Enterprise Data Governance for National Specialty Retailer
Turning 1,400 Stores and 22 Million Customer Records Into a Unified, Compliant, Trustworthy Data Asset
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The Client
National Specialty Retailer
This national specialty retailer operates in the home furnishings and lifestyle category, with 1,400 retail locations, a rapidly growing direct-to-consumer e-commerce channel, and a loyalty program with 22 million enrolled members. The company generates approximately 840 million transaction records annually across in-store POS, e-commerce, mobile app, and call center channels.
The Challenge
The Problem
The data quality crisis became visible when the marketing team attempted to launch a personalized email campaign segmented by customer lifetime value (CLV). Three different systems produced three different CLV calculations for the same customers, with discrepancies exceeding 40% for high-value segments. The CMO pulled the campaign. The CTO investigated.
Customer records were fragmented across seven systems with no single source of truth and no consistent matching logic. The loyalty program database contained 22 million records, but an estimated 3.8 million were duplicates, and 2.1 million had incomplete address data.
Product data was equally problematic — the same product might be categorized as 'lighting' in the POS, 'home decor' in e-commerce, and 'accessories' in the loyalty platform. The regulatory dimension added urgency: DSAR fulfillment took 14 weeks on average, well beyond the regulatory timeline.
Our Approach
4 Phases. 20 weeks.
Flynaut built a data governance program across organizational accountability, policy standards, and technical enablement — deploying MDM, PIM, automated quality monitoring, and privacy compliance automation across 14 source systems.
Data Landscape Assessment & Framework Design
5 weeksMapped every system that created, stored, processed, or consumed customer and product data. Conducted working sessions with stakeholders from merchandising, marketing, IT, store operations, e-commerce, finance, and legal. Produced a data lineage map covering 14 source systems, 23 data pipelines, and 2,400 data elements.
Root causes: no authoritative source designation, no standardized data definitions, no quality validation at ingestion points, and no organizational accountability structure.
Data Quality Remediation & Master Data Management
6 weeksDeployed Informatica MDM as the golden record system with a probabilistic matching engine using name, address, email, phone, loyalty ID, and transaction history. Tuned match confidence thresholds against a manually verified sample of 10,000 records achieving 97.3% match accuracy.
The consolidation reduced 22 million loyalty records to 17.8 million unique profiles, eliminating 4.2 million duplicates — more than the initial estimate of 3.8 million.
Quality Monitoring & Stewardship Operations
5 weeksBuilt automated data quality monitoring using Great Expectations integrated into every pipeline. Established a network of 14 data stewards across merchandising, marketing, store operations, and IT with KPIs tied to data quality scores. Implemented Atlan data catalog for self-service discovery.
Data quality is not a project; it is an operating discipline. Monthly governance council meetings chaired by the CDO review quality metrics, resolve issues, and approve policy changes.
Privacy Compliance & Regulatory Readiness
4 weeksRebuilt DSAR fulfillment process using the customer golden record and comprehensive data lineage. Implemented consent management integration (OneTrust) propagating preferences across all downstream systems within minutes through an event-driven architecture.
When a customer opts out of marketing, that preference is enforced across email, SMS, direct mail, and in-app messaging simultaneously — not just the system where the opt-out was recorded.
The Results
Performance That Speaks
Metric
Before
After
Change
Customer Data Quality Score
54%
92%
Duplicate Customer Records
4.2M
<50,000
Product Taxonomy Consistency
61% cross-channel
98.5% cross-channel
CLV Calculation Variance
40%+ discrepancy
<3% variance
DSAR Fulfillment Time
14 weeks avg.
6 weeks avg.
Personalization Campaign ROI
Baseline
340% improvement
Email Deliverability Rate
78%
94%
Data Pipeline Failure Rate
12% monthly
1.4% monthly
Data Stewardship Coverage
0 stewards
14 stewards
Every dollar spent on data quality remediation returned approximately $8.60 in personalization-driven revenue within the first year. The first personalized campaign launched on governed data generated a 22% higher conversion rate than any previous campaign.
Technology
The Stack
Reflections
What This Project Taught Us
Data governance fails when it is treated as an IT project. It succeeds when it is treated as an organizational capability with executive sponsorship, clear accountability, and measurable business outcomes. The CEO's decision to tie data quality KPIs to the annual performance reviews of the 14 data stewards was the difference between a program that gets implemented and one that gets circumvented.
The matching algorithm is only as good as the business rules that define a 'match.' Technology cannot tell you whether a shared mailing address means the same household or two different customers. Those decisions require business context and cross-functional working sessions that no software vendor can automate away.
For retailers specifically, the connection between data governance and personalization ROI is direct, measurable, and large. That ratio makes data governance one of the highest-ROI investments a retailer can make — but only if the governance program is designed to serve business outcomes rather than generate documentation for its own sake.
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