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Big data : a crucial challenge for energy players


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Big data revolution is in motion in the energy sector as implementation of profitable strategies requires processing and interpretation of a growing flow of data.
We strongly believe that by investing in new forms of data processing, energy players will take the right steps towards usable decision making data.
Keeping in mind that in a highly competitive environment, missing the Big Data boat could cause disastrous shipwrecks.

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Big data : a crucial challenge for energy players

  1. 1. Big Data: A crucial challenge for energy players July 2014 Twitter : @ch_retail
  2. 2. 2 Big Data revolution is in motion in the energy sector  Given recent evolutions on energy markets, remaining competitive implies to be able to process a growing flow of data.  We strongly believe that the keys to success are two- fold when it comes to designing a successful Big Data strategy: • A structured and robust framework • A continuous upgrade of hardware and infrastructure to stick to volume of data and complexity of analyses  Big Data represents a source of business opportunities for energy players…  … from rethinking client relationship to crafting tomorrow’s risk management. Quality Depth Exhausti- veness Centrali- sation « Real time » data « Data storage » Exogen- ous data Big Data
  3. 3. 3 Evolutions within energy markets imply a growing flow of data to be processed…  Energy players strengthen their international footprint as more regions reach crucial liquidity to generate significant trading volumes …  … and multi-commodity players are becoming the rule  Extension to 24 hours/7 days opened derivatives and spot exchanges • Pan-European Gas Market Exchange (PEGAS) will be one, as of July (still no exchange in Spain) • New products emerge that can modify supply and hedging options and strategy  Numerous parameters required to optimize portfolio management (power plants)  Providers increase real-time flow on meteorological data  Development of smart metering increases the volume and frequency of data collection: • Giving suppliers/ shippers a better view on how energy is consumed (at grid or user level)… • And enabling operators (transmission and distribution systems, LNG Terminal & Storage) to publish real time data and forecasts  Supply problems or political and regulatory decisions must be known rapidly and taken into account in order to be exploited on several time horizons … as business conduct gets more complex … and more information becomes available
  4. 4. Implementation of a successful Big Data strategy requires a structured and robust framework, from acquisition of info to decision-making process Acquire AnalyzeOrganize Decide1 3 4 6 Control2 Report5  Structured data: • Ex. market data gathered both on supply and demand side  Unstructured data: • Non-parametric statistics and unconventional data • Ex.: Twitter content  A dataset clean of errors • Wrong data can cause you to make wrong decisions • Warning mechanism and auto correction replacing wrong data with substitutes  Process collected data: • Reduce -> Aggregate -> Enrich -> Structure  Create datasets: • Use relational technology to assemble and match data  Data modeling: • Fast read and write speeds • Build statistical models comparing data sets’ variables and datasets  Find correlations: • Bring additional value to classical market analysis • Machine learning approach to capitalize on past events knowledge  Sort through to extract the useful and stay synthetic • Give it meaning, and make it exploitable, • Show the big picture and focus on a few key points  Anticipate • Predict market trends / price • Estimate likelihood of external factors (weather, media impact …)  Make predictive decisions • New market positions to enhance margins • Ex: new capacity planning, capacity expansions
  5. 5. 5 Big Data requires investing in new forms of data processing… Integrated framework Dynamic framework Static framework 3 4 5 6 2 1 0 Data Collection  Data warehouse  IT implementation & Infrastructure Data Storage  Historical data Data Cleaning  Data quality management  Data transformation Data Statistical Description  Mean, Median, standard deviation  Histogram…| VaR 95,99% Data Analysis  Clustering & segmentation  Automatic classification | Factorial analysis “X factor” Mining  Web mining (behavior…)  Image mining (face recognition) | Text mining Data Mining & Big Data  Prediction  Ranking / discrimination  Anticipation & simulation Today’s average position Past Future Present 7 Artificial Intelligence  Self-Learning models (auto efficient)  Multi crossing data set Integrated framework Dynamic framework Static framework
