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TDWI Presentation on Using Big Data Effectively

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Minn twdi 9 9

  1. 1. Big Data Analytics Using the information effectively
  2. 2. 9/10/2013 | 2 | ©2013 Ciber, Inc. Agenda • Big Data • Making Sense of it all • A Framework of Understanding • Topical information • Non Topical Information • Analytics • Examples • Getting there • Q&A
  3. 3. 9/10/2013 | 3 | ©2013 Ciber, Inc. Social Network Diagram • Contextual analytics is one of the hottest areas of interest pertaining to big data today • Smart companies know there is tremendous value in contextual analytics. But aggregating, categorizing, summarizing, exploring and contextualizing unstructured data is a big undertaking.
  4. 4. 9/10/2013 | 4 | ©2012 Ciber, Inc. Big Data
  5. 5. 9/10/2013 | 5 | ©2013 Ciber, Inc. What is the Big Data market? Source: “Big Data Market Size and Vendor Revenues”, Wikibon, Jeff Kelly, David Valante, David Elgyer, Feb 2013 – actual data through 2011 Acronyms: TBD = to be determined; SI = systems integrator; BPO = business process outsourcing
  6. 6. 9/10/2013 | 6 | ©2013 Ciber, Inc. Sample Industry Applications of Big Data Telco Call Detail Record (CDR) analytics for: • Customer service • Network planning • Regulatory compliance Financial Services Transaction analytics for: • Fraud detection • Customer retention • Distribution network planning (Branch, ATM, Call Center) • Regulatory compliance • Consumer card / Merchant activity Utilities Network / Process analytics for: • Grid monitoring / reliability studies • Preventive Maintenance • Power production monitoring / planning Retail Product analytics for: • Market Basket analytics • SKU trending • Competitive analyses • Context-aware buying • Social indicators of brand Healthcare Patient analytics for: • Cost of care reduction • Quality of care improvement • Claims optimization • Service provider consistency • Outcome diagnostics • Regulatory compliance
  7. 7. 9/10/2013 | 7 | ©2013 Ciber, Inc. Why Big Data? Insights from Analysis • Time college football products to win customers – WalmartLabs: social media buzz indicates when customers are getting excited about the upcoming season and their team(s). Combined with ShopyCat app provides targeted promos on team items. • Detecting nosocomial infections before they kill infants – Toronto hospital – Nosocomial infections can be life-threatening to premature infants if not treated quickly. Neonatal monitoring with real-time analytics can detect heart beat patterns that identify an infection before symptoms appear.
  8. 8. 9/10/2013 | 8 | ©2013 Ciber, Inc. Wal-Mart handles more than 1 million customer transactions every hour which import into databases containing more than 2.5 petabytes Volume Velocity Variety 1M/hour In addition to all procedure, claims and payment systems’ structured data add unstructured data in EMRs, patient monitoring devices, publications, drug structures, social network comments, carrier health sites, post-treatment care records… 80% Exist in the digital universe as of early 2013 1 zettabyte = 1,000 exabytes 1,000,000 petabytes 10^9 terabytes 10^12 gigabytes 2.7zettabytes What Drives Big Data Analytics
  9. 9. 9/10/2013 | 9 | ©2013 Ciber, Inc. EngineeringSocial/Mobile The Big Data Ecosystem Enterprise Systems Customer Loyalty & Service Systems Customer Case Files E-MailsAudioImagesProvisioning Systems Variety Veracity Velocity Volume Analysis Business Outcomes Predictive Analytics CEP Operational Control Simulation Social Analytics Digital Marketing WEB Analytics Blogs, Communities
  10. 10. 9/10/2013 | 10 | ©2013 Ciber, Inc. Hadoop and other options • A strategy for bringing together hardware and software • What choices are available and how do you choose the best option? • How do I govern it?
  11. 11. 9/10/2013 | 11 | ©2013 Ciber, Inc. Big Data Toolscape
  12. 12. 9/10/2013 | 12 | ©2013 Ciber, Inc. There are Many Use Cases for a Big Data Platform  Social Media - Product/brand Sentiment analysis  Brand strategy  Market analysis  RFID tracking & analysis  Transaction analysis to create insight- based product/service offerings  Multimodal surveillance  Cyber security  Fraud modeling & detection  Risk modeling & management  Regulatory reporting Innovate New Products at Speed and Scale Know Everything about your Customer  Social media customer sentiment analysis  Promotion optimization  Segmentation  Customer profitability  Click-stream analysis  CDR processing  Multi-channel interaction analysis  Loyalty program analytics  Churn prediction Run Zero Latency Operations  Smart Grid/meter management  Distribution load forecasting  Sales reporting  Inventory & merchandising optimization  Options trading  ICU patient monitoring  Disease surveillance  Transportation network optimization  Store performance  Environmental analysis  Experimental research Instant Awareness of Risk and Fraud Exploit Instrumented Assets  Network analytics  Asset management and predictive issue resolution  Website analytics  IT log analysis Back
  13. 13. 9/10/2013 | 13 | ©2013 Ciber, Inc. Processing and Archiving Strategies • Store forever • Selective storage • Throw away after processing
  14. 14. 9/10/2013 | 14 | ©2012 Ciber, Inc. Making Sense of it all
  15. 15. 9/10/2013 | 15 | ©2013 Ciber, Inc. Making sense of it all • Clarity of purpose • Definition of scope • Allocation of resources • Concrete result expectations • Comparative Analytical Measures (e.g. KPIs) – Rationalization of measures into actionable items and hierarchical groups – Defining predictive analytics workspaces ! ! !
