Palestra sobre status atual de Big data e tendências para os próximos anos

1. Big Data: Perspectivas
atuais e futuras.
Cezar Taurion
Executivo de Novas Tecnologias
Chief Evangelist
ctaurion@br.ibm.com

2. TUDO EM
TEMPO REAL
TECNOLOGIA
PERVASIVA E
COMPUTAÇÃO
SOCIAL
UMA NOVO
AMBIENTE DE
TRABALHO
A SOCIEDADE
HIPERCONECTADA
UMA NOVA
GERAÇÃO

5. Celulares/ smartphones/tablets já se igualam em numero à
população do planeta…

6. … and this isn’t just about
connecting people
We are building systems of systems
Latest generation car:
100 electronic controllers
10 million lines of code
Its own IP address
Developed in 29 months (usually
a 60-120 month process)
General Motors - 2011 Chevy Volt
http://ibm.co/btsi5C

7. The Connected Vehicle – ‘A System of
systems’
ANALYTICS SYSTEMS
• Vehicle Condition Monitoring
• Prognostics
• Advanced Diagnostics
• SW fault analytics
• Vehicle Repair
GPS
NETWORK
Satellite
Cellular
(WAN)
Vehicle
Control
Unit
GSM
IP
GPRS NETWORK
PLMN
BUSINESS SYSTEMS
• Customer Support
• Service Data
• Warranty Data
WiFi Zone
ICOM
DCAN
Ethernet
Most
Bytefligh
FlexRay
CAN
ECU 1
Vehicle and
Road Data
ECU n
Dealer
PDA
Vehicle-toVehicle
Vehicle to
Roadside
Tolling
EV/Hybrid
Charging
PARTNER SYSTEMS
• Police/Emergency
• Weather
• Traffic
• Concierge
• Vehicle registration
• Bank
• Helpdesk
• Government
• Utilities
• Insurance
(pay as you go)

9. Forecasts call for billions and
billions of connected devices
Ericsson CEO
Hans Vestberg
estimates 50 billion
devices will be
connected to the
Web by 2020
50 Billion Connections in 2020 –
Ericsson (from page 18 of 2010 annual report)
9

10. 2012: 2800 Bilhões de GB!

12. Big Data refers to how to collect, store, and
manage information that comes into an
enterprise so that it can be harvested for
decision making
12

13. Traditional Approach
New Approach
Structured, analytical, logical
Creative, holistic thought, intuition
Hadoop
Streaming
Data
Data
Warehouse
Transaction Data
Web Logs, URLs
Text data: emails, chats
Internal App Data
Mainframe Data
Structured
Repeatable
Linear
Enterprise
Integration
Unstructured
Exploratory
Iterative
OLTP System Data
ERP data
Social Data
RFID, sensor data
Traditional
Sources
New
Sources
Network Data

14. “Clearly, the big data revolution is fostering a
powerful new type of data science. Having
more comprehensive data sets at our
disposal will enable more fine-grained longtail analysis, microsegmentation, next best
action, customer experience optimization,
and digital marketing applications” –
Forrester

15. 15

16. Adoção de Big data
'Big Data' está ainda no canto da tela do radar dos CIOs/CEOs/Gestores…
Most are already debating/
evaluating/ considering
'Big Data'
Some are just
starting to explore
'Big Data'
Several plan to
implement w/in the
near future
Only a minority has
not looked/ won't
look into it
Ignorants
16
Early
Explorers
Adoção
Heavy
Explorers
A few are already/
still implementing
'Big Data'
Planners
Implementors

17. Clients are in an exploratory phase analyzing traditional
data types to address challenges around Operations &
Customer Experience
Key imperatives for clients
implementing Big Data technologies
Top business imperatives for
using Big Data technologies:
Improve
Improve
operational
operational
efficiency from
efficiency from
machine data
machine data
Intelligent Infrastructure Management
Grow, retain &
Grow, retain &
satisfy
satisfy
customers
customers
Real-time Call Data Record Analytics
Optimize building energy consumption with
centralized monitoring
Automate preventive and corrective
maintenance
Organizations are analyzing traditional types of
data – most often Customer & Transaction data
Real-time mediation and analysis of 6B
CDRs per day
Data processing time reduced from 12 hrs
to 1 sec
Hardware cost reduced to 1/8th
Q: What type of data are organizations analysing most? n = 163
Source: Ventana Research – The Challenge of Big Data Benchmark Research
17

