3. • The Challenge
• Shortcomings of the current Enterprise IT
• Introduction of the "Digital Business Platform"
• Location of the Digital Business Platform in the
Enterprise Architecture
• Digital Business Platform and Orchestration
5. • The Challenge
• Shortcomings of the current Enterprise IT
• Introduction of the "Digital Business Platform"
• Location of the Digital Business Platform in the
Enterprise Architecture
• Digital Business Platform and Orchestration
10. • The Challenge
• Shortcomings of the current Enterprise IT
• Introduction of the "Digital Business Platform"
• Location of the Digital Business Platform in the
Enterprise Architecture
• Digital Business Platform and Orchestration
15. • The Challenge
• Shortcomings of the current Enterprise IT
• Introduction of the "Digital Business Platform"
• Location of the Digital Business Platform in the
Enterprise Architecture
• Digital Business Platform and Orchestration
20. • The Challenge
• Shortcomings of the current Enterprise IT
• Introduction of the "Digital Business Platform"
• Location of the Digital Business Platform in the
Enterprise Architecture
• Digital Business Platform and Orchestration
58. Application
Service Interface Layer
In-Memory
IMDB, CEP,
Cache
IMDB, CEP Distributed Cache In-Memory DB
Streaming CEP
Graph
Stream
1 Pass
Stream
Big Data Core
Query Engine
In-Memory Analytics
InteractiveBulk Transfer
Sources
Streaming
Organisations can choose from different data technology components to build the architecture
needed to support data acceleration. These include big data platforms, complex event processing,
ingestion, in-memory databases, cache clusters, and appliances.
61. Advanced Visualizations
Get deeper insights from analytics by using interactive best practice advanced visualizations that
enable drill-to-detail. For maximum effectiveness use these visualizations in combination with
blended data and advanced analytic models.
Text & Unstructured
Segmentation/Clustering
Predictions & Simulations
Behavior
Networks and Relationships
Geospatial
Treemaps and scatter plots visualize
aggregate and individuals size, proximity
and concentration of clusters
Parallel coordinates visualize patterns in
multivariate discrete and continuous
datasets with parameterized colors
Visualize location and geographic network
data on multi-layered maps give insights
into event concentrations.
Parameterize time-series predictions for
‘What-if’ analysis. Confidence bands
visualize the models strength
Find relationship clusters and patterns with
network diagrams are used chord charts for
hierarchical entity relationships
Text clouds combined with sentiment scores
are powerful for generating insights from
social and unstructured data.
65. Who we are
Helps business quickly achieve digital acceleration, with
scale, insights and agility
More API deployments run on Apigee than any other
platform
Customers include 20% of the Fortune 100, 50% of the top
global brands, 50% of the top retailers
Customer-driven product leadership with APIs and
Big Data
Over 100k developers in the fast growing Apigee developer
community
Products
Services
Knowledge
68. Enterprises need to operate differently to stay relevant
68
Omni-channel AgilityIndividualization
Individualized interactions
Relevant experiences
Accelerate decisions
Proactive response
Adaptive processes
Continual learning
Be where customers are
Holistic view
Contextual journey
Proliferation of Mobile
and Digital
Accelerating Pace of
Change
Evolving Customer
Expectations
69. Predictive Analytics on Big Data key to operating differently
69
DeveloperUser API API Team Backend
Predictive Insight
Hadoop
Data Warehouse
AppAdaptive
App
Data Scientist
Adaptive
API
70. Existing Big Data investments lack key capabilities
70
Data Lake /
Data Warehouse
• Big Data Infrastructure
• Lack specialized data structures to analyze
fine-grain event data
• Lower precision resulting from predictive
analytics on summarized, tabular data
• Difficult to incorporate signals from
unstructured data in analysis
• Difficult to access insights in real-time
• Difficult to integrate predictive insights into
apps and APIs
Business
Intelligence
• BI Visualization &
Reporting
Data Science • Statistical Analysis
• Scientific Visualization
ChallengesCurrent Big Data Investments
71. 71
In addition, many challenges to address
Best Practices
Global Deployment
Unstructured Data
Processors
Real Time Processing
Continual Improvement
APIs & Automation
Sophisticated Machine Learning
Model Building
Monetization
Security
System Management
Big Data Structure
Adaptive
Apps & APIs
73. Business
& Technical
User
Rapidly realize value API Team
Apps
Developer
Develop more precise insights
Discover complex hidden patterns
Data
Scientist
Apigee Insights: Enable adaptive apps and APIs
Hadoop
Data Warehouse
Other
Developer & Business User Tools
Predictive & Descriptive Analytics
Graph & Sequence Processing (GRASP)
74. Business User
Tools
Developer
Tools
RESTful
APIs
Graph Machine
Learning
Modeling
Workbench
Real Time Scoring
Data Loaders
Unstructured Data
Processor
Temporal Graph
Database
Apigee Insights: Enable adaptive apps and APIs
Business
& Technical
User
API Team
Apps
Developer
Data
Scientist
Hadoop
Data Warehouse
Other
Predictive & Descriptive Analytics
Developer & Business User Tools
Graph & Sequence Processing (GRASP)
75. How do you detect hidden patterns and
relationships?
75
Data Data
• How do you discover hidden patterns in
interactions across touch points & channels?
• How do you understand the customer journey
across siloed channels?
• How do you deliver a contextual journey vs.
channel optimized interactions?
