually help run a bank?
A good management dashboard does not show everything, only what leads to a decision:
- key KPIs for the board in one view,
- drill-down from the big picture to the detail (from result to cause),
- current data instead of a monthly lag,
- business and operational metrics side by side.
How do behavioural analytics and prediction support sales and retention?
Once the data is in order, behavioural and predictive analytics become possible:
- analysis of customer behaviour on transaction data,
- predictive models (churn, up-sell),
- segmentation for sales and retention actions.
This is also an area where we draw on group competencies (personalisation/CDP, Synerise) in prediction and personalisation.
How do you keep data quality and compliance (GDPR)?
Analytics is only as good as the data under it, so we watch for:
- data quality (completeness, consistency, correctness),
- data governance and clear metric definitions,
- GDPR compliance in processing customer data,
- security of sensitive data.
Common mistakes when building banking data analytics
In our experience, data projects in banking tend to stumble over the same things:
- building a warehouse “for everything” instead of for specific decisions and reports,
- no single definition of metrics (every department counts its own way),
- treating a report as an end in itself rather than decision support,
- skipping data quality at the start and firefighting later,
- analytics detached from core banking and real business processes.
That is why we start from the question “what decision should this report support”, not “what data do we happen to have”.
How do we do it at RITS?
We do it as a nearshore partner from Poland, with real experience in data and analytics for banking:
- we ran business analytics, data analysis and IT support for a banking group, staffing business analysts, Java developers and core banking systems administrators, among others,
- we delivered report generation as part of a digital platform for the bank’s corporate clients,
- we verify skills before the start, and we build analytics on organised data, not ad-hoc exports.
You can build this with us as custom software or augment your data team with our specialists. When prediction and personalisation come in, we add an AI layer with group competencies, always with people in the decision loop.
Build your own data team or outsource it?
It depends on how much control you want and how stable the scope is. For continuous analytics development, a dedicated team or team augmentation works well; for a specific warehouse or reporting build, custom software. We covered the differences in our article on IT engagement models.
Where to start with organising a bank’s data?
Start from the most important decisions and reports you want to produce faster and more reliably, then work back to the data they need. This direction, from decisions to data, lets you build a warehouse and reporting that are genuinely useful, not “just in case”.
Want to turn your bank’s data into fast reports and better decisions? Let’s talk - we’ll design the warehouse, reporting and analytics for your needs.