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Big Data Overview. Stephen Simpson Sharply Unclear. V1.1 February 2013. What is B ig Data ?. Major evolution of BI. Multiple Core enterprise systems. Consumer social data. Builds on technological innovations from consumer Internet - PowerPoint PPT Presentation
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Big Data OverviewSTEPHEN S IMPSON
SHARPLYUNCLEAR
V1.1 February 2013
What is Big Data?
3
Major evolution of BI• Builds on technological innovations
from consumer Internet• Data arrives in large volumes at
unpredictable times, in variety of forms, and from different sources
• Different sources somehow related, but complex associations
• May require granular real-time analysis & follow-up actions
• Data structures specified at runtime • Scales linearly – start small• Commodity hardware & open
source software• Economical & practical to keep vast
historical volumes on-line
Big Data IT Business
Big storageBig analytics
Big InsightsBig Returns(?)
Consumer social data
Multiple Core enterprise systems
M2M sensor data
Operational data (for digital forensics)
E-mail & ECM
External data sources
Where Big Data countsFinancial Services
Customer Risk Analysis Surveillance & Fraud Detection Central Data Repository Personalisation & Asset Management Market Risk Modelling Trade Performance Analytics
Retail, Manufacturing & Telco Customer Churn Analysis Brand and Sentiment Analysis POS Transaction Analysis Pricing Models Customer Loyalty Targeted Offers Manufacturing Plant monitoring Sensor analytics & insight
Government & Energy Security Digital Forensics Utilities and Power Grid Smart Meters Biodiversity Indexing Medical Research & Seismic Data Tax evasion
Web & e-Commerce Online Media Mobile Online Gaming Search Quality Recommendations Influence Preventing Network Failures
5
Transparency◦ Information available across all organization’s businesses
Segmenting Populations◦ Social network influencers◦ Client micro-segmentation
Supporting human decisions with algorithms◦ Best-next-product recommendations◦ Optimising preventative maintenance downtime
Enabling experimentation◦ Champion/challenger hypothesis testing
New business models, products & services◦ Core processes “listening” to outside world◦ Make Mobile real-time context-based offers
Out manoeuvring & disrupting the competition◦ Operating at a faster tempo to generate rapidly changing conditions
Big Data creates business value
6
The most successful businesses…
…Collect & analyse information better. Act upon that information faster. Incorporate learning into next iteration
Hindsight Oversight Insight Foresight
Business Intelligence Operational Intelligence
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Operational intelligence requires agility
Acting quickly offers major competitive advantage
Long cycle times are wasteful:◦ Don’t know what is important
or accurate until test hypothesis◦ Reduces collective ability of
organisation to learn
Speed in learning amplifies effectiveness of everything else
◦ Use agile techniques to reduce scope of each cycle
◦ Use results of cycle to decide what to do next
What’s happening in the market?
The industry watchers say
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$5B market. 40% CAGR IDC
$50B market by 201758% CAGR wikibon.org
5% market is technologies. The majority is services Forbes
What do clients need?
10
Clients need guidance & new skills
Leadership◦ New capabilities to ask right
questions◦ Provide clear business goals◦ Define success for company
Talent Management◦ Business linkage: what learnt,
and how to use it?◦ To handle large data sets◦ Statistics
12
Technology◦ Unstructured data modelling◦ Hadoop (Big Data storage engine)◦ Analytics & Visualisation
Decision Making◦ Authority & responsibility◦ Co-located with information
collection point
Culture◦ Hunches : what do we think? ◦ What do we know?◦ Weaving into fabric of daily operations
12
Critical success factors Data sources
◦ Creatively sourcing internal & external data◦ Upgrading IT architecture & infrastructure
◦ To ensure easy & fast ingestion, storage and retrieval of data
Prediction & Optimisation Models◦ Focusing on biggest drivers of performance◦ Building or acquiring models that balance complexity with ease of use
Organisational Transformation◦ Simple, understandable tools for people on front lines◦ Updating processes & developing business capabilities to use tools effectively◦ Linking Mobile, Cloud, Big Data and Social
13
What do clients want? Proof of Concepts
◦ Agile: succeed (or fail) quickly and cheaply◦ Successful ones will evolve into much larger programs
Services against the “Big Data skills gap”◦ Provision of skills identified earlier in this presentation
Trusted Advisors◦ Route map: clear & honest balancing of cost, value & limitations◦ Identification and advice on best technology providers
Value propositions◦ With credible references◦ Configurable by appropriately trained internal staff
14
Asset IntelligenceLoad factor, Faults,
Conditions
Situational IntelligenceVoltage & Quality, Post
Fault Demand Side Mgmt., Outages
Forecasting IntelligenceAsset Failure Prediction
Smart interventionsCapacity Operations
Planning
Possible incremental approach in UtilitiesFurther Big Data opportunities across Business divisions:
Supply Chain Management
Revenue Assurance
Personalised Pricing
Consumer mobile
Supply BusinessGeneration Business
AggregatorsDemand Side
Response Operator
2013
2013/4
2016
2018
2020
AssetIntelligence
SituationalIntelligence
ForecastingIntelligence
Proof of Concept
Internal dataset enhancement to
network, premises & integration
Smart Grid Monitoring
Smart Grid & Meter Data
value extraction
Smart Data correlation to external cloud weather/social
media data
Incremental approach can be taken to all verticals