Transform Telecom Operations with Data Quality and Governance - Precisely Data Integrity Suite

Why this matters for Telecom

Your business depends on accurate address data. When addresses are wrong or incomplete, everything downstream breaks such as service qualification, network planning becomes guesswork, and tax calculations become inaccurate.

One of the challenges are the address data comes from multiple sources such as CRM systems, call centers, partner feeds, legacy platforms and each one has its own quality issues. You need a way to standardize, verify, and enrich this data at scale, then reuse it across service qualification, planning, and compliance.

This use case shows you how to build a single, trusted address foundation that powers three critical business functions, without duplicating effort or creating parallel pipelines.

Your data landscape

The telecom domain involves data related to subscribers, call activity, devices, plans, and billing systems. This data supports key business processes:

• Service subscription and availability analysis: Determine which customers can access which services
• Network usage tracking: Monitor voice, data, and roaming activity
• Billing and revenue management: Calculate accurate charges and taxes
• Churn analysis and retention: Identify at-risk customers and opportunities
• Fraud detection: Catch suspicious patterns and protect revenue

At the center of all this is address data. Addresses are the spine connecting customers, service plans, billing records, and network infrastructure. When your addresses are accurate and enriched, everything else becomes possible.

Learn more: Data Cataloging

Who benefits

This use case serves multiple teams across your organization. Each has different needs, but all depend on the same trusted address foundation:

• Service qualification teams: You need to confidently determine whether a customer's address is serviceable. You want to reduce failed installs and rejected orders caused by bad address data.
• Network planning teams: You need to prioritize where to build next. You want planning decisions based on trusted, enriched address data, not guesswork.
• Billing and finance teams: You need accurate tax calculations. You want to reduce revenue leakage and audit risk caused by incorrect jurisdiction assignments.
• Compliance and audit teams: You need to prove that data is accurate and processes are auditable. You want traceability and policy enforcement.
• Data and IT teams: You want one governed pipeline, not multiple ad-hoc fixes. You want to avoid brittle logic spread across systems.

Three connected use cases

All three use cases share the same foundation: verified, geocoded, and enriched address data. You build the pipeline once, then reuse it across multiple business functions.

• Service qualification: Determine which addresses are serviceable
• Network planning: Prioritize where to invest in network expansion
• Tax compliance: Ensure accurate jurisdiction assignment and billing

Use case 1: Service qualification

The challenge

Service qualification failures are often data problems, not network problems. When your address data is incomplete, inconsistent, or unverified, you can't confidently determine whether a customer's location is serviceable. This leads to failed installs, rejected orders, and frustrated customers.

What you'll Accomplish

You'll establish trust in your address data by verifying, standardizing, and enriching it. This creates a reliable foundation for service qualification decisions.

The 7-step process

1. Assess current quality: Open your raw customer address dataset in the Catalog. Look at the Profile view to see completeness issues, format inconsistencies, missing unit numbers, and low quality scores.
2. Establish baseline: Apply data quality rules to measure your current state. Check postal code format, serviceability status, and other critical attributes. This baseline is defensible and measurable.
3. Verify & standardize: Run addresses through verification. DIS confirms these are real, deliverable addresses and standardizes them to USPS format with ZIP+4 enrichment.
4. Geocode: Convert addresses to latitude/longitude coordinates. DIS assigns a PreciselyID—a stable identifier for each location that you can use consistently across all systems.
5. Enrich with building attributes: Add attributes like residential vs. commercial, multi-unit indicators, unit counts, and mixed-use flags. These explain what exists at an address, not just where it is.
6. Apply serviceability logic: Combine quality indicators, geocode confidence, and building attributes to determine serviceability. Express confidence levels, review flags, or eligibility tiers.
7. Prove impact: Compare raw vs. cleansed quality scores. Show how many records improved. Measure the business impact: faster approvals, fewer failures, better analytics.

By fixing address data once, you unlock reliable service eligibility, faster onboarding, and a trusted foundation for planning and billing.

Learn more: Data Quality Rules | Data Enrichment

Use case 2: Network planning and expansion

The Challenge

Network planning decisions are expensive and long-lasting. When your planning is based on low-confidence address data, you make costly mistakes. You might prioritize the wrong areas, miss high-value opportunities, or waste capital on low-ROI buildouts.

What you'll accomplish

You'll use the same trusted, enriched address data from Use Case #1 to prioritize sales opportunities and plan network buildouts more effectively. No new remediation pipeline is required—you're just using the data differently.

