“Have you stopped speeding?” You could probably object to a leading question like this in court, but what happens when an important question with only a yes or no answer is solicited on a mandatory form, and the response becomes part of an actionable database record?
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Quasi-identifiers, or indirect identifiers, are personal attributes that are true about, but not necessarily unique, to an individual. Examples are one’s age or date of birth, race, salary, educational attainment, occupation, marital status and zip code.
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This is part 4 of a 4-part series on Production Analytics. Processing on Par with Information [Part 1] Data Processing Drives Efficiency [Part 2] Processing Real World Data [Part 3]
In this final article of the series covering the Production Analytic Platform paradigm, we look at data virtualization—a key requirement in today’s multi-source, data-overloaded world.
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This is part 3 of a 4-part series on Production Analytics. Processing on Par with Information [Part 1] Data Processing Drives Efficiency [Part 2] Unifying the Worlds of Information and Processing [Part 4]
The inclusion of full function data processing in the Production Analytic Platform simplifies the task of gathering data from external sources such as the Internet of Things and clickstream data that requires both intensive exploratory modeling as well as high-speed application and maintenance of those models on real-time and streaming data.
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This is the first of a four-part series of blog articles examining the inherent tradeoffs between data processing and information storage and presentation within traditional ETL paradigms — from the ODS to the data lake.
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This is part 2 of a 4-part series on Production Analytics. Processing on Par with Information [Part 1] Processing Real World Data [Part 3] Unifying the Worlds of Information and Processing [Part 4]
Considering data processing as a central component of data management and on a par with databases offers new insights on how to improve overall efficiency and return on investment in traditional data warehouses.
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One of the biggest concerns with releasing a dataset is the risk that a potential attacker can identify the owners of particular records. Even though masking or removing unique identifiers, like names and Social Security Numbers, can reduce that risk substantially, it may still not be enough.
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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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The Internet of Things (IoT) continues to grow at an unbelievable rate, and creates tremendous benefits and opportunities for society. With it grows the demand for products and services that control, manage, and protect the massive amounts of data streaming from all those network-connected devices.
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It’s the end of 2016, and enterprise data continues to change rapidly in form, size, use, and residence. Rarely does it remain in siloed constructs anymore, limited to certain business units or untouched by the outside world.
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As a follow-on to my introduction to IoT and MQTT, this article describes how device data collected from standard climate sensors and sent through an MQTT (Message Queuing Telemetry Transport) server can be rapidly and inexpensively pre-aggregated by a single SortCL program (in IRI CoSort) on a tiny edge device or gateway.
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