Data Engineering for Banks [Part-2]: Start with a Data Assessment

In Part 1 of the data engineering for banks series, we explored why data engineering has become a boardroom concern in EU banking, driven by real-time payments, DORA, ESG, and a growing reliance on analytics in compliance, risk, and product.

If you haven’t read it yet, visit here > Why EU Banks Need Stronger Data Engineering?

Now the question shifts from why to “where.”

→ Where does your bank stand today?

→ Which systems feed your critical reports?

→ Where does data break, slow down, or get patched manually?

This second article marks the start of the hands-on phase of the series.

We begin with one of the most overlooked but high-impact practices in modern banking: data assessment.

Four Layers Where Gaps Usually Hide

When assessing your data landscape, certain hidden layers tend to slow down or complicate banking operations. Understanding these helps you see where real improvements can happen:

What Does a Banking Data Assessment Look Like?

Take a simple retail loan journey.

A customer applies online, data enters your origination system, moves into risk scoring, then into core banking, and finally updates the reporting layer.

If any part of that flow is slow, inconsistent, or unclear, it affects the customer, the regulator, and your internal teams.

We usually assess four key signals:

➡️ Freshness: Is the data recent enough to act on, or are teams working with yesterday’s picture?

➡️ Flow Clarity: Do you know how data moves from source to output, across all your products and channels?

➡️ Transformation Logic: Are business rules applied consistently across departments?

➡️ Usage Confidence: Do product, risk, and compliance teams trust and rely on the same outputs?

When these are clear, data becomes a performance asset.

This is what strong data engineering helps unlock: an ecosystem that behaves as well as it looks on paper.

How Data Engineering Strengthens the Foundation

Data engineering sits between your data producers (applications, sensors, third-party APIs) and your data consumers (dashboards, compliance reports, ML models, or customer-facing interfaces).

Its job is to design, build, and maintain robust pipelines that ensure the right data gets to the right place, in the right format, and at the right time – with zero manual intervention.

Start with a Data Understanding Review

Before modernizing your data systems, the first step is to see clearly. That’s what a Data Understanding Review does.

The process is simple:

1️⃣ Landscape Walkthrough: We map your existing data flows, sources, and dependencies.

2️⃣ Performance Scan: We surface where delays, gaps, and breakdowns occur.

3️⃣ Foundation Alignment: We identify where engineering interventions can unlock speed, clarity, and control.

Quick Assessment Checklist for Your Bank’s Data Landscape

Here’s a simple way to get a sense of where your data landscape stands today.
Score each on a scale of 1 to 5:

→ 1 = Big concern (we talk about this issue often)

→ 2-3 = It works, but room to improve

→ 4-5 = It’s sorted, we trust it

🟩 What to do with your scores:

● 4–5 → Solid. These areas can be scaled and improved further.

● 2–3 → Common gaps. Worth deeper engineering attention soon.

● 1 → Critical. Fix now before moving into bigger architecture or platform changes.

Top FAQs on Data Assessment for Bank