Big data - Key Enablers, Drivers & Challenges
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Big data is characterized by the 3 Vs - volume, velocity, and variety. The document discusses how big data is growing exponentially due to factors like the internet of things. Key enablers of big data include data storage, computation capacity, and data availability. Addressing big data requires technologies, techniques, and talent across the value chain of aggregating, analyzing, and consuming data to derive value. However, big data also presents management challenges around decision making, change management, technology clashes, and skills shortages. The document provides an example of how big data could help sales professionals better prepare for client meetings.
Big data - Key Enablers, Drivers & Challenges
- 1. Big Data – Let’s Embrace It! By Shilpi Sharma Nov, 2012 When Execution Matters 1 (Confidential)
- 2. Topics Covered V3 and Enablers 3Ts and Challenges Use Case: Sales Enablement When Execution Matters 2 (Confidential)
- 3. Big Data Characteristics – V3 A petabyte is one quadrillion bytes, or the equivalent of about 20 million filing cabinets’ worth of text. An exabyte is 1,000 times that amount, or one billion gigabytes. As of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months or so. More data cross the internet every second than were stored in the entire internet just 20 years ago. 30Bn pieces of content shared on Facebook every month When Execution Matters 3 (Confidential)
- 4. What is Big Data? When Execution Matters 4 (Confidential)
- 5. Key Enablers When Execution Matters 5 (Confidential)
- 6. Key Enabler – Data Storage When Execution Matters 6 (Confidential)
- 7. Key Enabler – Computation Capacity When Execution Matters 7 (Confidential)
- 8. Key Enabler – Data Availability When Execution Matters 8 (Confidential)
- 9. Key Drivers – Internet of Things & Big Data of 2B electricity utility meters 18% are smart meters Intelligent machines fighting 50B+ for bandwidth by 2020 When Execution Matters 9 (Confidential)
- 10. Gartner Emerging Technologies Hype Cycle 2012 Investments in Big Data $5B+ Infrastructure (2009-2011) When Execution Matters 10 (Confidential)
- 11. Industry Sectors Rich in Big Data When Execution Matters 11 (Confidential)
- 12. Value Potential Across Sectors For Hi-Tech Companies, Big Data is generated from Value Chain When Execution Matters 12 (Confidential)
- 13. Readiness Across Sectors Information is the only industry that will get most value from Big Data with ease. When Execution Matters 13 (Confidential)
- 14. Big Data – 3Ts Technologies, Techniques & Talent When Execution Matters 14 (Confidential)
- 15. Big Data Technologies Where processing is hosted? Distributed Servers/Cloud (e.g. Amazon EC2) Where data is stored? Distributed Storage (e.g. Hadoop DFS) What is programming model? Distributed Processing (e.g. MapReduce) How data is stored& indexed? High-performance schema-free database (e.g. Cassandra) What operations are performed? Data Analytics, Semantic Processing (e.g. R) When Execution Matters 15 (Confidential)
- 16. Big Data Techniques A set of techniques to extract patterns from large datasets by combining methods from statistics and machine learning with database management. Few examples: Supervised Learning – Support Vector Machine Unsupervised learning – Cluster Analysis Data fusion – Signal processing, Natural Language Processing Optimization – Genetic Algorithm, Neural Networks Predictive Modeling – Regression, Time Series Analysis When Execution Matters 16 (Confidential)
- 17. Big Data Talent When Execution Matters 17 (Confidential)
- 18. Big Data Value Chain Aggregate Analyze Consume Derive Value Data Data Data Data Integration (from Smart Sampling of Visualization Connect the dots multiple sources) Data (Actionable Insights) Data harmonization Finding similar items (multi-rate, noisy, missing) Building Models and incremental updating of Data Classification models Change Management Data Policy & Governance Technology Management When Execution Matters 18 (Confidential)
- 19. Big Data – Management Challenges Big data brings the potential for transformation, not the actual transformation Change Decision Making Management Clash of Shortage of Skills Technologies When Execution Matters 19 (Confidential)
- 20. Food for Thought When Execution Matters 20 (Confidential)
