Occam's Razor
APRIL 21, 2014
The mistake we make is that we obsess about every big, small and insignificant analytics implementation challenge and try to fix it because we want 99.95% comfort with data quality. We wonder why data people are not loved. :). Tactical advice: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models.
datapine
AUGUST 14, 2019
Therefore, there are numerous data science tools and techniques that provide scientists with an easier, more digestible workflow and powerful results. Our Top Data Science Tools. The tools for data science benefit both scientists and analysts in their data quality management and control processes.
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Ontotext
JANUARY 6, 2022
Spotting Data Consistency Issues. We integrated only a bit of Transparency data, but have already found various data quality issues. This is possible because by semantically interlinking different types of data coming from different sources, we can look at the bigger picture and can easily see problems in the data.
datapine
DECEMBER 4, 2019
The commercial use of predictive analytics is a relatively new thing. The accuracy of the predictions depends on the data used to create the model. For instance, if a model is created based on the factors inherent at one company, it doesn’t necessarily apply at a second company. Graph Analytics.
Andrew White
JANUARY 11, 2021
Where does the Data Architect role fits in the Operational Model ? Assuming a data architect helps model and guide and assist D&A then they play a key role. This would be part of a Data Literacy program. Decision modeling (one of my favorites). And not just for synthetic data techniques.
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