In data science, what are often referred to as “dimensions” or “attributes” can be understood here as signals. Signals describe the specific qualities or dimensions a data point conveys. These qualities can be leveraged to explore anomalies or identify patterns across data sets. In our design context, we treat signals not just as descriptors but as the basis for design decisions—translating them into effective visualization patterns that help communicate the underlying record or transaction with clarity.
Signals as descriptors of a single data point
Every payment embodies at least five descriptive layers: it is transactional (an exchange of value), temporal(happens at a precise moment), contextual (ACH vs. wire, domestic vs. cross‑border, etc.), inferred (risk score or forecast added by modelling), and behavioral (the pattern of how often that payment type occurs). Labelling those layers up front tells us which story the data point can tell and informs the visual grammar—bar, line, heat‑map, gauge—that will communicate it best.
Signals as patterns across many data points
A single payment’s signals matter, but clusters of similar signals expose cost leaks, timing issues, or fraud. We therefore differentiate base‑level signals (attached to raw records) from pattern‑level signals (aggregations and correlations). This distinction underpins our four‑layer hierarchy—Recommendation → Insight (pattern) → Signal (data point) → Raw data—so users can trace every conclusion back to first principles.
In data science, signals refer to meaningful information extracted from raw data. In the context of UX, we use these extracted signals to extrapolate meaning, inform user understanding, and guide design decisions. Thus, for our purposes, signals are both data descriptors and tools that enable effective communication through design.
Signals steer us toward well‑known visualization patterns—bars for magnitude, lines for trends—and inspire new visual forms when conventional charts fall short. Having a shared signal vocabulary lets designers and data scientists jointly decide when to reuse a tried‑and‑true chart and when to invent a bespoke graphic that better reveals a relationship.
| Signal Type | Example in CashPro | Typical / Novel Visual |
|---|---|---|
| Transactional | Fee per wire transfer | Baseline bars or lollipop chart |
| Temporal | 6‑month ACH‑volume trend | Area chart with moving‑average overlay |
| Contextual | ACH cost vs. industry avg | Bullet chart or slope graph |
| Behavioral | User approval timing patterns | Heat‑map calendar |
| Inferred | Predicted fraud probability | Gauge, radial band, or risk badge |
When stakeholders asked “How should we show FX corridor efficiency?” we replied “Which signals tell that story?” By mapping signals first, the team chose visuals with intentionality. Every pixel now earns its place—simplifying complexity while preserving traceability.