What is Salesforce Einstein Discovery?

Have been looking at some of Salesforce offerings.

Video of of tech of basics of Salesforce Einstein:

By Mark Tossell:

Einstein Discovery is a curious enigma in the Salesforce ecosystem. Most people in the Salesforce world have heard of Einstein, as it has been widely publicised, but the majority have little understanding of what it does and how they can use it. In fact, even a good number of thought leaders and consulting partners are in the dark around Einstein.

I think that three factors have contributed to this scenario:

Lack of understanding around the general subject of artificial intelligence and machine learning, especially in the context of business deliverables.

Confusion about the rather complex family of Salesforce Einstein products – including Einstein Analytics, Sales Cloud Einstein, Einstein insights, Einstein Prediction Builder, and Einstein Discovery. Phew!

The challenge that Salesforce Account Executives have to effectively communicate a  product family that is becoming increasingly diverse and complex.

This lack of understanding is unfortunate, because Einstein Discovery (ED) is an extremely powerful tool that can offer tremendous business value if correctly implemented and effectively employed.

The purpose of this post is not to publish a detailed technical treatise about the ED platform; I’ll leave that to others. Rather, my goal is to distill the jargon, bypass the hype, and introduce a very capable piece of kit to those who might benefit from it.

1. What is Einstein Discovery?
Supervised machine learning. Those thee words succinctly define the ED platform. However, what does this mean? How does a machine learn? And why does it need supervision, as if it were some delinquent child? Let me explain.

Machine learning (ML) is analytical model building that can be automated, where the systems “learn” from data and identify patterns, resulting in meaningful conclusions and predictions.

I like this definition of ML from the SAS institute web site – “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

There are two types of machine learning: supervised, and unsupervised. They are fundamentally different, so let’s briefly set them apart.

In supervised machine learning you have a set of features, or variables, similar to the column names in a spreadsheet. You also have a response variable that you use the features to make predictions for. A simple example would be looking at the brand, age, size and condition of motor vehicles in order to make a prediction about their value. Supervised learning is what Einstein Discovery does, and it is very good at it.  .... "