February 2010 Archives

Data Quality Tips from Our Experts

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By Tim Sidor

DQT Product Specialist

 

Speeding Up MatchUp Object Incremental Processing

The Incremental Deduping Interface allows multi-users to simultaneously verify existing records and add new ones. This may come at the expense of speed, however, because each record processed must be immediately added (inserted) to the key file, so each running process can see the newly updated key file - ensuring accurate data storage.

But what if your database is a single-user or single entry point application? Timing and order of comparisons isn't important, so why take the speed hit?

The Incremental Deduper now includes a group of Transaction functions, allowing the process to accurately match new incoming records while delaying them from being physically written to the Incremental database (keyfile) until you send a commit call to the process. This will greatly speed up processing when adding large numbers of records and give you the flexibility to rollback a series of added keys if problems arise with the actual data.

Incremental.ClearMappings();
Incremental.AddMappings...

 

Incremental.BeginTransaction()

while (!Database.Eof()) {

Database.Record.Incremental.BuildKey()

Incremental.AddRecord()
Database.NextRecord()

If error then

Incremental.RollbackTransaction()

return}

Incremental.CommitTransaction()

 

Calling this transaction delays the insert calls, while deduping operations remain the same, thus saving processing time without compromising accuracy.

Remember, transaction processing SHOULD NOT be used if multiple threads/processes/users/machines are accessing the same Incremental database!

 

SMARTech's main challenge was integrating disparate data sources into a data-centric hub for the purpose of consumer and constituent engagement. It was essential to utilize a master data management solution that would successfully allow SMARTech to gain a single, accurate, organized view of their customers by consolidating data across different data sources.

The company also needed a strong technical support team that would aid in the implementation of a master data management solution, and provide quick replies to any issue that would arise during the transition process.

Melissa Data's Total Data Quality Integration Toolkit (TDQ-IT) was the perfect solution. TDQ-IT is a complete enterprise platform that works within the SSIS data fl ow to deliver a wide range of data integration, transformation, and cleansing functionality including: profiling; parsing; cleansing; matching; and monitoring.

Ultimately, utilizing TDQ-IT proved to be a successful venture for SMARTech.

Read the full case study here: http://www.melissadata.com/whatsnew/dqt-tdqit-smarttech-case-study.pdf

 

Melissa Data Closes 2009 with Record Sales for DQ Product Line

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Multiple sales in December for the Total Data Quality Integration Toolkit (TDQ-IT) helped close out the year, exceeding expected forecast.

Melissa Data has a host of data quality and enrichment solutions to help any size company - from small to mid-size companies to Fortune 500. The product offering includes multiplatform APIs, Web services, and enterprise platforms like TDQ-IT which delivers the full spectrum of data quality processes, as defined by Gartner's Magic Quadrant.

Free trials of Melissa Data's data quality tools and TDQ-IT are available at
www.melissadata.com/dqt or call 1-800-MELISSA to learn more about the company's 4-month guaranteed ROI program.

Read the full press release here:

http://www.melissadata.com/newsreleases/record-sales-data-quality-total-data-quality-integration-toolkit-product-line.htm

 

Moving Mountains of Data Without Sacrificing Performance

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With the increased merging of disparate core business systems in the enterprise--as well as the emergence of additional systems in the form of enterprise resource management, customer relationship management, hierarchical storage strategies, and other business-driven initiatives--many companies today find themselves moving mountains of data on a daily basis.

Business intelligence (BI) initiatives in particular typically rely on data warehousing strategies to provide critical information and reports to management in support of business decisions. Such strategies often require the timely transfer of enormous amounts of data from line-of-business systems.

Too much time taken in data transfer can adversely impact a company's agility and could mean lost windows of business opportunities. It can also encroach on processing resources better devoted to core business applications.

Read the full article at http://www.dbta.com/Articles/Editorial/Trends-and-Applications/Moving-Mountains-of-Data-without-Sacrificing-Performance-60440.aspx

 

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