Recently in Address Quality Category

Melissa Data will demonstrate new "Golden Record" functionality in its MatchUp Component for SQL Server Integration Services (SSIS) at Enterprise Data World (EDW). MatchUp SSIS is a powerful tool for advanced matching and deduplication management. By integrating the Golden Record selection tool, MatchUp SSIS represents an industry breakthrough based on its ability to discern contact data information, and select the surviving record based on the level of quality of the information provided.

Melissa Data will also showcase its popular collection of contact data quality and integration solutions. The event will be held April 28 through May 2 in San Diego, Calif. at the Sheraton Hotel and Marina.

Come stop by Booth #412 and say hi!

CLICK HERE FOR MORE INFO

By Elliot King


And while that definition may be good enough in a practical sense for specific issues, it really isn't good enough to diagnose the sources of data quality problems generally. Constructing a general framework for data quality problems can be a useful guide in better identifying and resolving specific issues.

One of the earliest efforts to better understand the nature of data quality problems calls for classifying problems into three general categories--operational, conceptual and organizational. Operational data quality issues are those that are generated through problems with data capture and transmission. Inaccurate data is collected. Data may be missing. Or data may be corrupted through some process, for example.

Conceptual data quality problems occur when data is not well defined or it is inappropriate for its intended use. One of the most famous examples of a conceptual data quality problem (though it is not often thought of in this way) was brought to light in the movie Moneyball.

The basic thrust of the movie was not that the information old-time baseball scouts used to evaluate players was wrong per se; it was they were collecting the wrong data to identify productive players. Batting average, for example, is less useful in determining a player's value than on-base percentage. A pressing new conceptual data problem is the attempt to use electronic patient records to judge medical treatment outcomes.

When operational and conceptual data problems persist over time despite repeated attempts to fix them, organizational data quality problems are usually the culprit. In these cases, wrong, missing and invalid data is not really the problem, but the symptom. Something has to be fixed in the organizational structure or culture.

The point is this--data can be wrong for many reasons and it can't fundamentally be fixed without a general understanding of the error's cause.

What do you do when you're faced with verifying and correcting international addresses? Melissa Data and Loqate tackle the issues that arise from different language and character sets, the over 130 address formats worldwide, and the many diverse sources of data. Find out how your customer retention, satisfaction and revenue can be impacted with better data quality; how global address verification and geocoding works; and much more! Melissa Data will showcase Contact Zone, its one-click data quality software solution to easily clean your data in leading CRM platforms such as Salesforce, etc. Webinar starts April 16 at 11:00 AM EST.

CLICK HERE TO REGISTER NOW!

By David Loshin


Yet, as more organizations look to merge data sets and feeds from different sources, some challenges remain, particularly with the use of unstructured text (such as that presented via Twitter or Facebook.) People cannot be expected to always conform to your organization's data standards, and often use colloquial terms or their own words to describe ideas that would map to your own dimensional values.

For example, if you wanted to filter out the individuals who prefer to drive "pickup trucks" (one of our standard values), it is not enough to scan for that phrase. Many individuals will refer to their pickup truck using different terms, such as a make and model ("Ford F-150," "Chevy Silverado") or a different name ("light truck") or a nickname ("baby monster"), but these terms have to be linked to the overall classification term.

This is an example of a simple hierarchy, in which one concept ("automobiles") is divided into a collection of smaller classes (the NHTSA classifications). Each of those classes in turn contains other phrases and terms. Within each of those included collections, there may be other inclusive categorization, such as by make and then model.

With a well-defined hierarchy for classification, unstructured text can be scanned for matches with values that live within the hierarchy, and that enables the standardized classification. To round out the example, a Twitter tweet exclaiming the author's love of "driving his Ford F-150" can be scanned, with the model name extracted, located within the make and model hierarchy for pickup trucks, thereby allowing us to register his/her automobile driving preference!

By David Loshin


More concretely, the example we used was mapping individuals to their car purchase preferences, but different applications used different car classifications that did not share the same number of values and the value sets did not directly map in a one-to-one manner. The potential result is confusion in interpreting the results, especially if this classification is just one variable used for creating a customer profile.

One way to address this is to put a standards policy for classification dimensions in effect by selecting a single set of concepts, mapping those conceptual values to a standard single set of values, and then insisting that any application that uses that conceptual domain always use the standard.

This sounds simple, but it actually may entail some effort, since no one person may be aware of all the places that any specific classification domain is used.

This task goes beyond a "data management" activity and essentially becomes a "data governance" one involving a broad solicitation across the community of data consumers to determine the classification dimensions used and the enumerations of values employed within each dimension.

At the same time, the analyst spearheading this effort must have a plan for capturing the classification data, harmonizing values across variant lists, selecting a standard, communicating the standard, and then ensuring that the standard is put into practice.

Establishing good practices and processes for domain harmonization and standardization is an important topic to be considered in upcoming posts, but next time we will look at a growing challenge for classification domains: aligning data from unstructured text with the standard classification dimensions.

By Elliot King


Ironically, the impact of poor data quality on customer satisfaction is often overlooked. According to a survey of members of the Association of Business Process Management Professionals (ABPMP), of the 45 percent who reported that they are working on improving CRM processes, only 38 percent have evaluated the impact that poor-quality data has on the effectiveness of these processes. That statistic is a little frightening.

Poor data quality coupled with the inability to deliver the right data to the customer at the right time damages overall customer satisfaction in a variety of ways. Consider this scenario. Many retailers run multiple promotions at the same time. Each promotion has different rules and restrictions and run for different time periods.

When customers go to pay, if the final price does not reflect the discounts they anticipated, they will not be real happy. The flip side of the coin is true as well. If a sales process does not include a discount where one is due, an opportunity to build goodwill or perhaps close a deal will be lost.

But misunderstandings and the failure to provide the appropriate product are the tip of the iceberg. If point-of-contact personnel do not have confidence in the information they receive, the process for which the data is needed will be slowed and their productivity diminished.

And there is the annoyance factor. Think about your interactions with your telephone or cable provider. You call with a problem. The computer confirms the number you are calling about. If you are lucky and you finally find a human to talk to, how many times do they have to reconfirm all your information--telephone number, address, and so on--especially when you are passed from one person to another? Okay, part of the problem is most likely the lack of overall system integration, but part of the problem is a fear of faulty information.

Poor data quality is not a theoretical issue. It can hurt you in the place that may hurt most--your relationship with your customers.