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Overview of the Big Data ecosystem and its use in the telecommunications industry


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Study about the big data environment and some leads to make the most of it (from the perspective of the telecommunications sector)

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Overview of the Big Data ecosystem and its use in the telecommunications industry

  1. 1. Big Data Overview of a Big Data eco system and its use in the telecommunication industry Bearing Point .
  2. 2. Big Data revolutionizes the way companies do business allowing to develop new strategies as well as optimize processes Big Data affects all organizations from different industries - Over 90% of all data in the world was created in the past 2 years - The amount of data transferred over mobile networks increased by 81% to 1.5 exabytes (1.5 billion gigabytes per month between 2012 and 2014) - 1.9 million IT jobs will be created in the US by 2015 to carry out big data projects 40 30 20 10 2009 ‘Source: HP Vertica M 2011 I Zettabytes Big Data growthl 2015 2020 Peters d an ergaard, Gartner Research _ - _ _. J yr. ’ ‘I (IIYOU can have data ~ ' witho cannot have information d technola9"5t ‘ Daniel Keys Moran, author an 1-’ 1 without informatiorj, but You ut data. G9°fi'Fey Moor ‘ 9, author and consultant _- _ :2 BearingPoint.
  3. 3. Exploiting big data for business usage requires companies to define their use cases and navigate through the value chain to select the right setup and partners Big Data Value Chain Overview 1. 2. 3. . 5. Data Data Storing Data Data Generation and Accessing Preparation Usage Content IAn enormous variety Retrieved data is ITo make big data ready IAna| ytics allows ITrans| ating conclusions {of data sources have to I stored and made Ifor analytics, available I examination of raw I into implementable be made accessible to accessible while data has to be data with the purpose business actions and Eexploit analytical value Iassuring data integrity consolidated, normed Ito draw conclusions measures is key to Iof big data I and standardized. This I either by deep dive I materialize big data ‘ I I data preparation is a I analytics or I I pre-requisite for data Ivisualization of data _ Data types I I - Transformation I I - Decision support Data origins ° Access ° Loading 3- Predict 3- Just in time business Characteristics 5° Data velocity I g° Consolidation - Report modeling Main players . Teradata MapR Iflypefcubeo NetBeans (exemplary) Oracle SPSS '“"' “"" 3- MySQL 1° APEX - Oracle 3 - Sybase I - Hadoop I - SAP I i I- Amazon Web I - IBM I Services I - Autonomy (HP) lSI2"5‘B"§‘ I I‘ 3". °“l’~“‘ I - - lnfochimps l - Daataari/ iI: tch I I- Google BigQuery I- Microsoft Source: BearingPoint analysis 3 BearingPoint.
  4. 4. MNCs are adopting Big Data generated knowledge along different areas of intervention both for internal and external purposes Most of the revenues potential lays within the monetary value of raw data and insights Areas of Intervention Customer centricity and customer experience management / “ New Business Model Risk and Financial services 0 Operational Optimization Targeting product and marketing offers Sentiment Analysis Churn Prevention Enhancing the multichannel consumer experience Predictive Policing Fraud Management and prevention Revenue assurance Market fluctuation forecasting Network Optimization Proactive issue resolution Real time FAQ broadcasting IT operational efficiency Internal Use ’IDATE A Big Data for Telcos: How Big Data can get new revenues and reduce costs_DecA2D13 4 Customer micro segmentation Sentiment Analysis Location based Marketing Geo targeted advertising Revenue sharing with digital retailers Health care monitoring Safety tracking of Family members Marketing Insights Extemal use Focus on Monetization ‘r Mobile revenue from selling mobile- user behaviour data may reach fl billion USD in 2016* including both insights and raw data sales ‘r The competition on this segment is low: 1) no OTTs offering such concept 2) mobile data: CDRS, location & demographics are unique to telcos ‘r There are not many initiative around the sales of data so far Data Usage BearingPoint.
