Featured White Paper
Data Quality Management Considerations in Master Data Management Structures
Learn about the continued intransigence of corporate data quality problems and how such data quality issues can be resolved within the MDM and CDI implementation context.
Featured Case Study
WellPoint Inc. Speeds Response Time with StreamWeaver®
Read how the WellPoint Inc. health benefits company used StreamWeaver® to respond to business users’ requests in hours or days instead of weeks.
Featured Demo
Customer Data Quality Solution for SAP
View a Macromedia Flash (1.67Mb) demonstration of our Customer Data Quality solution for SAP.
The Data Quality Process
Achieving success in your customer data quality efforts requires a comprehensive, structured approach. Our solution incorporates a systematic approach that takes data from its source through the key steps in the data cleansing process and finally to its target application.
This eight-step approach incorporates the following:
Access
Often, data exists in multiple, disparate widely-distributed sources. Your data quality solution should include an ability to connect to source data either directly or through integration with your existing data access technologies. Our data quality solutions are built upon a full-featured data integration platform.
Interpret
The ability to recognize and understand data may be one of the biggest challenges in implementing a successful data quality program. Stored data often exists in a wide variety of formats. In many cases, individual elements within a given record are commingled in a single field. Many records also contain spurious data. Before any data quality processes can be implemented, the source data must be parsed into appropriate components so that it can be understood by the data quality systems. Our solutions employ sophisticated pattern matching and powerful data manipulation functions that enable any kind of data to be separated into its appropriate components.
Standardize
Standardization is a critical process for two important reasons. First, you may need to standardize certain data elements such as name and address fields in order to improve mail deliverability and qualify for postal discounts. More importantly, the standardization of all data elements is necessary to achieve the highest possible results for matching and identifying relationships between records.
Validate
Standardized data is not necessarily accurate data. In the validation process, information is checked against known, up-to-date reference data for correctness. The sources used for this process may include regulatory bodies (e.g., United States Postal Service), third-party data providers (e.g., Dun & Bradstreet) or a company’s internal reference sources (e.g., accounting data).
Match/Identify
Marketing not only depends on accurate data, it is also important to understand the relationships between records. Recognizing customers, duplicates and households across data sources is critical for success. Once data has been parsed, standardized and validated, the matching process can begin. Our applications apply sophisticated matching algorithms to identify potential duplicate records or relationships between records — whether the data is name and address in nature or any other type of customer information — and can be easily adjusted so that different business units can achieve their desired results.
Consolidate
Once duplicate records have been identified, you may wish to consolidate records in order to eliminate redundant activities and improve communications. Our technology provides users the highest degree of flexibility and control in determining how to link or merge duplicate records, so you can create the most accurate and complete record from any collection of customer information.
Enhance
Our technology delivers a variety of information to be appended during the data cleansing process, including:
- Phone number for a business or individual
- Customers who have recently changed addresses
- Advanced demographic intelligence
- Point-level geocoding, which determines the specific latitude and longitude of an address
- Tax jurisdiction assignments
- Spatial analysis including zone determination, such as flood zones, sales zones, etc.
Deliver
A comprehensive solution will be able to deliver the data in an automated, well-integrated fashion to applications, databases or other operational data stores. Our platform provides for both batch and transactional (real-time) systems so that all entry points for data can be covered by the data quality process.
Learn more about our powerful industry applications