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Big Data Overview

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

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Big Data Overview

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  1. Big Data Overview Stephen Simpson SharplyUnclear V1.1 February 2013

  2. What is Big Data?

  3. Major evolution of BI Multiple Core enterprise systems Consumer social data • 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 M2M sensor data Big Data Business IT E-mail & ECM Big Insights Big Returns(?) Big storage Big analytics External data sources Operational data (for digital forensics)

  4. Where Big Data counts Financial Services • Customer Risk Analysis • Surveillance & Fraud Detection • Central Data Repository • Personalisation & Asset Management • Market Risk Modelling • Trade Performance Analytics 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 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

  5. Big Data creates business value • 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

  6. The most successful businesses… • …Collect & analyse information better. Act upon that information faster. Incorporate learning into next iteration Business Intelligence Operational Intelligence Foresight Insight Hindsight Oversight

  7. 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

  8. What’s happening in the market?

  9. The industry watchers say $5B market. 40% CAGR IDC $50B market by 2017 58% CAGR wikibon.org 5% market is technologies. The majority is services Forbes

  10. What do clients need?

  11. Clients need guidance & new skills • 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 • 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. 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. Possible incremental approach in Utilities • Further Big Data opportunities across Business divisions: • Supply Chain Management • Revenue Assurance • Personalised Pricing • Consumer mobile ForecastingIntelligence SituationalIntelligence 2020 Smart Data correlation to external cloud weather/social media data AssetIntelligence 2018 Smart Grid & Meter Data value extraction Asset Intelligence Load factor, Faults, Conditions Situational Intelligence Voltage & Quality, Post Fault Demand Side Mgmt., Outages Forecasting Intelligence Asset Failure Prediction Smart interventions Capacity Operations Planning 2016 Smart Grid Monitoring 2013/4 Internal dataset enhancement to network, premises & integration Supply Business Generation Business 2013 Aggregators Proof of Concept Demand Side Response Operator Incremental approach can be taken to all verticals

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