Quality Dashboard
User Guide — A Single View of Your Data's Quality
| Last updated: September 2026
Contents
- Overview
- Finding Your Way Around
- Choosing an "As Of Date"
- Top 5 Critical Data Elements by Score
- Top 5 Critical Data Elements by Count/Balance
- Top 5 Business Rules by Defective Exposure
- Data Quality by Dimension
- Where the Numbers Come From
- Quick Reference
1. Overview
The Quality Dashboard is the at-a-glance home for the Quality module. It gathers the data-quality numbers that matter most — which of your important data elements are cleanest, which carry the most records, which business rules are catching the most problems, and how your quality breaks down overall — so you can check the state of your data without opening each screen one by one.
Everything on the Dashboard is read-only: it's a summary you look at, not a place you change settings. When something needs a closer look, you move on to the Scorecard, Rule, or Issue screens to act on it.
What you can see here
- The critical data elements with the lowest quality scores — the ones most worth your attention.
- The critical data elements that carry the most records or highest balances.
- The business rules exposing the most defective records.
- How your data quality splits across quality dimensions (Completeness, Validity, Accuracy, and so on).
WHERE TO FIND IT — The Dashboard is the first screen under the Quality module. Open Quality → Dashboard.
2. Finding Your Way Around
The Dashboard is arranged as four panels in a 2×2 grid, with a single date selector in the top-right corner:
- Top left — Top 5 Critical Data Elements by Score.
- Top right — Top 5 Critical Data Elements by Count/Balance.
- Bottom left — Top 5 Business Rules by Defective Exposure.
- Bottom right — Data Quality by Dimension.
Each of the four panels is laid out the same way: a short ranked list at the top, and a chart below it showing the same figures visually. The list gives you the exact numbers; the chart makes the differences easy to see at a glance.
Each panel is covered below.
3. Choosing an "As Of Date"
The dropdown in the top-right corner lets you choose the reporting date you want to look at. These are the dates on which your data quality was last measured — usually month-ends — listed with the most recent first.
Picking a different date refreshes all four panels at once to show how your data looked at that point in time. This makes it easy to compare one period against another, or to check whether things are improving.
A SNAPSHOT, NOT LIVE DATA — The Dashboard shows the results of the most recent quality measurement for the date you choose. It doesn't recalculate as you watch — the figures stay fixed until the next measurement is run. See Where the Numbers Come From.
4. Top 5 Critical Data Elements by Score
A Critical Data Element (CDE) is a piece of data that is important to your business — the information you rely on to get the job done. Each CDE is given a quality score, shown as a percentage: the higher the score, the cleaner that element is.
This panel lists five critical data elements together with their scores, so you can spot the ones that aren't measuring up. It focuses on the elements with the lower scores — in other words, the ones most worth checking first.
| What you see | What it means |
|---|---|
| Element name | The name of the critical data element. |
| Score (%) | How clean that element is. A higher percentage is better. |
| Column chart | The same five scores side by side, so differences stand out. |
HIGHER IS BETTER — A score close to 100% means almost every value for that element is in good shape. A lower score is a sign that element needs attention.
5. Top 5 Critical Data Elements by Count/Balance
This panel looks at the same kind of critical data elements, but ranks them by size rather than by score — how many records (or how large a balance) each one covers.
It lists the five elements with the largest counts, newest measurement first. This tells you where your biggest volumes of data sit, which is useful when deciding where a quality problem would have the widest impact.
| What you see | What it means |
|---|---|
| Element name | The name of the critical data element. |
| Count / Balance | How many records — or how large a value — that element represents. |
| Column chart | The same five figures side by side. |
VOLUME, NOT QUALITY — This panel is about how much data an element covers, not how clean it is. Read it alongside the score panel: a low score on a high-volume element is worth prioritising.
6. Top 5 Business Rules by Defective Exposure
Business rules are the checks that decide whether your data meets its requirements. When a rule finds records that don't pass, those records are its defective exposure — the amount of data at risk because that rule failed.
This panel lists the five business rules exposing the most defective records, largest first. These are the checks where the most data problems are concentrated, so it's a good place to start when deciding what to fix.
| What you see | What it means |
|---|---|
| Rule | The name of the business rule being checked. |
| Defective exposure | How many records that rule flagged as not meeting the requirement. |
| Bar chart | The same five rules ranked from most to least exposure. |
START AT THE TOP — The rule at the top of this list is catching the most problems. Clearing it usually has the biggest effect on your overall data quality.
7. Data Quality by Dimension
This panel shows how your data quality breaks down across quality dimensions. A dimension is a standard way of describing what kind of quality is being measured. OnCoor uses the industry-standard set:
- Completeness — required information is actually filled in.
- Validity — values are the right type, format, and within expected ranges.
- Accuracy — data matches a trusted source of truth.
- Consistency — related data agrees across the platform.
- Timeliness — data is current and available when needed.
- Uniqueness — no unexpected duplicates.
The pie chart splits your data into a healthy portion — labelled Non-Defective, the share that passes every check — and slices for the exposure found in each dimension. A large Non-Defective slice means most of your data is in good shape; a noticeable slice for any dimension points to where the problems are.
| What you see | What it means |
|---|---|
| Dimension | The quality dimension, or "Non-Defective" for data that passes. |
| Value (%) | The share of your data represented by that slice. |
| Pie chart | The same split shown visually, one colour per dimension. |
THE GREEN SLICE IS YOUR HEALTHY DATA — "Non-Defective" is the portion meeting all requirements. The remaining slices show how much exposure each dimension is responsible for.
8. Where the Numbers Come From
The Dashboard doesn't measure your data itself — it displays the results of the latest quality measurement. Behind the scenes, your business rules and scorecards (set up elsewhere in the Quality module) score your data on a schedule, and each run is stored under an as-of date.
When you open the Dashboard, it reads the stored results for the date you've selected and shows you the highlights. That's why:
- The As Of Date dropdown only lists dates that have been measured.
- The figures don't change while you're looking at them — they reflect the last run for that date.
- If a date looks out of date or is missing, a new measurement may need to be run before it appears here.
For the full detail behind these summaries — every rule, element, and dimension — use the Scorecard screen in the Quality module.
9. Quick Reference
A fast lookup for the most common actions.
| I want to… | Do this |
|---|---|
| Get an overall picture of my data quality | Open Quality → Dashboard |
| Look at a different reporting period | Choose a date from the As Of Date dropdown (top right) |
| See which important elements score lowest | Read the Top 5 Critical Data Elements by Score panel |
| See which elements carry the most records | Read the Top 5 Critical Data Elements by Count/Balance panel |
| Find where the most data problems are | Read the Top 5 Business Rules by Defective Exposure panel |
| See how quality splits across dimensions | Read the Data Quality by Dimension panel |
| Get the full detail behind a number | Open the Scorecard screen |
Source: OnCoor Quality module product documentation, written for end users. For the latest screens and options, always refer to the in-app interface.