Big data: status atual e tendências
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Transcript of Big data: status atual e tendências
Big Data: Perspectivas atuais e futuras.
Cezar Taurion
Executivo de Novas TecnologiasChief [email protected]
A SOCIEDADE HIPERCONECTADA
TECNOLOGIAPERVASIVA E COMPUTAÇÃO SOCIAL
UMA NOVO AMBIENTE DE TRABALHO
TUDO EM TEMPO REAL
UMA NOVA GERAÇÃO
Celulares/ smartphones/tablets já se igualam em numero àpopulação do planeta…
… 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
WiFi Zone
Cellular (WAN)
Vehicle-to-Vehicle
Vehicle to Roadside
Tolling
Satellite
Vehicle and Road Data
The Connected Vehicle – ‘A System of
systems’
DCAN
Ethernet
Most
Bytefligh
FlexRay
ICOM CAN
ECU 1 ECU n
GPSNETWORK
GSMGPRSPLMN
IPNETWORK
PARTNER SYSTEMS• Police/Emergency
• Weather
• Traffic
• Concierge
• Vehicle registration
• Bank
• Helpdesk
• Government
• Utilities
• Insurance
(pay as you go)
Vehicle
Control
Unit
Dealer
ANALYTICS SYSTEMS• Vehicle Condition Monitoring• Prognostics• Advanced Diagnostics• SW fault analytics• Vehicle Repair
EV/Hybrid Charging
BUSINESS SYSTEMS• Customer Support• Service Data• Warranty Data
PDA
Forecasts call for billions and billions of connected devices
9
50 Billion Connections in 2020 –Ericsson (from page 18 of 2010 annual report)
Ericsson CEO
Hans Vestberg
estimates 50 billion
devices will be
connected to the
Web by 2020
2012: 2800 Bilhões de GB!
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
Web Logs, URLs
Social Data
Text data: emails, chats
Traditional Approach
Structured, analytical, logical
New Approach
Creative, holistic thought, intuition
HadoopStreaming
Data
New Sources
UnstructuredExploratory
Iterative
StructuredRepeatable
Linear
Data Warehouse
TraditionalSources
Enterprise Integration
Internal App Data
Transaction Data
ERP data
Mainframe Data
OLTP System Data RFID, sensor data
Network Data
“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 long-
tail analysis, microsegmentation, next best
action, customer experience optimization,
and digital marketing applications” –Forrester
15
16
'Big Data' está ainda no canto da tela do radar dos CIOs/CEOs/Gestores…
Adoção de Big data
Some are just starting to explore
'Big Data'
Most are already debating/ evaluating/ considering
'Big Data'Adoção
A few are already/ still implementing
'Big Data'
Several plan to implement w/in the
near futureOnly a minority has not looked/ won't
look into it
Ignorants Early Explorers
Heavy Explorers
Planners Implementors
Improve operational
efficiency from machine data
Improve operational
efficiency from machine data
Clients are in an exploratory phase analyzing traditional data types to address challenges around Operations &
Customer Experience
Top business imperatives for using Big Data technologies:
Grow, retain & satisfy
customers
Grow, retain & satisfy
customers
Source: Ventana Research – The Challenge of Big Data Benchmark ResearchQ: What type of data are organizations analysing most? n = 163
Organizations are analyzing traditional types of data – most often Customer & Transaction data
Key imperatives for clients implementing Big Data technologies
Intelligent Infrastructure Management
�Optimize building energy consumption with centralized monitoring
�Automate preventive and corrective maintenance
Real-time Call Data Record Analytics
�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
17
Sentiment Analysis
Novas técnicas de visualização
The rise of the Data Scientist in 2013
20
“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” – ForresterData Scientist: The Sexiest Job of the 21st Century – Harvard Business Review
Big Data impacta todos setores de negócio…
Insurance
� Solvency II� Antifraud, Waste, Abuse
� Next Best Action
� Operational Risk� Policy Analytics
� Claims Analytics
� Single View of Customer
Banking
� Single View of Customer � Customer Centric� Asset Optimization � Security � Enterprise Ops Risk Mgmt� Credit Lifecycle Mgmt� Next Best Action� Fraud – AML� Digital Adoption
Telco
� Centralized BI Delivery Center� EDW and BI Transformation
� Call Detail Record Analytics
� Advanced Analytics Lab � Next Best Action
� Predictive Asset Optimization
� Network Analytics
Energy & Utilities
� Power Delivery Dashboard� CFO Performance Insight
� Smart Meter
� Customer Insight � Grid Analytics
� Risk Analytics
� Condition Based Maintenance
Media and Entertainment
• Audience Insight
• Business process transformation
Retail
� Customer Driven Loyalty Marketing
� Collaborative Analytics Platform� Intelligent Ops Center
� Customer MDM
� Social Media Segmentation
Travel and Transport
Consumer Products
� Post Event Analysis and Tracking
(DSR)
• Shelf Availability (SW)
• Promotional Spend Optimization (SW)
• Merchandising Compliance (SW)
Government
� Social Program Integrity
� Citizen Access and Insight
� Border Control Management
� Customs Risk Management
� Road User Charging
Healthcare
Automotive
� Actionable Consumer Intelligence
� Predictive Asset Optimization (Equip
Health & Mfg Quality and SCO)
Life Sciences
� Strategic Insight Portfolio (SIIP)
� Clinical Research Library
� Patient Adherence
Chemical and Petroleum
� Turnaround Management� Performance Mgmt System
� Drilling Analytics
� Master Data Management
Industrial Products
Electronics
� Predictive Asset Optimization
� Customer Analytics
� Quality Early Warning System
� Supply Chain Analytics
� Customer Loyalty & Insights
� Dynamic Social Media
Recommendations
� Production Design and Optimization
Scheduling
� Customer Segmentation and
Member Analytics
• Measure & Act on Population Health
Outcomes (SW)
• Engage Consumers in their
Healthcare (SW)
SW Business Use Cases
Como agir?
22
Funding
Source of Value
Sponsorship
Data
Platform Trust
Culture
Measurement
Strategy Technology Organization
Expertise
Establish a common visionto guide actions and deliver value
Create trustworthy relationships
Create confidencewith governance and security
Ensure alignment between analytic focus and value creation
Create value with rigor
and collaboration
Measure impact and model the future
Make decision based on facts
Increase knowledge-sharing opportunities
Integrate hardware and softwareto manage big data
Source: Analytics: A blueprint for value – Converting big data and analytics into results, IBM Institute for Business Value © 2013 IBM
Multiple Data Sources
Prediction & Optimisation
Models
Organizational Transformation
� Creatively source internal & external data
� Upgrade IT architecture and infrastructure for easy merging of data
� Focus on the biggest drivers of performance
� Build models that balance complexity with ease of use
� Create simple, understandable tools for people on the frontline.
� 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
11 22 33
Como agir?
Obrigado pela Atenção
Cezar [email protected]
https://www.ibm.com/developerworks/community/blogs/ctaurion/?lang=en
@ctaurionFacebook e Linkedin