How to Drain a Murky, Big Data Swamp

Gathering data without proper governance can turn data lakes into data swamps – devoid of metadata management, rife with duplication, plagued with errors, and presenting an opaque view of your operations. But Big Data verification and governance solutions can transform expanding data volumes into trusted, actionable information across the enterprise. Melissa’s V.P. of Enterprise Sales and Strategy, Bud Walker, shares…

Continue Reading

The Relationship between Data Governance and GDPR

According to the GDPR, personal data refers to any information related to a person including name, photo, email, address, bank details, social media updates, medical information and even IP addresses. It can come in various forms and contains several data elements. Data governance and GDPR are complementary as a strong data governance program is vital for GDPR compliance and with…

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

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

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

Maximize Value and Mitigate Risk

By Elliot King It seems like everybody knows, or should know, that data is a company's most important asset. Data is the primary commodity employees, customers and suppliers generate and consume everyday--it is the corporate lifeblood. Data is the raw material that let's organizations know if they are doing well or falling short. Data forms the basis of corporate decision-making.…

Continue Reading