- 21. Take an example – A Client Meeting Types of Data: Internal Information: Company, Presentations, collateral, pricing, contracts Personal Information: Territory assignment, Goal Attainment, Past interactions with customer External Information: Company, People, Competition, Market Data Sources: Suddenly you are going from a few office documents to hundreds of files and channels that are being continually updated. Static like a webpage, personal profile, competitive cheat sheet Dynamic like a YouTube channel demonstrating a competitor’s product, a blog reviewing an announcement, or twitter channel When Execution Matters 21 (Confidential)
- 22. Some Facts $135,262 – Average support costs per year for each salesperson 7 hours/week - Average salesperson spends looking for relevant information to prepare for sales calls 50% of the information is pushed through email; only 10% is made available in a useful format Source: Forrester Research & IDC Sales Advisory Service When Execution Matters 22 (Confidential)
- 23. Big Data Application Connect the dots across internal and external data for sales professional What has been sold at client? How it has been working? Where the industry is moving? What are top challenges for the decision makers? How does it connect to product portfolio you are selling? What has been the buying pattern at client? Any new insights based on Install Base? Win More Deals, Increase Productivity, Sell Smarter When Execution Matters 23 (Confidential)
- 24. When Execution Matters 24 (Confidential)
Editor's Notes
- #4: Many of the most important sources of big data are relatively new. Facebook was launched in 2004, Twitter in 2006. Smartphones and the other mobile devices iPhone was unveiled only five years ago, and the iPad in 2010.
- #5: Highly structured data in these systems is typically stored in SQL databasesObservational data tends to come from the ‘Internet of things”Interactions are about how people and things interact with each other or with your business.Web Logs, User Click Streams, Social Interactions & Feeds, and User-Generated Content are classic places to find Interaction data
- #10: According to Pike Research, in 2008 a mere 4% of the planet’s 1.5 billion electric utility meters were smart meters; today that has jumped to 18% of electric meters installed.Vehicle-to-vehicle (V2V) communications is also rapidly emerging as another M2M market. Currently, the U.S. Department of Transportation is working with the University of Michigan to test V2V systems on 3,000 vehicles. Logistics companies with more than 9 million vehicles in the U.S. are watching the results of that study carefully because of the promised savings in V2V operating costs.2020 researchers expect there to be more than 6 billion wireless subscribers using smartphones. However, Swedish communications giant Ericsson predicts that there will be over 50 billion intelligent machines fighting for bandwidth by then.Undoubtedly, networks will be faster in eight years. I expect there will be improvements in compression techniques and methods to limit M2M interactivity as well as other ways to boost network performance and capacity. Pricing will be another way carriers will be able to manage network loads.
- #11: http://www.itworld.com/it-managementstrategy/289534/big-data-startups-lure-investment-dollars
- #16: Computing and storage are typically hosted transparently on cloud infra- provide scale, flexibility and high fail-safety (reduced upfront cost)Distrbuted processing of Big-data requires non-standard programming models – beyong single machines or traditional parallel programming models (like MPI)..aim is to simplify complex programming tasksNoSQL databases support large amount of data cost effectively, flexible and fast (no predefined schema needed) and operate on distributed infra
- #17: Computing and storage are typically hosted transparently on cloud infra- provide scale, flexibility and high fail-safety (reduced upfront cost)Distrbuted processing of Big-data requires non-standard programming models – beyong single machines or traditional parallel programming models (like MPI)..aim is to simplify complex programming tasksNoSQL databases support large amount of data cost effectively, flexible and fast (no predefined schema needed) and operate on distributed infra
- #22: BenefitsIncreasing Personal and Team ProductivityDecreasing Ramp TimeIncreasing Win Rate
- #23: http://www.sambacloud.com/what-is-samba-cloudA typical sales professional is mostly travelling, coordinating via email and work on different, out-of-date versions of content on their laptops or tablet’s cloud drives. The deal history is spread across everyone’s local version.