  16. 16. 9/10/2013 | 16 | ©2013 Ciber, Inc. Role of the Data Scientist • Creating Intelligent Tagging • Selecting tools for analysis • Defining algorithms and data mining techniques
  17. 17. 9/10/2013 | 17 | ©2012 Ciber, Inc. A Framework of Understanding
  18. 18. 9/10/2013 | 18 | ©2013 Ciber, Inc. What is Contextualization ? • Context is the interrelated conditions in which something exists or occurs . Helping define context is Environment, Setting, Timeline, Genre • Why is context important? – Consistency needed in returned result sets – The context describes the internal or external “framework” – Internal contextual information is crucial – External contextual information is knowledge that which cannot be gotten from the text of the item itself – Time and resources are wasted in searching irrelevant and non-material information
  19. 19. 9/10/2013 | 19 | ©2013 Ciber, Inc. Problems in searching data • Voluminous • Ambiguous meanings • Inconsistent tagging • Multiple item types – text, formatted, PDF, TIFF, graphical, blogs, mashups • Knowledge of what is wanted is required to understand and return the proper result sets • Differentiation is necessary between – Real-time needs (e.g. fraud detection, medical Emergency room procedures) – Near-time needs (sometime in the near timeline) – Relaxed-time (some clearly defined future period)
  20. 20. 9/10/2013 | 20 | ©2013 Ciber, Inc. Topical information • Topical information is generally visible in the data stream –Keywords, data ranges, etc.
  21. 21. 9/10/2013 | 21 | ©2013 Ciber, Inc. Non-topical information • Has to be retrieved outside the item – Although topic is crucial to the relevance of an item, non-topical criteria plays an important role in the determination of relevance and significance – The identification and use of non-content (or “context”) descriptors is necessary – How widely agreed upon are the values of a given criterion among users (or user groups)?
  22. 22. 9/10/2013 | 22 | ©2013 Ciber, Inc. Non-topical information cont’d –What is the degree to which an attribute- value is “public” or “private”? • How useful is each criterion for the search tasks to be addressed by the specific query system? • How easily can a criterion be identified and assigned to an item? • What methods can be applied for refining and speeding retrievals?
  23. 23. 9/10/2013 | 23 | ©2013 Ciber, Inc. Descriptors - The defining of disambiguity • Do the content descriptors correspond or relate to non-topical relevance criteria of the system’s users? • Will users see a relationship between their relevance criteria and these descriptors, and use these descriptors in their search queries?
  24. 24. 9/10/2013 | 24 | ©2013 Ciber, Inc. Content descriptors • Content descriptors (topical relevance criteria) – “Public” knowledge: • People of similar cultural backgrounds would (more or less) agree on the meanings. However, context descriptors (which can function as non-topical relevance criteria) can vary widely in the degree to which their attribute-values are considered public or private.
