This research from leading IT research firm Enterprise Management Associates (EMA) evaluated how machine learning changed the landscape of observability into a critical foundation of predictive analytics in driving business outcomes. These slides provide insights into this research, including: - Observability in today’s landscape - Implanting an observability solution in a hybrid, multi-cloud environment - Successes and challenges of implementing an observability solution - How observability enhances the data-driven journey - Key components of a successful observability solution

1. Driving Observability Through
Machine Learning and
Predictive Analytics
William Schoeppner
Research Director
wschoeppner@enterprisemanagement.com
Sponsored by . . .

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• Driving Observability Through Machine Learning and Predictive
Analytics On-Demand Webinar:
https://info.enterprisemanagement.com/machine-learning-and-
predictive-analytics-webinar-ws
• Check out upcoming webinars from EMA here:
https://www.enterprisemanagement.com/freeResearch

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Featured Speaker
Will is an accomplished information technology
and business operations leader with more than 25
years of experience developing, managing, and
leading business operations, integrated
technology, and vendor management. Prior to
joining EMA, he led Strategy and Performance for
the Governor’s Office of Information Technology
as the Deputy Chief Strategy Officer.
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Will Schoeppner
Director of Research
Enterprise Management Associates

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Sponsor
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The Maturity of
a Successful, Data-Driven
Organization
1 2 4
Agenda
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3
The Foundation of Data
Management
Business Success
in Adapting ML/AI
to Observability
The Challenges in
a Complex Landscape
Trends in
Predictive Analytics
and Observability
5 6 7
Finding the
Right Observability
Solution
Summary
Observations

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Demographics
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Total Qualified: 155
Location
Total: 1382
Company Size: 1,000+
49% US
17% France
16% Germany
17% UK

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Demographics
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8. The Maturity of a
Successful, Data-
Driven Organization

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13. The Foundation of
Data Management

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17. Business Success in
Adapting ML/AI to
Observability

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21. The Challenges in a
Complex Landscape

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28. Trends in Predictive
Analytics and
Observability

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30. Finding the Right
Observability Solution

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35. Summary
Observations

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Thanks to Research Sponsor
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