Jesper Frederikson - Key learnings from scaling saas successes and failures

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"As you set out to scale your business, the leadership team has address a range of critical topics such as: - develop and formulate your culture – and maintain it as you grow create and implement a clear planning & management framework that ensures all functions are aligned hire, enable, develop and maintain a world-class team build a predictable and effective revenue generating “machine” make your customers successful and turn them into vocal fans Learn from successes – and failures – accumulated at Postini/Google, Symantec (integrating MessageLabs), DocuSign, Okta, Siteimprove and Keepit and leave armed with the key things to be aware of as you begin to innovate & execute on multiple dimensions in order to scale quickly and profitably.

Technology

Key learnings from scaling SaaS
successes and failures
Jesper Frederiksen
June 11, 2020

© Okta and/or its affiliates. All rights reserved. Okta Confidential

© Okta and/or its affiliates. All rights reserved. Okta Confidential 3
SMALL TEAMS CAN
MOVE MOUNTAINS
PEOPLE SCALING ALL
THE WAY
PROCESS PROTECTS YOU
THERE ARE NO SECRETS
STRETCH YOUR STARS

VALUES MATTER!
ENFORCE COMMUNICATEREWARD

© Okta and/or its affiliates. All rights reserved. Okta Confidential 5
DEFINE YOUR
FRAMEWORK
TRANSPARENCY
FACE REALTY
THINK AHEAD
CADENCE

© Okta and/or its affiliates. All rights reserved. Okta Confidential 6
FOCUS
CADENCE
THE ANSWER IS A
NUMBER
TEAMSPORT
PROCESS ADHERANCE
PREDICTS PERFORMANCE

© Okta and/or its affiliates. All rights reserved. Okta Confidential 7
STARTS WITH SALES
MAKE THEM PAY
EXEC SPONSORSHIP
DURATION POLICIES
SERVICES ARE OKAY

© Okta and/or its affiliates. All rights reserved. Okta Confidential 8

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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other? Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.

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