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Test Data Management: Goal Setting & Team Building (Step…

by David Friedland

This article is part of a 4-step series introduced here. Navigation between articles is below.

Step 1: Goal Setting & Team Building

Someone needs test data to do something, like:

stress-testing the functions and performance of applications prototyping database load/query and DW ETL/ELT operations benchmarking prospective new hardware or software outsourcing development or proofs of concept demonstrating systems with real-looking, but not real, sample data

In all these cases, the most realistic data possible is needed, but it should also be safe and de-personalized. Read More

Test Data

Test Data Management: A Primer

by David Friedland

Welcome to IRI’s primer on test data management. This is the opening article, which is followed by a 4-step series.

Introduction

As anyone familiar with the challenges of healthcare.gov Read More

IRI Business

7 Headline Hacks to Grab a Big Audience

by Jason Koivu

Imagine it’s time to write a headline for your blog article. You just spent hours drafting, in your opinion, a masterpiece. You selected a great keyword or two as the basis of your subject and compiled some great content to make that keyword shine. Read More

MDM

On the Importance of Metadata

by David Friedland

Background

Lines of business do not think in terms of metadata per se. Users want to know what information is available, and where it is. They want to know if the data is reliable, protected, who’s using it, where it came from and how it’s been changed. Read More

IRI Business

MDM Thoughts

by Paul Friedland

Master Data Management (MDM) is a discipline designed to make data more dependable, sharable and accessible. Here are some of IRI’s philosophies around MDM:

Most developers believe that data are, or should be, application-independent.

Business Intelligence (BI)

Multiply Aggregation

by Paul Friedland

As CoSort users have known for a long time, the Sort Control Language (SortCL) program supports multi-level, conditional aggregation functions on a static or running basis. Read More

Data Transformation

New Features in CoSort 9.5.3

by David Friedland

Editors note: CoSort Version 10 was released in mid-2018. Please see this article for links to its features and upgrade details.

What’s New in CoSort 9.5.3

Along with the new website, there is a new release of IRI’s flagship CoSort package for data management and data protection. Read More

Data Transformation

ETL vs ELT: We Posit, You Judge

by David Friedland

Full disclosure: As this article is authored by an ETL-centric company with its strong suit in manipulating big data outside of databases, what follows will not seem objective to many. Read More

Data Masking/Protection

How to Tokenize Credit Card Data

by Zachary McHenry

Abstract: This article explains a method of protecting credit card data with tokenization using the IRI FieldShield data masking tool.

Submitting one’s credit card details electronically can be disconcerting (e.g., Read More

Test Data

Generating Test NID Data: Korean Social Security Numbers

by Zachary McHenry

This article discusses the generation of computationally valid social security numbers for the purposes of testing applications specific to Korean business interests. If you are interested in US social security number test data generation, see this article. Read More

Big Data

Data Profiling: Discovering Data Details

by Dale Robson

Data profiling, or data discovery, refers to the process of obtaining information from, and descriptive statistics about, various sources of data. The purpose of data profiling is to get a better understanding of the content of data, as well as its structure, relationships, and current levels of accuracy and integrity. Read More

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