Archive for the ‘MDM for customer data’ Category

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Go green with CDI

Published by Admin on 28-10-2008 in Data Quality and MDM for customer data. 0 Comments Tags: .
Green Globe

How much paper was used to deliver three copies of your glossy brochure in the same household? Every tried to estimate the impact on our environment of all the undeliverable direct mailers? How much fuel consumption can be reduced when orders delivery can effectely be grouped together by geographic location?

Being more environmentally friendly is going to be a huge topic in the next few years. Effective data quality and customer data integration can go a long way into making a difference. A totally business to how we look at investing in these areas at this moment.

Citizen Data Integration failed…

Published by Ramon de Noronha on 23-10-2008 in MDM for customer data. 0 Comments Tags: , , .

Just before the upcoming elections in the United States on November 4th many voters have fallen victim to incorrect voters registration. It might well involve over hundred thousands of voters.

The Help America Vote Act was passed in 2002 in order to prevent voter registration and voting counting problems which happened with the presidential elections in 2000. One of the measures was to create centralized databases, which allow voters to check their registrations. In rolling up and centralizing local databases to state databases many data quality mishaps have occured. Resulting in thousands of voters who are now not eligible to vote. These people have to reestablish their eligibility to vote in the coming weeks.

Click here for the full article in the Washington Post.

 

Seven tips for success in implementing CDI

Published by Ramon de Noronha on 22-10-2008 in MDM for customer data. 0 Comments

Jill Dyché, an expert on Customer Data Integration, was asked about the most common mistakes and obstacles when organizations want to implement CDI. For the complete article please follow this link. She came up with 7 very usefull tips:

1) Anticipate the saboteurs. With CDI there will be people in your organization claiming that “we already do that with our data warehouse/ODS/CRM system/toothpicks-and-glue.” Anticipate their arguments and educate them, offering deliberate examples of why they’re full of it. Nicely, of course.

2) Articulate more than one business problem that CDI can solve. You want to position your CDI effort as an ongoing program that can enable different business needs. If you position CDI as a one-trick pony (for instance, it can handle identity resolution), you’ll only be able to ride that horse for so long…

3) Know your functional requirements. It never ceases to amaze me how quickly companies want to enlist vendors. “We’ve gotten everyone in a room and we know what we need,” they explain as they cherry-pick brochures off of vendor websites and schedule proofs of concept (POC). Not so fast! At best, this is a well-intentioned attempt to fill in the blanks by talking to smart vendors. At worst, it’s tire-kicking in a vacuum. Unlike with business intelligence (BI), master data management (MDM) requires a huge and measured focus on functional requirements. Until you have your list in hand, you won’t be able to give the vendors what they need to deliver the goods.

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What’s the value of unique identifiers

Published by Ron Mulderij on 16-10-2008 in Data Quality and MDM for customer data. 0 Comments Tags: , , .

In a survey by Human Inference processes were discovered to be the most challenging data quality aspect.

HI Survey Results
HI Survey Results

Since in processes often unique identifiers are used to identify customers, accounts or data in general the reliability of these identifiers influence the quality of your process. To avoid incorrect results and decisions a check of the data related to the identifiers is required. The illegal use of such an identifier leads to polluted data. A name-number-check should be implemented for e.g.:

  • bank account numbers
  • social security numbers
  • chamber of commerce numbers

The importance of persistent identification

Published by Ramon de Noronha on 16-10-2008 in Data Services and MDM for customer data. 0 Comments Tags: , , .

A much overlooked issue in Customer Data Integration projects is “Persistent Identification”.

Persons and companies are very often identified using their address data. But, what do you do if a person has moved from address A to address B. One, thing you really don’t want is that the person is added to database as a new person (INSERT). From that moment a duplicate person or company resides in your system. This should be prevented, by creating searching indexes which include the current and the previous address of the persons and companies in your database.

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Icesave? We need vdi!

Published by Admin on 10-10-2008 in MDM for customer data. 0 Comments Tags: , , .

The world is changing rapidly. We are becoming less loyal to our suppliers. Switching is easier than ever before. But the landscape of vendors is huge. We can not only do business with banks originated in the country you live in, but international banks are offering their services too. At sometimes interesting interest rates… But what do we know about these vendors? Do we keep track of them? Do we know that Icesave is a daughter company of Landsbanki? And if we do business with both, do we know our total risk when they collapse, as happened previous week? It is time for consumers to stand up, time to take control. We need the Single Vendor View! Let’s think about VDI: Vendor Data Integration!