  6. 6. 6 … that will necessitate continuous hardware and infrastructure upgrades to adapt to the volume and complexity of data to be managed Flexibility & Adaptability  Change the way you treat the data according to your continuous experience  You always need to handle more data, more frequently and with agility, implying great storage capacities Power & Speed  Windows of opportunity may close up quickly therefore your calculation speed should be optimized ASAP  As long as you keep manual steps, you will not reach optimum -> automation must be your motto Modeling Computation Storage Self-detection, auto upgrading and efficient models Optimized computation capacity Centralized, unlimited data storage capacity Homogenisation of modeling practices, business incentives for Data modelling technique development Good computation capacity Aggregation of data sources (Finance, Risk, Marketing, Sales…) R&D Development Limited computation capacity Limited storage capacity Integrated Dynamic Static
  7. 7. 7 Numerical transformation induced by Big Data has root causes and objectives that transcend sectors, and the energy industry is no exception Source : Ventana Research | 2013 Source : Analytics | IBM Institute | 2012 Refocus on customer (CRM) 55% Process optimisation incl. cost optimisation 4% New business model 15% Risk management/ Financial reporting 23% Collaborative working mode 2% Cost reductions Reduce and limit manual steps Produce daily results evermore precisely Increase the compute speed Store and analyze even more data But in addition to these ‘standard’ objectives, players on energy markets will focus on specific issues…
  8. 8. 8  On-demand data mining to dig into meta-data  Risk and P&L indicators (calculation and reporting)  P&L explain – detection of abnormal variations - VaR back-testing  Data preparation for EMIR, Basel II/III, MIFID, REMIT, audits …  To support KYC, rogue trading, AML or anti-fraud process …  Pre-trade decision support (ex. locational/ geographical spreads, time spreads on storage etc.)  To help identify trades from various systems to avoid missed or duplicated trades … with business or process orientations  Automatically executed quantitative processes or High-Frequency-Trading  Real-time optimization of day-ahead and intra-day position coverage  Optimize client consumption forecasts (short – medium – long-term)  Optimize production forecasts (generation) Business-oriented issues Process-orientedissues Data Tagging Trading Analytics Watch Tower Regulation Financial Data Management Forecasting Hedging Strategy Systematic Trading
  9. 9. 9 Don’t let yourself be overrun by competitors …  Hardware and infrastructure upgrades induced by Big Data (storage, calculation capacity, etc.) must not overshadow the necessity of investing in human capital.  Players on energy markets that will best ride the Big Data wave will not only get their heads above water in a harsh competition context…  Controlling the three V’s of data (Variety, Volume and Velocity) creates an alternative information edge, which is: • A potential new source of uncorrelated excess returns • Advanced techniques for valuing clients and deals • Most helpful in risk and performance management • Easing data management for internal purposes
  10. 10. 10 CH&Cie at a glance Management Consultancy ... … for Financial Services & Commodities Retail Banking Private BankingCorporate & Investment Banking Insurance Commodities Customer Experience Risk & Finance IT & Operations Business Development
  11. 11. 11 8 offices around the world, in major trading and financial places… … 100+ consultants, with strong academic backgrounds and experience Business school 60% Engineering school 30% Others 10% In average, CH&Cie consultants have 7 years of experience within consulting firms, Financial Services and Commodities.
  12. 12. 12 Your contacts for this offer Director - Head of CH&Cie Commodities + 33 6 40 56 21 71 Paris Office CEO & Partner + 44 78 34 55 03 98 + 33 6 12 41 64 06 London Office Partner + 44 203 427 3559 + 33 7 87 68 81 77 London Office Manager + 33 6 65 02 80 07 Paris Office Geneva Office Rue de Lausanne 80 CH 1202 Genève, Suisse London Office 50 Great Portland Street London W1W 7ND Paris Office 25 rue Alphonse de Neuville 75017 PARIS