  25. 25. 9/10/2013 | 25 | ©2013 Ciber, Inc. Public Knowledge Examples • “Has pictures” is a criterion that could be considered “public” as most people could agree on whether or not a document “has pictures”, if given a specific document to evaluate. • On the other hand, the criterion of “Regency Era” is highly situationally dependent - i.e. a limited subset of the public has knowledge of it - (specifically the period between 1811 and 1820, when King George III was deemed unfit to rule and his son - the Prince of Wales - ruled as his proxy as Prince Regent)
  26. 26. 9/10/2013 | 26 | ©2013 Ciber, Inc. Genres refine taxonomy • Genre is a “folk typology” • Item categories must enjoy widespread recognition by their intended user groups to qualify as genres. – Examples: Resumes, Ballet, Music, Chemical formulae, statistical results • Groups of people agree on and define Genres by mutual consent (Explicitly and Implicitly) – E.g. Taxonomies (plants, accounting, medical), laws, voting, polls • Genres give rise to sub-genres with increasing granularity – E.g. Music, classical, romantic, new age, atonal – Genres and sub-genres may contain common elements • E.g. classical music and romantic music may have an intersection of data points
  27. 27. 9/10/2013 | 27 | ©2013 Ciber, Inc. Genre knowledge • Genre is a type based on purpose, form and content. – E.g. The “resume” genre is for soliciting employment, divided into sections with contextual descriptors • Knowing a particular item’s genre also infers significant things about an item, sometimes enough to a make a judgment regarding the Item’s relevance to an information need – E.g. The phrase “Classically Trained Musician” infers knowledge to read music and understand musical terminology along with additional shades of musical knowledge
  28. 28. 9/10/2013 | 28 | ©2012 Ciber, Inc. Analytics
  29. 29. 9/10/2013 | 29 | ©2013 Ciber, Inc. Historical Analytics • Presentation of historical data – Dashboards, Drill-downs, interactive reports, static reports – New methods and devices – Identifying the metrics that affect key objectives – Synchronizing those metrics through an organization – Creating user tools to show effects of good (and bad) choices – Tying the financial, operational, and sales worlds together – Analyzing to predict the future – Refining models for accuracy
  30. 30. 9/10/2013 | 30 | ©2013 Ciber, Inc. Predictive Analytics • Manipulation of data – Dashboards, Drill-downs, interactive reports – New methods and devices – Varying the metrics that affect key objectives – Synchronizing the impact of metrics through an organization – Creating user tools to show effects of good (and bad) choices – Tying the financial, operational, and sales worlds together – Creating models that show potential future scenarios – Refining models for accuracy using advanced tools and statistics
  31. 31. 9/10/2013 | 31 | ©2012 Ciber, Inc. Examples
  32. 32. 9/10/2013 | 32 | ©2013 Ciber, Inc. Examples of Harnessing Data Resources Retailer reduces time to run queries by 80% to optimize inventory Stock Exchange cuts queries from 26 hours to 2 minutes on 2 PB Government cuts acoustic analysis from hours to 70 Milliseconds Utility avoids power failures by analyzing 10 PB of data in minutes Telco analyses streaming network data to reduce hardware costs by 90% Hospital analyses streaming vitals to detect illness 24 hours earlier Big data challenges exist in every organization today
  33. 33. 9/10/2013 | 33 | ©2013 Ciber, Inc. In Order to Realize New Opportunities, You Need to Think Beyond Traditional Sources of Data Transactional and Application Data Machine Data Social Data  Volume  Structured  Throughput  Velocity  Semi-structured  Ingestion  Variety  Highly unstructured  Veracity Enterprise Content  Variety  Highly unstructured  Volume
  34. 34. 9/10/2013 | 34 | ©2013 Ciber, Inc. • Data at rest – oceans • Collection of what has streamed • Web logs, emails, social media • Unstructured documents: forms, claims • Structured data from disparate systems • Data in movement - streams • Twitter / Facebook comments • Stock market data • Sensors: Vital signs of a newly-born Two Sample Types of Big Data
  35. 35. 9/10/2013 | 35 | ©2012 Ciber, Inc. Getting there
  36. 36. 9/10/2013 | 36 | ©2013 Ciber, Inc. Leveraging Big Data Requires Multiple Platform Capabilities Manage & store huge volume of any data Hadoop File System MapReduce Manage streaming data Stream Computing Analyze unstructured data Text Analytics Engine Data WarehousingStructure and control data Integrate and govern all data sources Integration, Data Quality, Security, Lifecycle Management, MDM Understand and navigate federated big data sources Federated Discovery and Navigation
  37. 37. 9/10/2013 | 37 | ©2013 Ciber, Inc. Outcomes Utilizing Big Data Capabilities To Analyze Any Big Data Type With Unique CapabilitiesAchieve Breakthrough Outcomes Content Transactional / Application Data Machine Data Social Media Data Visualization and Discovery Know Everything About Your Customers Run Zero-latency Operations Innovate new products at Speed and Scale Instant Awareness of Fraud and Risk Exploit Instrumented Assets Hadoop Data Warehousing Stream Computing Integration and Governance Text Analytics
  38. 38. 9/10/2013 | 38 | ©2013 Ciber, Inc. Big Data Platform and Entry Points 2 – Analyze Raw Rata 5 – Analyze Streaming Data 1 – Unlock Big Data 3 – Simplify your warehouse 4 – Reduce costs with Hadoop
  39. 39. 9/10/2013 | 39 | ©2013 Ciber, Inc. Q & A Contact Richard Gristak, Senior Director of Business Intelligence – rgristak@ciber.com
  40. 40. 9/10/2013 | 40 | ©2012 Ciber, Inc. Thank you

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