Blog
Insights on data engineering, analytics and sovereign AI
Explore technical articles, practical workflows and expert perspectives on data processing, predictive modeling and large-scale analytics.
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ETL Guide 2022 from “Into The Minds”: Anatella dominates the market
Whatever the criteria used to qualify an ETL (scope of functionality, speed of execution, quality of customer support, etc.), the dominant ETL…
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The 11 challenges of data preparation and data wrangling
The events of 2020 have accelerated the shift to teleworking and digital relationships. With digitalisation, another transformation is also underway: the analytical…
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What’s the best machine for data science?
What’s the best machine for data science? A recurrent question in many data science blogs is “hey guys, what machine would your…
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The place of data wrangling in the data value cycle
Learn more about data wrangling and how we position it within the data value cycle.
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Why we fail with machine learning, and how to avoid it.
With 53-90 of project failure, chances are each of us will be confronted to it. If making mistakes is part of the…
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Stop obsessing about a perfect model
A perfect model is often an illusion because of the ratio effort / impact, Model Quality Decay and Prediction Window. To be…
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Let’s stop misleading our users with meaningless classification rates!
So, you think AI classifies well? How we end up misleading AI users about the efficiency of classification models
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TIMi is committed to more responsible digital technologies
On Earth Day, this April 22, 2021, TIMi has signed the Responsible Digital Charter for a more environmentally friendly, ethical and inclusive…
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Data wrangling: definition, steps, tools
Data scientists spend a large portion of their time on data preparation activities. Much of this data preparation activity involves data wrangling.
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