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What is Big Data?


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Presented by Daniel Ling and Magnus Ebbesson at Findability Day, 14th of June 2012 in Stockholm.

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What is Big Data?

  1. 1. WHAT IS BIG DATA ? Findability Day 2012, Stockholm 14th of June, by Daniel Ling and Magnus Ebbesson © FINDWISE 2012
  2. 2. BIG DATA by Findwise!•  VOLUME   !! •  Sift through the noise to identify the right data to improve business insight•  VELOCITY   •  Analyse more data in less time to facilitate faster more responsive business decision making•  VARIETY   •  Identify, mine and capitalize on new data sources and integrate them with existing data for deeper insights•  VISUALIZATION   •  Present data in a meaningful and user friendly way to drive better business decision across your organization
  3. 3. Big Data Dimensions !!
  4. 4. Big Data and Search!Database Big Data tools Search!
  5. 5. Findability Usage!          Enterprise Search     Application or Nische     Info Hub and Big Data•  Findability  within   •  Search  within  specific   •  Indexing  and   Enterprise  Content.   applica=ons.   processing  of  internal  •  Generic  search,   •  Applica=on  may  be   and  external  data.   Intranets  etc.   desktop  client,  nische   •  Search  and  aggregate.   portal  etc.   •  Informa=on  hubs.       •  Big  Data                  
  6. 6. Why Big Data – because of growth?"•  An"" estimated 90% of the world’s data (from the WWW and machine generated data from network nodes and applications) has been created over the past two year •  The data is doubling every two years and global annual data creation is set to leap from 1.2 zettabytes in 2012 to 35 zettabytes in 2020 (IDC’s2011 Digital Universe Report) •  Walmart handles more than 1 million customer transactions every hour •  Every day, we create 2.5 quintillion bytes of data •  Unstructured information is growing 15 times the rate of structured information
  8. 8. Big Data strategy – extract business value"•  Data as an asset - evaluate how the right data strategy will make your business more agile, competitive and profitable •  Identify the business drivers in your data assets •  Start with a plan – understand the importance of devising a viable and workable roadmap for your big datajourney •  Clarify your priorities – determine where big data analysis is most needed now in your organisation •  Planning future success – using insights from big data to increase the value of predictive analytics.  
  9. 9. Big Data strategy – choose the right tools"•  Define which technology strategy will enable scalable, accurate, and powerful analysis of the data •  Find out how to select the best big data solutions for your specific business needs •  Discuss the key questions you need to be asking when evaluating technology partners •  Determine what you want to get out of your big data investments and how to communicate this to potential vendors  
  10. 10. Use case: Insurance Industry"•  Analyzing both internal information in claims and databases, combining it with external data from social media and third parties etc. •  Processing both structured and semi-structured data in large scale to find patterns. •  Example 1: A prospective policyholder with numerous speeding tickets is more likely than a safer driver to end up with a sports injury. •  Example 2: Publicly available social data will be increasingly useful in helping insurers distinguish clients. •  Example 3: Mining Facebook and Twitter for promising sales leads, example: a woman proud of her pregnancy might want to buy life insurance.  
  11. 11. Use case: Banking"•  Analyzing the customers transaction data, enabling visualizing and search on the big data sets. •  Enriching the information: with geo coordinates, transaction category and other metadata.  
  12. 12. June 15, 2012