It is unreasonable to expect that an in-demand resource like a data scientist will always be there to help when you need them. But the increasing simplicity and power of automated intelligence tools — like Einstein Discovery — allow you to tap into these powers at any time.
Einstein Discovery can find connections in your data in minutes, show and explain the factors that affect the outcome you’re looking for, and automatically build a model for your team to use instantly. It also explains the model in plain English, making it easy for others in your organization to challenge assumptions, update the model as conditions change, or even use it as the starting point for a different problem.
Let’s revisit our earlier scenario and look at the contrast in the experience when automated intelligence tools become the new Batman:
● Your call is answered immediately — there are many more analysts in your company than Data Scientists
● Models are created quickly — connections are found in your data that even the most experienced data scientist would likely miss, not for lack of skill, but for lack of bandwidth
● Automated intelligence tools show and explain the factors that affect the outcome you’ve been given, and the solution is automatically documented in plain English
● If things change, iterations can happen quickly. Updates are easy and effortless.
● Anyone in your organization can challenge assumptions, update the model as conditions change, or use it as the starting point for a different problem
As we saw, the experience in the moment was very different, but there will also be longer-lasting impact:
Collaboration and trust have increased.
Everyone involved can more easily communicate what the model predicts and how it will ultimately affect business operations. Since predictions impact the process and strategy that your colleagues follow, this kind of transparency makes it much easier to have follow-up conversations on change-management efforts. It also helps others identify new areas for innovation. For example, businesses can apply the changes prompted by the analysis to other areas of operations, even outside their area of specialization.
The “skill hurdle” is greatly diminished.
The benefits of data science are no longer restricted to just a few people with specialized skills. You and your team can now investigate more opportunities without waiting in line. Business people with intimate knowledge of the nuances of the business can direct the analysis without having to fight for scarce data-science resources. Suddenly hundreds — or even thousands — of business users can run their own analyses, unlocking their own opportunities for improvement.
This is what democratized AI means to us at Salesforce: Now Batman can be everywhere at once.
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