Metrics are fundamental to succeeding with SaaS. As you pour tenants into a shared infrastructure environment, you need to rely on a rich collection of data that can drive insights into the architectural, operational, and business dimensions of your SaaS solution. In this session, learn to identify the different types of metrics commonly collected by SaaS providers and connect these with the design and architecture strategies that are employed to surface and analyze this data on AWS. We look at how SaaS organizations instrument, aggregate, publish and build actionable views of this data with specific emphasis on how a robust metrics architecture can fundamentally impact the operational, architectural, and business decision-making process for SaaS organizations.

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SaaS Analytics and Metrics:
Capturing and Surfacing the Data That's
Fundamental to Your Success
Andy Powell
andpow@amazon.de
Partner Solutions Architect, ISV - EMEA
A R C 3 2 5

3. Analytics and metrics are the life blood
of SaaS applications

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What are metrics?
A metric represents a time-ordered
set of data points
Think of a metric as a variable to
monitor, and the data points
represent the values of that
variable over time

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Metrics feed analytics

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Analytics – Making sense of metrics
Insights
Analytics
Metrics
The art and science of
extracting insights and
actionable information
from data

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Sourcing of metrics
Application activity
• Background processes
• Data imports
User activity
• Requests
• Data transfer
• File uploads
Resource usage
• Network
• CPU
• Memory
• Storage

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SaaS metrics collection granularity
Fine
grained
• More detailed
• True vision of
resource usage
Coarse
grained
• Rough
numbers
• Abstracts
resource usage
Metrics

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Giving metrics context
X-TENANT-ID TENANT_ID
Tenant ID flows through the application
giving context to the request and
processing actions
Amazon
CloudWatch
AWS X-Ray
HTTP header Context Var

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Java metrics collection with Spring & X-Ray
• Leverage AspectJ and Spring Framework
• Non-intrusive metric collection
• https://github.com/aws/aws-xray-sdk-java
+ =

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Java metrics collection with aspects
AWS X-Ray
Method
execution
Join points
Aspect
Advice
Pointcut

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JavaScript metrics collection with Express middleware
Matchedroute
CORS
middleware
Authentication
middleware
Metrics
middleware
Primary
functionality
HTTP request
Express middleware
HTTP response
AWS X-Ray

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SaaS metrics storage on Amazon Web Services (AWS)
Amazon
CloudWatch
Amazon
DynamoDB
Amazon
Athena
Amazon
RDS
Amazon
S3
Amazon
Elasticsearch
Service
AWS X-Ray
Amazon
Redshift
Amazon
Machine Learning

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Aggregating metrics in a data lake
A data lake is a centralized
repository that allows you to store
all your structured and unstructured
data at any scale
You can store your data as-is,
without having to first structure the
data, and run different types of
analytics—from dashboards and
visualizations to big data processing,
real-time analytics, and machine
learning to guide better decisions

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Analytics – Applied metrics
Collect metrics
Determine cost
per customer
Price SaaS
offering
Metrics and analytics fuel
business decisions
• Operations
• Pricing
• Profitability
• Efficiency
• Roadmap

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Monetizing SaaS analytics – Benchmarks
Tenant A
Tenant B
Third-party data
(Demographics, economics, geographic)
Aggregate analysis Benchmarks
Data anonymization

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Next evolution of analytics
Reactive vs. predictive
Leverage machine learning
Predict outcomes from
historical & population
data

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Key takeaways
• Metrics are the lifeblood of SaaS
• Metrics drive analytics
• Leverage AWS tools to collect app / infrastructure metrics
• Storing metric data is big data
• Analytics drive the business
• Analytics open new revenue avenues

19. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Andy Powell
Partner Solutions Architect, ISV – EMEA
andpow@amazon.de

20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.