Melissa Data Creates New Plug-ins for Pentaho Marketplace

Good news for companies looking to keep their data lake from turning into a data swamp. Melissa Data has created a set of new data quality plug-ins for Pentaho's big data integration and analytics platform. The plug-ins will help users quickly move beyond tactical data quality efforts to engage in a broader data governance strategy by combining data quality, data…

Continue Reading

Melissa Data and Semarchy Partner to Integrate Data Quality and Master Data Management

Melissa Data Enrichers Enable Clean, Global Contact Data for Semarchy Users; Webinar Demonstrates Best of Breed Strategy for Fast, Optimized MDM Operations Rancho Santa Margarita, CALIF - November 12, 2014 - Melissa Data, a leading provider of contact data quality and data integration solutions, today formally announced its partnership with Semarchy, a developer of innovative Evolutionary Master Data Management (MDM)…

Continue Reading

Data is Born in Business Processes

By Elliot King One of the most critical factors affecting the health of an organization is the relationship between business processes and the IT infrastructure. Data is created through businesses processes and the ways that these operations are designed and implemented have an enormous impact on the quality of the data, from acquisition through application, and retention. In short, corporate…

Continue Reading

Standardizing Classifications

By David Loshin In the most recent post, we posed a straightforward problem: if we have a reporting or analytical objective that depends on using a dimension for classification, what happens when two different value domains are presumed to map to the same conceptual domain? More concretely, the example we used was mapping individuals to their car purchase preferences, but…

Continue Reading

Customer Centricity and Location Characteristics

By David Loshin In my last set of posts I began to examine the integration of the concept of "customer centricity" into business processes, starting with the ability to uniquely differentiate individuals in relation to their "contact-based" identifying characteristics, such as street address and telephone number. Interestingly, from one part of the discussion, we could draw a conclusion that due…

Continue Reading

Just Grow Up

By Elliot King When it comes to data quality, so many companies need a change of attitude--or to put it bluntly, they just need to grow up. Too often, organizations approach data quality reactively, addressing their efforts to fixing what they discover as broken as quickly as possible. A proactive approach is generally more effective. In this perspective, data is…

Continue Reading

The Format of Nothing

By David Loshin The first question I always wonder about missing data is about the format of the missing data, especially in systems that predate the concept of the "system null" value. For example, early systems maintained files storing tables with fixed-width columns. When one of a record's field was missing a value, something had to be fitted into that…

Continue Reading

Structural Differences and Data Matching

By David Loshin Data matching is easy when the values are exact, but there are different types of variation that complicate matters. Let's start at the foundation: structural differences in the ways that two data sets represent the same concepts. For example, early application systems used data files that were relatively "wide," capturing a lot of information in each record,…

Continue Reading

Communication is a Key to Data Quality

By Elliot King Too often, data quality is seen as a strictly technical issue. Data quality problems must be identified, assessed and then rectified, and that process is best managed by experts using the right tools. But communication may be the most important element in a data quality program. A data quality program can only succeed if all the stakeholders…

Continue Reading

Validation, Standardization, and Correction: Tool or Process?

By David Loshin There are all sorts of tools associated with address standardization, cleansing, and validation. As an example, the USPS has a certification program for software vendors, referred to as CASS (Coding Accuracy Support System)™ certification. According to their website, CASS enables the Postal Service™ to evaluate the accuracy of address matching software programs in the following areas: (1)…

Continue Reading