76. • Unique big data structure (GRASP) purpose-built to analyze fine-grain time-aware
interaction data – at scale
• Identify complex hidden patterns and relationships in customer behavior
• Dramatically improve ability to predict future actions
Use specialized big data structure to analyze customer
journey
76
PROFILE
ConsumerID: U56
Gender: M
Geo: San Francisco
Interests: Bikes
PROFILE
ConsumerID: U60
Gender: F
Interests: News
Age: 35-40
2 4
1 3 40
0 3
View product
A
on web
Buy product
A on mobile
Call
customer
service
Return
Product A
in store
3
2
4
1
0
Time
GRASP
(Graph and Sequence Processing)
time-sequenced graph database on
Hadoop
Fine-grain event and entity data
77. How do you improve prediction accuracy?
77
• How do you avoid losing precision when working
with sampled or summarized data?
• How do you detect signals hidden in
unstructured data such as text?
• How do you prevent prediction accuracy from
degrading over time?
78. Apply powerful machine learning on fine-grained event
data
• Significantly improve prediction accuracy by applying powerful machine learning
algorithms directly on fine-grained event data in GRASP
• Always deliver fresh, relevant interactions with a continual learning feedback loop
78
Rules
Based
Approach
Traditional
Statistical
Analytics
Apigee
Insights
Precision
Traditional Analytics Apigee Insights
(Machine Learning on Big Data)
Static Model Dynamic Model
Focuses only on top
attributes
Leverages both strong and weak
signals
Batch only Batch and real time
Adapts to changing conditions
Easier to put into action
Significantly better results
79. Discover hidden signals in unstructured data
79
MBRS payment was rcvd the same day as
dunning took effect. Dunning was not posted until after
payment was processed. Final notice sent out
after payment was sent out as well. Payment was sent out
and postmarked before due date. The member was
termed correctly.
88%
45%
With
Unstructured
Data
Without
Unstructured
Data
Prediction Accuracy
(Likelihood for member to complain)
Healthcare Payer Example
2x
• Unstructured data is difficult to analyze but holds valuable signals
• Improve prediction accuracy by extracting signals hidden in text
80. How do you realize value from data?
80
• How do you enable developers to build new
adaptive apps and APIs?
• How do you enable business users and analysts
to interact with predictive insights?
• How do you integrate predictive insights into
existing apps and systems?
Predictive
Insights
Business
Value
81. Access insights in real-time
81
• In-memory real-time processor to enable fast access to insights
• Regularly updated to always provide fresh, relevant scores
Real-time access to insights using NoSQL
Propensity Free Shipping 10% Off Churn
User 1 0.72 0.68 0.33
User 2 0.56 0.23 0.55
User 3 0.32 0.45 0.67
User 4 0.20 0.32 0.18
User 5 0.44 0.69 0.22
82. Easily consume insights via APIs and visualization
82
Predictive insights
Descriptive insights
• Use RESTful APIs to build new adaptive apps and APIs or extend existing ones
• Enable developers and business users to act on predictive insights
Build new adaptive apps and APIs, extend existing
ones, or use or extend visualization tools provided
83. Deliver individualized interactions across multiple channels
83
Direct Mail
Email
Web
Mobile
Outreach
Fine grained
event data
Individualized interactions
across multiple channels
Machine Learning on
GRASP
Predictive Models
Targeting Recommendations
Churn Other Advanced
3
2
4
1
0
Propensity
Free
Shipping
10% Off Churn
User 1 0.72 0.68 0.33
User 2 0.56 0.23 0.55
User 3 0.32 0.45 0.67
User 4 0.20 0.32 0.18
User 5 0.44 0.69 0.22
0 View product
page on web
1 Receive mobile
offer
2 Call customer
service
3 Agent note:
dissatisfied
4 Return product in-
store
5 Purchase
Online
Customer
Profile
85. 85
IBC: Proactive Customer Service with Predictive
Analytics
“Apigee has helped us move from simply answering customers’ calls to proactively reaching
out to our members before they have issues.”
Somesh Nigam, SVP and Chief Informatics Officer, Independence Blue Cross
• $1.2B Medicare Advantage
business
• 50+% Reduction in
complaints to Medicare
• Transformed from reactive
to proactive customer
service
• Identified root causes for
dissatisfaction
86. Typical Use Cases
Proactive Churn Reduction
Reduce churn by predicting customers likely to be at-risk
and proactively responding to them
Individualized Targeting & Recommendations
Improve customer retention, cross-sell, up-sell, loyalty, and
acquisition
20% increase in profit
Individualized daily deals emails
Leading Online Services Company
100% increase in revenue & profit
Individualized website recommendations
Leading Shopping Site
164% lift in conversions
60% more clicks, 55% fewer emails
Recommended products - game launch
Leading Gaming Company
50% fewer complaints
6x more precise predictions
1 month in advance
Leading Healthcare Insurer
87. Apigee Insights
87
Deep Predictive Analytics Expertise
• Team led Big Data @
Yahoo!, IBM, and others
• Thought leaders in machine
learning and big data
• Extensive Vertical expertise
from client engagements
Accelerate
Business Growth
• Increase revenue, profitability
and customer satisfaction
• Individualized targeting and
recommendations
• Proactive Churn Reduction
Develop
Adaptive Apps & APIs
• GRASP on Hadoop
• Machine Learning on GRASP
• Unstructured & API data
• Real Time
• APIs and Automation
• Cloud or on-premise
Apigee
Insights