The 5-step process

• Start with trusted addresses: Begin with the cleansed and enriched address dataset from Use Case #1. This dataset is now suitable for decisions, not just storage. It's where planning teams should start—not with raw feeds.
• Apply service priority logic: Not all serviceable addresses are equal. A 100-unit MDU may be prioritized over ten single-family homes. Apply business-aware prioritization that considers eligibility, building attributes, and confidence.
• Monitor data trends: Set up observability on your address dataset. Monitor for volume changes, spikes in new addresses, schema drift, and data drift. Catch upstream issues before they skew planning models.
• Prepare for BI tools: DIS ensures your GIS, BI, and mapping platforms receive data they can trust. Coordinates are accurate, building attributes are normalized, and identifiers are consistent.
• Align teams: Planning teams, data teams, and governance leaders all work from the same trusted foundation. Reduce rework and reconciliation loops.

Once you trust your addresses, you can stop guessing where to build and start prioritizing with confidence. The same address foundation that enables service qualification now powers smarter expansion and prioritization.

Learn more: Data Observability

Use case 3: Tax jurisdiction assignment

The challenge

Tax errors are often address errors in disguise. Tax jurisdiction boundaries can change at very granular levels. Without precise address resolution, even small ambiguities can assign the wrong tax rate. This creates audit exposure, customer distrust, and costly remediation.

What you'll accomplish

You'll ensure accurate tax calculations by using the same verified, geocoded address pipeline from Use Case #1. This reduces revenue leakage and regulatory risk while demonstrating compliance to auditors.

The 6-step process

1. Understand address precision: Compare raw address data (limited precision, no ZIP+4) with cleansed data. ZIP+4 is the gold standard for tax jurisdiction determination in the US.
2. Enrichment strategy: Select property-based tax attributes (PAS dataset) or GeoTAX enrichment using precise geocodes. Flexibility reduces long-term technical debt.
3. Enforce quality rules: Apply rules that enforce minimum standards for compliant tax calculation. Check that country is not null, and validate state, county, and city. Quality is validated continuously.
4. Make rules explicit: Use the Governance Center to create a Tax Compliance Policy linked to your address data. Make expectations explicit and auditable.
5. Prove accuracy: Compare raw vs. cleansed records and highlight the tax_jurisdiction_changed flag. Quantify how often bad data led to wrong tax assignments.
6. Achieve ZIP+4 resolution: Achieve ZIP+4 resolution at scale. Higher precision means fewer disputes and audits, and you avoid relying on coarse tax logic.

If your address is wrong or imprecise, your tax calculation is wrong and that's a compliance risk, not just a data issue. By fixing address data once, you enable precise jurisdiction assignment, transparent before/after impact analysis, and governed, auditable tax logic.

Learn more: Data Quality Rules | Data Governance

The shared foundation

All three use cases share common elements that make them powerful:

• Shared observers: Monitor address data trends across all use cases. Catch issues early before they impact service qualification, planning, or billing.
• Reusable quality rules: Define rules once, apply everywhere. Reduce duplicated logic and ensure consistency across teams.
• Policies tied to business terms: Governance that makes sense to your business. Make compliance requirements explicit and auditable.
• Workflow examples: Automate responses to data events. Move from detection to action.

This "build once, reuse everywhere" model reduces duplicated logic across eligibility, planning, billing, and analytics teams.

Operationalize with workflows

Data Integrity Suite moves beyond detection and insight to orchestrating real business actions. Workflows show both system-driven automation and user-initiated processes, common patterns in telecom and enterprise data governance.

Workflow 1: PII detection and notification

• Trigger: New dataset tagged as containing PII
• Action: Automatically send parameterized email notification with contextual metadata (dataset name, domain, source, tags)
• Benefit: Proactive privacy and compliance enforcement with clear accountability

Workflow 2: Dataset access request

• Trigger: User selects "Make a Request" on a governed dataset
• Action: Automatically create and submit access request ticket in Jira with structured context (dataset, requester, business justification, governance metadata)
• Benefit: Faster access with built-in governance, standardized requests, seamless integration with existing tools

Learn more: Data Governance and Workflows

Key outcomes

Here's what you'll achieve by implementing this use case:

• Reliable service qualification: Confidently determine which addresses are serviceable. Reduce failed installs and rejected orders caused by bad address data.
• Smarter network planning: Prioritize buildouts based on trusted data, not guesswork. Focus capital where ROI is highest.
• Accurate tax compliance: Reduce revenue leakage and audit risk by ensuring taxes are calculated based on precise addresses.
• Operational efficiency: Build one pipeline and reuse it across multiple business functions. Reduce duplicated effort and technical debt.
• Proactive governance: Enforce compliance automatically and respond to data events in real time. Reduce manual reviews and tribal knowledge.

What's next

Once you've established your address quality foundation, you can expand your program in these directions:

• Customer master data: Apply the same approach to customer names, phone numbers, and other key attributes. Create a single source of truth for customer data.
• Real-time systems: Expose your data quality pipeline as a real-time service so applications can query serviceability, priority, and tax jurisdiction on demand.
• Network data: Apply quality rules and observability to network infrastructure data. Ensure network planning data is as trusted as address data.
• Compliance posture: Use your governance framework to demonstrate compliance to regulators and auditors. Build audit trails and policy enforcement.