The Short Answer
Data quality isn't an inherent attribute of data; it's a result of processes. Therefore, measurement that isn't tied to a business impact and assigned an owner changes nothing. Instead, it generates a report someone briefly reviews quarterly and gives a nod of approval.
An effective "Scorecard" consists of only four to six metrics, each with a defined threshold, an owner, and a corrective action. The difference between a Scorecard and a report is that with the former, every red number triggers someone into action.
The Five Dimensions — And What They Truly Measure
| Dimension | What it Checks | When it's Critical |
|---|---|---|
| Completeness | Percentage of filled fields that drive decisions | Always |
| Validity | Conformance to format rules and valid values | Integrations, Regulations |
| Uniqueness | Duplication at an entity level | Before migration and after mergers |
| Timeliness | How current data is compared to reality | Forecasting, Service, Collections |
| Consistency | Whether the same data is identical across systems | Multiple systems and financial reporting |
Organizations almost always begin with the first three. Timeliness and Consistency become relevant when additional systems rely on the CRM – and that's precisely when failure in these areas becomes most costly.
Completeness: Not Every Field Is Worth Measuring
Measuring the fill rate for 300 fields produces a meaningless number. The correct approach is to define a small "field package" for each main process – five to eight fields without which the process cannot function – and measure only those.
It's crucial to also include artificial completion checks: the percentage of records where a field was suspiciously filled with a repetitive value (e.g., a dot, a dash, "unknown"). This is often the first sign that a defined rule is hindering work rather than improving it.
Timeliness: The Dimension Everyone Skips
Data can be complete, valid, and unique – and simply no longer accurate. A customer status field untouched for 14 months isn't data; it's a memory. Measurement is simple: the distribution of time since the last update for critical fields, compared to how quickly reality genuinely changes.
In sales opportunities, this directly translates to forecast quality: the percentage of open opportunities whose close date has already passed is one of the strongest and fastest-to-calculate metrics.
From Threshold to Action: What Happens When a Metric Is Red
For each metric, three levels are defined – Green, Amber, Red – and an action for each level. Amber triggers a team review; Red triggers a correction with a target date. Without such a definition, the metric becomes information rather than a management tool.
The actions themselves should be varied: sometimes the correction is a one-time cleanup, sometimes a change in workflow, and often the right solution is to remove the field altogether – because no one actually needs it.
For additional background on duplicates, see Salesforce Data Deduplication, and on structure that generates quality, see Salesforce Data Model Design.
Scenario: An Insurance Company That Measured Everything And Improved Nothing
An insurance company built a quality dashboard with 34 metrics. It ran for a year. No metric significantly improved because there was no ownership: the dashboard belonged to the BI team, and the fields belonged to agents.
In the second stage, the dashboard was narrowed to four metrics: the fill rate for the signing field package, the percentage of policies with an overdue renewal date, the duplication rate at the insured level, and the percentage of failed email deliveries. Each metric was assigned a regional manager with a quarterly target, and the metrics were presented in sales meetings, not IT meetings.
Within two quarters, two metrics crossed their thresholds. The third metric didn't budge – and investigation revealed that the field was required on a form agents filled out after closing a deal, i.e., at a time when they had no incentive. The solution was a change in the process's placement, not an additional validation rule.
Common Risks and Preventive Actions
| Risk | How it Appears in Practice | Preventive Action |
|---|---|---|
| Too many metrics | A dashboard no one acts upon | Four to six metrics with owners |
| Metric without a threshold | Debate over "is 78% good?" | Threshold derived from business impact |
| Ownership by IT | No behavioral change in the field | Business owner for each metric |
| Validation without measurement | Fields filled with dummy values | Measuring artificial completion |
| One-time measurement | Temporary improvement that regresses | Regular, periodic Scorecard |
How to Measure Success
| Area | What to Measure | Check Frequency |
|---|---|---|
| Completeness | Fill rate for process field package | Monthly |
| Timeliness | Median time since last update | Monthly |
| Uniqueness | Estimated duplication rate | Quarterly |
| Impact | Complaints, integration failures, forecast accuracy | Quarterly |
Scorecard building and operational process are conducted as part of our Integrations and Data Service.
Checklist for Establishing Measurement
- ☐ No more than six metrics selected
- ☐ Each metric has a defined field package, not the entire object
- ☐ Each metric has a threshold derived from business impact
- ☐ Each metric has a full-name business owner
- ☐ Actions defined for Amber and Red levels
- ☐ Artificial completion is also measured, not just completeness
- ☐ Baseline established before improvement begins
- ☐ Report presented in a business forum, not technical
- ☐ Checked whether a problematic field is actually necessary
- ☐ Periodic review for the list of metrics itself determined
Professional Resources
- Salesforce Data 360 — https://www.salesforce.com/data/
- Salesforce Data 360 Architecture — https://architect.salesforce.com/docs/architect/fundamentals/guide/data-360-architecture.html
- HPI Pro – Integrations and Data — https://hpi.pro/integrations-data