  5. 5. Big Data : monetization to share the benefits of data ownership Use case : Morrissons used Telefonica Dynamic Insights solution to improve its customers loyalty Morrissons wishes to improve the loyalty of its customers without any heavy investment Dynamic Insight precisely analyze the footfall 1 Data anonymization A huge potential : in UK, households 1 go shopping to the supermarkets on 2 an average of around 5 times a week Aggregation: The hashed values are aggregated into groups Extrapolation: The sample is taken and extrapolated to population totals 2 But many rotations between 2 or 3 3 supermarket chains Morrisons stake is to better define customer targeting and 3 households that should be targeted 4 comparisons with the f0" C°UP0n5 Sending performance of challengers) The main source of Smart Steps data comes from the information about 02 sims users The data from 02 network is anonymized, but the main characteristics of the people having a phone are kept (gender, age) . _ __ E E; ., " This information is enriched with Morrissons . . . , " ' / own data V t —_. : Source : BearingPoint analysis 5 Thanks to 02 monetized data and solution, Morrissons was able to Benefit from as much customers data as with 10 years of conventional loyalty programs + Better define the good areas to targets and those which should be avoided + Optimize the sending of coupons to households which were not already regular customers + Limit the expenses on costly loyalty programs f. l-.1|%-1ir-iir-eI= .I»~r-eI1i- Iii-e-v-. Im-I-. iiIimi Il= I'I'FI)r“l§I: lIln'l'iii= I!IWIIllII-‘IQIIIH -imt-iiixam BearingPoint.
  6. 6. Big Data : improving existing products by understanding the work environment Use case : Vestas Wind Systems and wind turbines optimization Vestas wished to make the most of The method aimed to determine the its already developed products by weather at any point and time Pinpointing of the best spots to GI b Ifl d I. 1 deploy wind turbines 1 O a OW mo 9 mg 2 Maximizing the power generated 2 Mesoscaie modeiing 3 Reducing energy costs 3 Wind library generation . . Determining the wind 4 Reducing the downtime 4 characteristics The powerful software and hardware used by Vestas allowed it to efficiently deal with a always growing set of parameters (around 200 today) Data from 27,000 of the wind turbines and the history of the company 35,000 meteorological stations around the world Z Coming soon : g|oba| deforestation metrics, satellite images, Historical data Source : BearingPoint analysis 6 Vestas enhanced its wind turbines abilities Wind forecasting response time reduced by approximately 97% + Development time for a wind turbine site reduced by nearly a month + Enhanced ability to deal with huge amounts of data + Cost per kilowatt hour lowered for the customers (ROI increased) Optimization of the wind forecasting response time and of the Clients ROI What could be reused by telecoms ? I Optimization of the antennae network thanks to the use of: I data from the existing antennae I users data, including the strength of the signal at each time and location BearingPoint.
  7. 7. Companies benefit according to the revolutionary development of Big Data Telecommunication industries can be inspired by other industry sectors already using Big Data Benefits for telecommunication operators Opportunities for telecommunication industries already using Big Data based on benefits of other industries . . A. ' Increasing the companies’ turnover - Big increase of customer data | ‘ ' Customer loyalty is forced up ° Optimize offers ° Opportunity for additional value ' Reducing editing time .23 ° Reducing communications fraud J ' Drive operational intelligence, agility and cost effectiveness _'- < I - Understanding of customer requirements and as ’ adaptation of products (I ' Discovering & removing superfluous 5 functions in products ‘ Telecommunication operators are the natural pioneers and leaders of the Big Data trend There is still room for improvement They can greatly benefit from crossing their technology with the more diverse and innovative strategies ofthe newcomers. 7 BearingPoint.
  8. 8. Thank you BearlngPolnt France SAS Immeuble Galllée 51, Esplanade du General de Gaulle FR-92907 Paris Ia Defense Cedex, France T463 1 58853000 M france@bearinggoint. com wwwbearingmintcom flllllllllll i Bearing Points
  9. 9. Bearing Point®