List of SAS Data Quality Customers
SAS Institute
100 SAS Campus Drive,
Cary, 27513-2414, NC,
United States
Cary, 27513-2414, NC,
United States
1 800-727-0025
Professional Services
13500
$3.5B
Since 2010, our global team of researchers has been studying SAS Data Quality customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased SAS Data Quality for Master Data Management from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using SAS Data Quality for Master Data Management include: Db Insurance, a South Korea based Insurance organisation with 4700 employees and revenues of $11.35 billion, Toyota Motor Credit, a United States based Banking and Financial Services organisation with 3800 employees and revenues of $979.0 million, IAG New Zealand, a New Zealand based Insurance organisation with 4000 employees and revenues of $600.0 million and many others.
Contact us if you need a completed and verified list of companies using SAS Data Quality, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The SAS Data Quality customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
Apply Filters For Customers
| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
|---|---|---|---|---|---|---|---|---|---|---|---|
 |
Db Insurance | Insurance | 4700 | $11.4B | South Korea | SAS Institute | SAS Data Quality | Master Data Management | 2023 | n/a |
In 2023 Db Insurance implemented SAS Data Quality as the Master Data Management foundation within a SAS-built analytics platform on SAS Viya to unify decades of policy, claims and customer data. The deployment was explicitly scoped to support enterprise fraud detection use cases across the finance and claims functions in Korea, supplying consolidated identity and policy records to AI and network analytics workflows.
SAS Data Quality was configured to perform data profiling, cleansing, standardization, entity matching and survivorship to assemble consolidated master customer and policy records prior to analytic consumption. Data quality rule management and stewardship workflows were applied to enforce standardized attributes and to automate data-quality scoring, enabling repeatable data preparation for the downstream SAS Viya analytics stack.
Operational integration focused on ingesting long-running policy, claims and customer repositories and producing a unified master data layer that fed the fraud detection models and network analytics. The implementation covered the finance and claims operational domains in Korea, with data stewardship and operational owners established to manage ongoing consolidation and exception remediation.
Governance controls included centralized data-quality rules, stewardship ticketing for exception handling and periodic profiling to maintain record survivorship logic. According to SAS sources, the unified-data approach driven by SAS Data Quality produced dramatic improvements in detection accuracy and analysis speed for enterprise fraud analytics.
|
 |
IAG New Zealand | Insurance | 4000 | $600M | New Zealand | SAS Institute | SAS Data Quality | Master Data Management | 2013 | n/a |
In 2013, IAG New Zealand implemented SAS Data Quality as part of its Master Data Management program in the New Zealand region, targeting customer address data to support underwriting and risk based pricing workflows. The initiative was scoped to the insurer's customer address repository and the pricing and underwriting business functions responsible for risk assessment.
SAS Data Quality Desktop and SAS Data Management capabilities were configured to parse, standardize, cleanse, and validate address attributes, and to perform matching and deduplication. The deployment emphasized address parsing rules, reference data normalization, automated validation pipelines, and staged cleansing to prepare records for downstream spatial processing.
Validated address outputs were routed into geocoding processes to improve location precision consumed by pricing models and underwriting decisioning, covering IAG New Zealand operations across the country. Data flows were integrated into existing risk assessment processes so that standardized address attributes became the canonical input for geocoding and risk scoring.
The project produced a rapid improvement in geocoding match rates, increasing from about 70% to roughly 85 to 90% within about a month, and it catalyzed a broader data quality and governance program at the insurer. Subsequent governance activity expanded to include standardized address reference data, ongoing quality monitoring, and formal stewardship procedures to sustain address data quality.
|
 |
Toyota Motor Credit | Banking and Financial Services | 3800 | $979M | United States | SAS Institute | SAS Data Quality | Master Data Management | 2025 | n/a |
In 2025, Toyota Motor Credit implemented SAS Data Quality as a Master Data Management solution to support CRM and finance functions. The deployment focused on consolidating fragmented customer records to create a single-customer view that would support CRM, predictive modeling and personalized financing workflows. The implementation narrative references Toyota Financial Services Italia, which centralized customer data on the SAS Viya platform and documented consolidation and rapid rollout of predictive models in Italy, informing the MDM design assumptions used here.
SAS Data Quality was configured to provide data cleansing, standardization, deduplication and entity resolution capabilities, enabling master data consolidation and survivorship rules to produce canonical customer records. Configuration emphasized persistent customer identifiers and standardized attribute schemas to support downstream CRM and finance processes. These functions reflect standard Master Data Management practices applied to customer master data consolidation and data quality enforcement.
Architecturally, the work is described on the SAS Viya analytics platform with SAS Data Quality operating as the master data consolidation engine feeding CRM and predictive modeling pipelines. Integrations were oriented toward feeding cleansed master customer records into CRM and analytics environments and supporting rapid deployment of predictive models within the SAS Viya stack. Operational coverage in the source is centered on CRM and finance areas in Italy, where the consolidated single-customer view underpins personalized financing and predictive scoring use cases.
Governance and process changes emphasized centralized MDM stewardship, standardized data quality rules and model rollout processes that leveraged the consolidated customer view. The documented outcome was a single-customer view implemented on SAS Viya and supported by SAS Data Quality, enabling CRM, predictive modeling and personalized financing activities in the noted Italy deployment.
|
Showing 1 to 3 of 3 entries
Buyer Intent: Companies Evaluating SAS Data Quality
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating SAS Data Quality. Gain ongoing access to real-time prospects and uncover hidden opportunities.
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
|---|---|---|---|---|---|---|---|---|
| No data found | ||||||||
FAQ - APPS RUN THE WORLD SAS Data Quality Coverage
SAS Data Quality is a Master Data Management solution from SAS Institute.
Companies worldwide use SAS Data Quality, from small firms to large enterprises across 21+ industries.
Organizations such as Db Insurance, Toyota Motor Credit and IAG New Zealand are recorded users of
SAS Data Quality for Master Data Management.
Companies using SAS Data Quality are most concentrated in Insurance and Banking and Financial Services, with adoption spanning over 21 industries.
Companies using SAS Data Quality are most concentrated in South Korea, United States and New Zealand, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of SAS Data Quality across Americas, EMEA, and APAC.
Companies using SAS Data Quality range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 0%, large organizations with 1,001-10,000 employees - 100%, and global enterprises with 10,000+ employees - 0%.
Customers of SAS Data Quality include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified SAS Data Quality customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Master Data Management.