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How Salesforce brought artificial intelligence and machine learning into its products with Einstein

By Scott Carey | Oct. 17, 2016
Salesforce spent all week during its Dreamforce conference talking about AI and machine learning and how it was making its range of SaaS CRM cloud products smarter for customers, we asked them how they did it.


Intelligence was the talk of Dreamforce - Salesforce's annual tech conference in San Francisco this month - with the SaaS giant's latest announcement 'Einstein' promising to bring complex data science techniques and predictive algorithms seamlessly into all of their cloud-based CRM products.

Here's how Salesforce used a spending spree on artificial intelligence (AI) startups and talent to bring these smart features to customers, all without opening up their precious data.

Project Einstein

Before he went on an AI acquisition binge, Salesforce CEO Marc Benioff said there was anxiety within the organisation around applying predictive algorithms to customer data they can't see, because customers want to keep their data private and secure.

The lightbulb moment for Benioff came "when we made acquisitions and they said we can provide intelligence on customer data without seeing it.

"Up to that point, if you couldn't see or normalise the data, you can't apply the intelligence. We have massive amounts of data, petabytes and petabytes, so we have the data that we need and the answer is that we can now operate on that data without interfering with the trust relationship with our customers."

General capabilities

Now that it could apply various machine learning techniques to this huge pool of customer data, general manager of the Einstein group John Ball and his team had to work out how to not only make the data models generally available while maintaining trust, but also make all the insights unique to their customer's domains.

Ball summed the problem up: "Given we have hundreds of thousands of customers across eight clouds we have millions of predictive models to build, and there aren't enough data scientists in the world to do that."

Ball had to ask himself: "How can we simplify this?" The solution was to automate the data wrangling process - a task which by most measures takes up 80 percent of data scientist's day-to-day job - by using the metadata.

He explained: "We quickly realised that we have metadata so we know if a field is an email or an opportunity, or if a lead object is linked to another. So we can do a bunch of automatic data preparation to feed into the predictive model."

Data Concerns

Since then Ball and the Salesforce executive team have been busy ensuring customers that their data will be secure within Einstein's underlying general models. Naturally competitors don't want their data to be used to power a predictive model which may benefit their rivals in any way, especially when the big Salesforce pitch to move to the cloud revolved around proprietary data ownership and security.


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