18. Sentiment Analysis

19. Novas técnicas de visualização

20. The rise of the Data Scientist in 2013
“A data scientist is someone who can
understand the desired business
outcome, examine the data, and create
hypotheses about how to establish
predictive rules that can enable
business outcomes such as increasing
eCommerce upsell, keeping a
production line running, or eliminating
stock-outs” – Forrester
20
Data Scientist: The Sexiest Job of the 21st
Century – Harvard Business Review

21. Big Data impacta todos setores de negócio…
SW Business Use Cases
Banking
Single View of Customer
Customer Centric
Asset Optimization
Security
Enterprise Ops Risk Mgmt
Credit Lifecycle Mgmt
Next Best Action
Fraud – AML
Digital Adoption
Media and
Entertainment
•
•
Audience Insight
Business process transformation
Automotive
Actionable Consumer Intelligence
Predictive Asset Optimization (Equip
Health & Mfg Quality and SCO)
Insurance
Telco
Solvency II
Antifraud, Waste, Abuse
Next Best Action
Operational Risk
Policy Analytics
Claims Analytics
Single View of Customer
Centralized BI Delivery Center
EDW and BI Transformation
Call Detail Record Analytics
Advanced Analytics Lab
Next Best Action
Predictive Asset Optimization
Network Analytics
Travel and
Transport
Customer Loyalty & Insights
Dynamic Social Media
Recommendations
Chemical and
Petroleum
Turnaround Management
Performance Mgmt System
Drilling Analytics
Master Data Management
Consumer
Products
•
•
•
Post Event Analysis and Tracking
(DSR)
Shelf Availability (SW)
Promotional Spend Optimization (SW)
Merchandising Compliance (SW)
Industrial
Products
Production Design and Optimization
Scheduling
Energy
& Utilities
Retail
Customer Driven Loyalty Marketing
Collaborative Analytics Platform
Intelligent Ops Center
Customer MDM
Social Media Segmentation
Power Delivery Dashboard
CFO Performance Insight
Smart Meter
Customer Insight
Grid Analytics
Risk Analytics
Condition Based Maintenance
Government
Social Program Integrity
Citizen Access and Insight
Border Control Management
Customs Risk Management
Road User Charging
Electronics
Predictive Asset Optimization
Customer Analytics
Quality Early Warning System
Supply Chain Analytics
Healthcare
•
•
Customer Segmentation and
Member Analytics
Measure & Act on Population Health
Outcomes (SW)
Engage Consumers in their
Healthcare (SW)
Life Sciences
Strategic Insight Portfolio (SIIP)
Clinical Research Library
Patient Adherence

22. Como agir?
Strategy
Technology
Organization
Sponsorship
Expertise
Culture
Establish a common vision
to guide actions and deliver value
Increase knowledge-sharing
opportunities
Make decision
based on facts
Source of Value
Data
Measurement
Ensure alignment between analytic
focus and value creation
Create confidence
with governance and security
Measure impact
and model the future
Funding
Platform
Trust
Create value with rigor
and collaboration
Integrate hardware and software
to manage big data
Create trustworthy
relationships
Source: Analytics: A blueprint for value – Converting big data and analytics into results, IBM Institute for Business Value © 2013 IBM
22

23. Como agir?
1
Multiple Data
Sources
Creatively source internal
& external data
Upgrade IT architecture
and infrastructure for easy
merging of data
2
Prediction &
Optimisation
Models
3
Organizational
Transformation
Focus on the biggest
drivers of
performance
Create simple,
understandable tools for
people on the frontline.
Build models that
balance complexity
with ease of use
Update processes and
develop capabilities to
enable tool use
Source : Making Advanced Analytics Work for You : A practical guide to capitalize on Big Data; Harvard Business Review, Oct. 2012

24. Cezar Taurion
ctaurion@br.ibm.com
https://www.ibm.com/developerworks/community/
blogs/ctaurion/?lang=en
@ctaurion
Facebook e Linkedin
Obrigado pela Atenção