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C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
PROČ UŽ SE NEOBEJDETE BEZ ANALÝZY DAT
JAKUB CHOVANEC - IDG KONFERENCE 3.6.2015
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
KDO JSME
CO DĚLÁME
ZKUŠENOSTI
NAŠI ZÁKAZNÍCI
#1 v poskytování datové analytiky a služeb v oblasti
Business Analytics a Business Intelligence
39 let na trhu
16 let v České republice
Integrovaný soubor softwarových produktů a služeb
Informační management
Analytika
Business Intelligence
70,000 instalací ve 139 zemích po celém světě
23% z výnosů investovaných do vývoje
$3,09 mld. ve výnosech v roce 2014
91 firem umístěných v top 100 v žebříčku Fortune Global 500
Klienti ze soukromého i veřejného sektoru po celém světě
V ČR: Česká spořitelna, Česká pojišťovna, Kooperativa,
Raiffeisenbank, Allianz, GE Money, ČPP, T-Mobile, DIRECT
Pojišťovna, ČSÚ, Komerční banka, VIG, Kooperativa, PČS,
Česká pošta, Ministerstvo zemědělství
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
DATA GENERATED AROUND US
Risk UtilitiesCommerce
E-commerce
CRM
Telco
Fraud Manufacturing
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
RISK WILL THEY PAY THE DEBT?
• Credit risk scoring
• Internal & external data – social networks
• User behavior on website
• „smart forms“
Risk
- position
- keywords in text
- average time on position
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MARKETING WHAT IS THE CUSTOMER BEHAVIOUR?
• Customer 360 view
• Propensity to buy
• Next best offer
• Churn analysis
Commerce
E-commerce
CRM
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
FRAUD WHO AND HOW? HOW TO FIND OUT?
• Insurance fraud
• Rules
• Social Network Analysis
• Text Mining
• Banking - Online fraud
• Fast data – Event Stream Processing
• Analyzes transactions as they are received
• Alert
• Investigation
Fraud60+ millions CZK saved in ½ year
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
ONLINE FRAUD THE CLIENT’S PROBLEM?
Fraud
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
UTILITIES HOW TO MANAGE RISK?
• Predictive maintenance
• Smart meter analysis
Utilities
Real-time analytics
Batch analytics
ALERT
VISUALIZATION
REPORTS
DASHBOARDS
INSIGHTS
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MANUFACTURING HOW TO USE DATA FOR QUALITY MANAGEMENT?
• Quality management
• Parts fitting
• Sensor data analysis
Manufacturing
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MANUFACTURING HOW TO USE DATA FOR QUALITY MANAGEMENT?
• Quality management
• Parts fitting
• Sensor data analysis
Manufacturing
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
MANUFACTURING HOW TO USE DATA FOR QUALITY MANAGEMENT?
• Quality management
• Parts fitting
• Sensor data analysis
Manufacturing
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
1. Activation 2. Set Preferences 3. See Featured Offers
6. View Offer Details8. Accept
TELCO – BIG DATA
5. View Offer on Map
7. Get Offer
4. Receive push
notification of local offer
9. Use in store
Commerce & Telco
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
WHY DO WE NEED TO ANALYZE DATA?
INSIGHT MONEY
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
HOW DATA VISUALIZATION
• Goal: easy to understand graphical representation of data using
• Shapes
• Colors
• Color intensity
• Position of shapes
• …
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
HOW DATA VISUALIZATION – PATTERNS, RELATIONSHIPS, …
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
SAS VISUAL
ANALYTICSEXPLORE YOUR DATA
• Visualization types
• Table analysis (pivot)
• Common graph types
• Powerful filtrations, selections
• Geo-analysis
• Analytics for business people
• Forecasting
• What-if analysis
• Correlation matrix
• Heat, tree maps
• Text analytics
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
SAS VISUAL
STATISTICSRAPID MODEL PROTOTYPING
• Exploratory predictive modeling, in-memory
• Linear regression
• Logistic regression
• GLM
• Clustering
• Model comparison
• Score code
• Group by modeling
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
SAS VISUAL
STATISTICSGROUP BY MODELING
• Big Data – one model doesn´t fit all
• Find „groups“ (clustering)
• Make models for each „group“
• Market segment
• Customer segment
• …
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
SAS
Data
In-Database Approach
SAS
Traditional Approach
HOW IS SAS
LEVERAGING
HADOOP?
DEPLOYMENT PATTERNS
SAS
Data
In-Memory Approach
MemoryData
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
SAS&HADOOP
DISTRIBUTIONSPREFERRED CLOUDERA & HORTONWORKS
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
WHY TO MAKE RELEVANT DECISIONS!
INSIGHT MONEY
C op yr i g h t © 2015, SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .
THANK YOU!
