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Six Steps to Managing Data Quality with SQL Server Integration
Services
A company’s database is its most important asset. It is a collection
of information on customers, suppliers, partners, employees,
products, inventory, locations, and more. This data is the
foundation on which your business operations and decisions are made;
it is used in everything from booking sales, analyzing summary
reports, managing inventory, generating invoices and forecasting. To
be of greatest value, this data needs to be up-to-date, relevant,
consistent and accurate – only then can it be managed effectively
and aggressively to create strategic advantage.
Unfortunately, the problem of
bad data is something all
organizations have to contend with and protect against. Industry
experts estimate that up to 60 percent or more of the average
database is outdated, flawed, or contains one or more errors. And,
in the typical enterprise setting, customer and transactional data
enters the database in varying formats, from various sources (call
centers, web forms, customer service reps, etc.) with an unknown
degree of accuracy. This can foul up sound decision-making and
impair effective customer relationship management (CRM). And,poor
source data quality that leads to CRM project failures is one of the
leading obstacles for the successful implementation of Master Data
Management (MDM) – where the aim is to create, maintain and deliver
the most complete and consolidated view from disparate enterprise
data.
The other major obstacle to creating a successful MDM application is
the difficulty in integrating data from a variety of internal data
sources, such as enterprise resource planning (ERP), business
intelligence (BI) and legacy systems, as well as external data from
partners, suppliers, and/or syndicators. Fortunately, there is a
solution that can help organizations overcome the complex and
expensive challenges associated with MDM – a solution that can
handle a variety of data quality issues including data deduplication;
while leveraging the integration capabilities inherent in
Microsoft’s SQL Server Integration Suite (SSIS 2005/2008) to
facilitate the assembly of data from one or more data sources. This
solution is called
Total Data Quality.
The 6 Steps to Total Data QualityThe primary goal of an MDM or Data Quality solution is to assemble data from one
or more data sources. However, the process of bringing data together usually
results in a broad range of data quality issues that need to be addressed. For
instance, incomplete or missing customer profile information may be uncovered,
such as blank phone numbers or addresses. Or certain data may be incorrect, such
as a record of a customer indicating he/she lives in the city of Wisconsin, in
the state of Green Bay.
Setting in place a process to fix these data quality issues is important for the
success of MDM, and involves six key tasks: profiling, cleansing,
parsing/standardization, matching, enrichment, and monitoring. The end result –
a process that delivers clean, consistent data that can be distributed and
confidently used across the enterprise, regardless of business application and
system.
1. Profiling
2. Cleansing
3. Parsing and Standardization
4. Matching
5. Enrichment
6. Monitoring
Supporting MDM
Building Support for Compliance and Data Governance
Total Data Quality Conclusion
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