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Historical SaaS App Data as the Ultimate Training Set for AI/ML

Historical SaaS App Data: The Key to Turning AI and ML from Disappointment into a Success Story”

How can something represent both one of the biggest opportunities and deepest disappointments of our modern era in SaaS App Data industry?

Hello AI and ML.

Artificial intelligence and machine learning have long been touted as both the harbingers of Ray Kurzweil’s singularity (coming to an Orwellian telescreen near you circa 2045) and the key ingredients needed to build a “smarter planet” (thank you, Ogilvy). But the reality is that few impactful, real-world use cases actually exist today. It’s no wonder that an Accenture survey reveals that only 27% of business leaders believe that analytical insights will actually deliver business impact. So, what’s stopping us from realizing the potential of the other 73%?

What’s Missing: Change SaaS App Data Sitting Right Under Our Noses

It turns out that the missing ingredient standing between theory and practice – both in analytics and AI/ML – is something extraordinarily simple: historical data. The only thing that matters, in business and in life, is change over time. It’s a fundamental evolutionary principle that we all understand on a visceral level – it’s how we observe our world, react and learn from our past. It’s how we evolve.

It’s also something that organizations like Amazon, Tesla and Gong understand and use to their advantage.

When you view, engage with or purchase something on Amazon, the machine gets smarter at predicting what you may be interested in next, because it learns from every minute change you input into it. When you drive your Tesla, a neural network learns from changes in video streaming from all cameras over time. This data is used to improve self-driving algorithms. When you deploy Gong, the platform ingests all video and meeting data, maps it across a timeline for each sales opportunity and then makes suggestions about what words and actions lead to positive or negative outcomes over time.

The more historical change data you feed into AI and ML, the better it gets at predicting outcomes…and the closer it gets to delivering on its original promise. But lest you think that you are out-flanked in the AI/ML economy by tech giants (like Google or Facebook) who possess vast troves of change data, you may not be as change data poor as you thought.

The Best Change Dataset: Your SaaS Application Data

According to a 2018 Gartner survey about SaaS migration, 97% of the respondents said their organization had already deployed at least one SaaS application¹. These applications, such as CRM (like Salesforce), ERP, marketing automation, e-commerce and so on, are places where you make critical business decisions every day. Cloud applications represent the frontlines of cause-and-effect patterns of change sweeping across your business. And while SaaS apps are excellent at their purpose, their goals are often “purpose-built,” their view is limited (often to a specific point in time) and their ability to correlate outcomes across systems is limited by both of those things as well as the APIs that interlock them.

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As more and more organizations realize that they are sitting on goldmines of ideal AI/ML training sets that are constantly being overwritten and thrown away inside of their SaaS applications, they will begin to obsessively preserve, ingest and mine the historical changes in their cloud application data for competitive advantage. This can be done with simple SaaS application backup tools on the market today.

Imagine if you could capture every single change across every one of your cloud applications and correlate them all against each other.

Would you notice patterns in precipitating events?

Would you be able to tell the difference between correlation and causality?

Would you be able to improve predictions with simple linear extrapolation?

While an individual human may not, an average ML algorithm likely would.

If you feed in enough change data points from one SaaS application, then your graph becomes a smooth curve that can tell you where the next data point will fall. Feed in historical data from multiple systems, and you can start to make pretty darn good predictions about what’s really driving your business.

If you don’t believe me, simply look at what organizations like Gong, Tesla and Amazon are already doing.

Just don’t wait longer than your competitors; in this game, the only thing working against you is time. The truth is it’s easy to get started  – just hit the “record” button on your SaaS data today.

Read Also: Geospatial Solutions Enabled by Blockchain, Big Data and AI Bring New Application Areas

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