Tableau-CRM-and-Einstein-Discovery-Consultant Reviews Questions

This dataset is too big and cannot be used in Einstein Discovery. Request a new dataset with fewer predictors.

Build the story with all the predictors and indicate that Einstein Discovery should show the top predictors.

Go back to the data preparation and reduce the number of fields to less than 30 in order to produce a story.

Build a story with a first set of predictors and assess which predictors are important to the story. Then drop the less important ones and add the predictors that were omitted in the first run and assess their impact.

The model is very strong. A GINI coefficient of .535 shows that this model is very effective.

The model is pretty good; the accuracy rating of .7597 means we can predict both wins and losses at a fairly high rate.

The strength of the model cannot be determined with the metrics shown.

The model is very weak and doesn't provide useful predictions due to the low threshold.

Only provide the user with the level of detail they need to see, with the option to drill down deeper into more details.

Utilize the latest templates for the most modern look and feel.

Intentionally omit specific details so that users can do ad-hoc exploration if needed for root-cause analysis.

Implement strict security predicates to minimize the amount of information displayed to users.

Low-fuel notifications

Sharing of apps with Communities

Access to the API

A and B

B and C