How to Derive More Business Value from Cloud Data?

Melissa AU Team | Australia, Data Quality | , ,

Today organizations source their data from websites, social media, IoT devices, clickstream analysis, custom applications and more. Each source provides different types of data in independent formats.

For example, a retailer may collect a customer’s name and email address from a web form, their date of birth, gender and demographic profile from social media platforms and budget preferences from responses to digital marketing initiatives.… Read More

Better Marketing Starts with Better Data

Blog Administrator | Address Verification, Analyzing Data, Analyzing Data Quality, Big Data, Big Data Blend, Data Cleansing, Data Governance, Data Integration, Data Management, Data Matching, Data Profiling, Data Quality, Data Quality Assessment, Global Address Verification, MDM | , , , , , ,

Improve Data Quality for More Accurate
Analysis with Alteryx and Melissa


are under more pressure than ever to gain accurate contact data for their
customers. When your consumer base ranges from Los Angeles to Tokyo, it can be
challenging. Poor data quality has a critical impact on both the financial
stability as well as the operations of a business. Verifying and maintaining
vast quantities of accurate contact data is often inefficient and falls short
of the mark.
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Big Data May Turn Data Quality on Its Head

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Big Data, Data Quality | , , ,

By Elliot King

Big data has emerged as a big issue for organizations both large and small. The idea of being able to capture and assess huge amounts of data, as well as use a vast new array of data types including unstructured and semi-structured data, has led to a kind of religious fervor. Analyzing big data will undoubtedly lead to better outcomes–or so the thinking goes.
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Mistakes Are All Around Us

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Quality | , , , , ,

By Elliot King

Mistakes happen. No matter how effective your data quality program is; no matter how well trained your personnel are; no matter how aware you are of the high cost of low data quality, data errors will creep into your databases. The reason is simple. Before information winds up in a database, it passes through a series of steps involving both human interaction and computation from data acquisition to archival storage.
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Standardizing Your Approach to Monitoring the Quality of Data

Blog Administrator | Address Standardization, Analyzing Data, Data Cleansing, Data Integration, Data Management, Data Profiling, Data Quality | , , , , , , ,

By David Loshin

In my last post, I suggested three techniques for maturing your organizational approach to data quality management. The first recommendation was defining processes for evaluating errors when they are identified. These types of processes actually involve a few key techniques:

1) An approach to specifying data validity rules that can be
used to determine whether a data instance or record has an error.

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