Data Masking/Protection
IRI has discussed startpoint security in further detail with the Outlook Series in a segment about data masking.
This article defines what we’d like to call “startpoint security” mostly by virtue of a comparison to endpoint security.
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Big Data
According to Simson L. Garfinkel at the NIST Information Access Division’s Information Technology Laboratory,
De-identification is not a single technique, but a collection of approaches, algorithms, and tools that can be applied to different kinds of data with differing levels of effectiveness.
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IRI Workbench
As we transition IRI Workbench to the Neon version of Eclipse™ in 2017, the Easy Shell feature that facilitated command-line executions of job and batch scripts will no longer be available.
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Big Data
To analyze data successfully, it must first be prepared successfully. Poor quality data creates poor results. Worse yet is data that takes too long to collect and clean because it is too big or too foreign.
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Data Transformation
For the last 30 or so years, the precursor to most large scale business intelligence (BI) environments has been the Enterprise Data Warehouse (EDW). A data warehouse (DW) is usually a central database (DB) for reporting, planning, and analyzing summarized, subject-matter data integrated from disparate, historical transaction sources.
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IRI Business
The other day, two different companies selling me services asked if their offering were in my wheelhouse. Since I didn’t know I had a wheelhouse, I couldn’t answer them.I had to look it up because in my 25 years at IRI, wheelhouse was a still new expression for me.
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Big Data
Before Big Data became a buzz word and Gartner hype cycle fodder (never mind falling into the ‘Trough of Disillusionment‘), companies like IRI were handling it.
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Test Data
Test Data
Test Data
This article is part of a 4-step series introduced here. Navigation between articles is below.
Step 2: Test Data Needs Assessment
Once the questions of who needs test data for what — and who will be dealing with it along its lifecycle are answered (see Step 1) — a deeper dive is needed into the specific technical aspects of the data itself.
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