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OverviewofAllSight
CustomerIntelligenceManagementSystem
Three Eras of Customer (Citizen) 360
CIF Era
Customer Data
Integration (CDI) / MDM Era
Customer Intelligence
Management (CIM) Era
Requirements:
1. Big & Unstructured Data
2. Power operational needs –
customer care, sales, onboarding
3. Power analytical needs –
marketing targeting, data science,
risk and fraud
Requirements:
1.Normalized relational
customer profile
2.Subset of ‘master data’
3.Real-time Operational use
cases – integrate with
CRM, web self-service
Requirements:
Batch transaction processing
Technology:
Hierarchical flat file
1970 - 2000 2000 - 2015 2016- Present
Depth of
Customer
Understanding
&
Business Value
Technology:
1.Relational database
2.Service oriented architecture
(SOA)
3.Message-oriented
middleware (MOM)
Technology:
1. Apache Hadoop
2. Spark
3. Machine Learning
4. Graph & columnar data stores
5. Natural language processing
6. Schema-less matching
You’ve Tried to Fit Customer Data Into Existing Systems
But You’re Making Compromises and Misrepresenting the Customer
CRM Apps
Don’t manage all data or
make it intelligent
MDM
Structured data only, do
not manage big data or
add intelligence
Hadoop (Data Lake)
Stores all data, but doesn’t
synthesize or add intelligence
– requires lots of other tools &
in-house build
Big Data Visualization
Only a tool to visualize what’s in
a big data repository. Doesn’t
synthesize data or manage it.
Data Warehouse
Only handles structured data,
and most are at-capacity for
current workloads
Analytics & BI
Lack data management &
enrichment (intelligence) –
predictive analytics needs
data from somewhere.
External Data
Not integrated to internal
data and becomes another
data silo.
A Customer Intelligence Management System
Centralizes Customer Data & Insights to Enable Deep Intelligence
Marketing
Applications
Customer
Care Apps
Sales Force
Automation
Customer Self
Service
Prospect
Lists
Social
Data
Transaction
Systems
Data
Warehouses
Data
Marts
Master Data
Management
Hadoop
Data Lake
External Data
Mktg. Data
Mgmt Platform
Customer Intelligence Management System
Analytics
Business
Intelligence
Big Data
Visualization
Data Ingest Manage
Understand -
Natural
Language
Processing
Link & Build
Context –
Synthesis &
Matching
• Data Store (Pre-
built models) for
C360, insight &
graph relationships
• Configurable
Perspectives/Views
• Governance &
quality
Analytic
Inferences &
Insights
Machine Learning Algorithms to continuously
Learn, Adapt, Improve, and Automate
Real-time
Services
CIM Dashboard
& C360 Graph
Batch
Interfaces
Act
A Customer Insight System – Data + Analytics + Action
Synthesize Reason
Evaluate:
Data
Confidence
Scores
Data
Enrichments
Intelligence
Evaluate:
Insight
Confidence
Scores
Big Data
Processing Engines
Users
At Rest
Streaming
Low Latency – From Real-time Streaming to Batch
OverviewofAllSight
CustomerIntelligenceManagementSystem

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AllSight - 6 Slides (Platform Overview)

  • 2. Three Eras of Customer (Citizen) 360 CIF Era Customer Data Integration (CDI) / MDM Era Customer Intelligence Management (CIM) Era Requirements: 1. Big & Unstructured Data 2. Power operational needs – customer care, sales, onboarding 3. Power analytical needs – marketing targeting, data science, risk and fraud Requirements: 1.Normalized relational customer profile 2.Subset of ‘master data’ 3.Real-time Operational use cases – integrate with CRM, web self-service Requirements: Batch transaction processing Technology: Hierarchical flat file 1970 - 2000 2000 - 2015 2016- Present Depth of Customer Understanding & Business Value Technology: 1.Relational database 2.Service oriented architecture (SOA) 3.Message-oriented middleware (MOM) Technology: 1. Apache Hadoop 2. Spark 3. Machine Learning 4. Graph & columnar data stores 5. Natural language processing 6. Schema-less matching
  • 3. You’ve Tried to Fit Customer Data Into Existing Systems But You’re Making Compromises and Misrepresenting the Customer CRM Apps Don’t manage all data or make it intelligent MDM Structured data only, do not manage big data or add intelligence Hadoop (Data Lake) Stores all data, but doesn’t synthesize or add intelligence – requires lots of other tools & in-house build Big Data Visualization Only a tool to visualize what’s in a big data repository. Doesn’t synthesize data or manage it. Data Warehouse Only handles structured data, and most are at-capacity for current workloads Analytics & BI Lack data management & enrichment (intelligence) – predictive analytics needs data from somewhere. External Data Not integrated to internal data and becomes another data silo.
  • 4. A Customer Intelligence Management System Centralizes Customer Data & Insights to Enable Deep Intelligence Marketing Applications Customer Care Apps Sales Force Automation Customer Self Service Prospect Lists Social Data Transaction Systems Data Warehouses Data Marts Master Data Management Hadoop Data Lake External Data Mktg. Data Mgmt Platform Customer Intelligence Management System Analytics Business Intelligence Big Data Visualization
  • 5. Data Ingest Manage Understand - Natural Language Processing Link & Build Context – Synthesis & Matching • Data Store (Pre- built models) for C360, insight & graph relationships • Configurable Perspectives/Views • Governance & quality Analytic Inferences & Insights Machine Learning Algorithms to continuously Learn, Adapt, Improve, and Automate Real-time Services CIM Dashboard & C360 Graph Batch Interfaces Act A Customer Insight System – Data + Analytics + Action Synthesize Reason Evaluate: Data Confidence Scores Data Enrichments Intelligence Evaluate: Insight Confidence Scores Big Data Processing Engines Users At Rest Streaming Low Latency – From Real-time Streaming to Batch