Use Data Quality and Business Impact KPIs
Learning Objectives
After completing this unit, you’ll be able to:
- Explain how data quality KPIs and business impact KPIs support monitoring and prioritization.
- Describe a lightweight methodology to identify fields that matter for a persona, process, and stage.
Turn Monitoring into Action
Monitoring produces evidence that helps organizations understand whether their data continues to meet business expectations. Depending on the monitoring solution, this evidence can include profiling snapshots, reports, dashboards, audit history, alerts, or business and data quality key performance indicators (KPIs). This unit focuses on KPIs and how they impact data monitoring and maintenance.
Defining KPIs is more than just a technical exercise. It’s a team alignment exercise. Much of the value comes from agreeing on which KPIs to use, why they matter, and what “good” looks like. These discussions create shared expectations and focus execution. Without that alignment, a KPI becomes just another metric, and teams are less likely to act on it.
KPIs are not universal. They must be defined in the context of who uses the data, for what purpose, and at what stage of a process. As a result, most objects require multiple KPIs, each tied to a specific persona or business outcome.
Start with a small number of meaningful KPIs that teams understand and agree on. Each KPI should be specific enough to drive action, but broad enough to reflect real usage. As adoption grows, KPIs can evolve alongside the data and the processes they support.
Explore the Two KPI Types
Data Quality KPIs
Data quality KPIs assess whether a dataset (or a filtered subset of records) has the information needed to support a specific purpose.
Examples include:
- Completeness of stage-relevant fields
- Validity of contact points used for outreach
- Consistency of classification fields used for segmentation
Business Impact KPIs
Business impact KPIs reflect outcomes the business is trying to achieve. These KPIs help evaluate whether improving data quality correlates to better results.
Examples include:
- Opportunity Amount
- Close Date
- Service level agreement (SLA) adherence
These KPI types work together. Data quality KPIs help detect data risk. Business impact KPIs help quantify why the risk matters.
Identify the Fields That Matter
Defining fit-for-purpose KPIs requires more than counting populated fields. Luna uses a repeatable methodology to identify which fields actually matter.
Step | Action | |
|---|---|---|
1 | Start with the business process and persona. | Work with business stakeholders to understand the decisions, tasks, or AI interactions you want to monitor. Then review the data available to that persona based on their security access. |
2 | Focus on the records that matter. | Narrow your view to the most relevant records such as recent records, active customers, or a specific segment. Not all records should be treated the same. |
3 | Analyze fields across lifecycle stages. | Check how fill rates and precision change as records move through key lifecycle stages, like opportunity stages or case resolution. Not every field matters equally throughout a process. Comparing stages helps reveal which information becomes important as work progresses. This can show where data improves or breaks down. |
4 | Account for mandatory fields. | Include fields enforced through validation rules or other controls, particularly those required for compliance such as General Data Protection Regulation (GDPR). |
5 | Identify consistently populated and reliable fields. | Fields populated more frequently or reliably across stages often indicate the information users consistently maintain because it supports their work. These fields are strong candidates for KPI focus. |
6 | Group fields by logical or process affiliation. | Organize related fields into meaningful categories. For example, address completeness, qualification checkpoints, or readiness criteria required to progress to the next stage. |
7 | Define simple KPI formulas. | Create distinct formula fields that measure the completeness of each key information category. KPIs should be easy to explain, track, and share with stakeholders. |
8 | Operationalize KPIs. | Add KPIs to dashboards and automated monitoring so you can track changes and act on issues over time. |
Create Record-Level Readiness KPIs
A readiness KPI combines several related fields into a single score for an individual record.
For example, NTO creates three readiness KPIs for opportunities.
- Approval Readiness: NDA signed, supplier agreement completed
- Billing Readiness: Billing contact, billing address, payment terms
- Implementation Readiness: Implementation owner, onboarding date, project kickoff information
Each category produces a readiness score for an individual opportunity. These scores can then be summarized across opportunities to identify trends, compare teams, or measure improvements over time.
Instead of asking whether a single field is complete, sales operations can monitor whether opportunities are ready to move successfully through each stage of the sales process.
Design Readiness KPIs
NTO wants to improve sales forecasting and ensure opportunities progress smoothly through the sales lifecycle. Rather than monitoring every field equally, Luna partners with the sales operations team to identify the information that consistently supports successful deals.
First, Luna reviews opportunity data from the perspective of sales users and compares Closed Won and Closed Lost opportunities. She discovers that successful opportunities consistently contain complete billing and implementation information before they close.
Next, Luna analyzes how field completeness changes across opportunity stages. Qualification information is typically completed early, while billing and implementation information becomes consistently available as opportunities approach closing.
Using these insights, Luna groups related fields into a small number of business readiness KPIs instead of monitoring dozens of individual fields.
One of the readiness KPIs Luna develops measures whether an opportunity contains the information required before billing can begin.
Rather than evaluating individual fields equally, the KPI measures whether each business requirement has been satisfied. Some requirements consist of a single field, while others depend on multiple fields and business rules.
Business Requirement | Evaluation |
|---|---|
Billing Contact | Billing Contact is populated with a name and email address or phone number. |
Billing Address | Required address components are complete based on the country.
|
Payment Terms | Payment Terms is populated. |
Purchase Order | Required only when the customer requires purchase orders. |
Each requirement can evaluate either completeness, validity, or precision. For example, unless you’re using data enrichment to ensure addresses are valid and complete, you cannot ensure a postal code is correct but you can check for minimum and maximum length. NTO has customers in Iceland that use a 3-digit code while the United States can have up to 10 characters.
Luna decides to create distinct data quality KPIs for billing contact and billing address completeness, then creates a composite formula to look at overall billing readiness.
If all applicable requirements are satisfied, the opportunity receives a 100% Billing Readiness score. If one required element is missing, the score decreases accordingly.
Because the KPI evaluates business readiness instead of individual field completeness, it provides a more meaningful measure of whether an opportunity is ready to progress through the billing process.
Reports and dashboards then aggregate these record-level scores across active opportunities, allowing sales operations to monitor readiness trends, identify bottlenecks, and prioritize improvement efforts.
Unit Recap
Effective monitoring begins by understanding the business process, the people who rely on the data, and the decisions the data supports. Profiling evidence helps identify the information that matters most, while business readiness KPIs translate that evidence into measurable indicators aligned with business outcomes.
By organizing related fields into meaningful readiness categories, organizations can move beyond monitoring individual fields and instead measure how well their data supports operational success.
In the next unit, learn how dashboards, profiling snapshots, automated alerts, and stakeholder feedback work together to detect meaningful changes over time and help organizations respond before data quality issues impact business outcomes.
Resources
- Trailhead: Data Enrichment Fundamentals
- AgentExchange: Data Quality Analysis Dashboards (Data Quality KPI example)