Tag Archives: technology

Choice Modelling

16 Thu Jan 2025

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Martyn Richard Jones – Madrid 16th January 2025

Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or scenario. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically “utility” in economics and various related fields). 

http://en.wikipedia.org/wiki/Choice_modelling

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Automatic identification and data capture (AIDC)

11 Sat Jan 2025

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Automatic identification and data capture (AIDC) refers to the methods of automatically identifying objects, collecting data about them, and entering that data directly into computer systems (i.e. without human involvement). Technologies typically considered as part of AIDC include bar codes, Radio Frequency Identification (RFID), biometrics, magnetic stripes, Optical Character Recognition (OCR), smart cards, and voice recognition. AIDC is also commonly referred to as “Automatic Identification,” “Auto-ID,” and “Automatic Data Capture.”
http://en.wikipedia.org/wiki/Automatic_identification_and_data_capture

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Adaptive Control

06 Mon Jan 2025

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Adaptive Control

Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to such changing conditions. 

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A/B testing

06 Mon Jan 2025

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FROM MY BEST SELLER: Make Analytics Great Again


A/B testing is a way of comparing two versions of the same variable, usually by testing a subject’s response to variable A against variable B and determining which of the two variables is more effective.
http://en.wikipedia.org/wiki/A/B_testing


Consider this: A company wants to increase the number of users who subscribe to their homepage newsletter. It’s a simple exercise in hypothesis (in this case, speculating) and testing. In this case, the company offers 50% of its users the old subscription page, and they provide the other 50% what they think will attract more subscriptions. They run the test, compare the statistics for the old page to the new page, and make their decisions based on that. A is the old page, and B is the proposed page. It’s that simple.

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Navigating Information Challenges: A Modern Guide

05 Tue Nov 2024

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The biggest challenges facing information

The challenges facing information today are closely related to the complexity of data management, technology and social factors. Here are some of the biggest challenges:

1. Data Overload:

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How to Spot Bullshit: A Guide to Evaluating Credibility

14 Mon Oct 2024

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Spotting bullshit

Martyn Jones, The Data Contrarian, Cariño, Monday 14th October 2024.

Narrator:  Ladies and gentlemen, I give you the fabulous Mel Brooks.

Dole Officer: “Occupation?”

Citizen: “Standup-Philosopher!”

Dole Officer: “What?”

Citizen: “Standup Philosopher. I coalesce the vapours of human experience into a viable and logical comprehension.”

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Is Data Warehousing Truly Iterative and Agile?

10 Thu Oct 2024

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Data warehousing eschews iterative development?

Martyn Jones, Santander, 10th October 2024

Narrator: The development method for data warehousing, whether following Inmon or Kimball, has always been iterative with apparent aspects of self-service, agility, rapid development and end-user development.  

Dud: We always built data warehouses iteratively, didn’t we, Pete?

Pete: Yes, that’s right, Dud. Iterations are delivered in increments. Small enough to be quickly deliverable. Large enough to be significant to the business.

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Rethinking Data Warehousing and Team Collaboration

04 Fri Oct 2024

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Data Warehousing means monolithic and siloed teams?

“Great things in business are never done by one person;

they’re done by a team of people.”

Steve Jobs

Martyn Richard Jones, Tours, 4th October 2024

Narrator: There is a widespread belief amongst the know-it-all crowd that data warehousing and business intelligence necessarily mean monolithic and siloed teams. And that the only way of moving away from such team organisations is to kill off data warehousing. But is this really a rational, coherent, and cohesive approach, as some people say it is? Or is it destructive stupidity born out of conceit, ignorance, and arrogance?

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