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Information 3.0 - Data + Technology + People

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Presented by Charlie Vanek

Presented by Charlie Vanek

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  • Note the three themes at the forum: Data, Technology & People are covered here.
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  • But can we say “in the big,” as a former colleague used to put it, will investments in IT capital deepening along with managerial innovation, has lead to gains in productivity for businesses. The question is, will investments in Big Data produce similar productivity gains.
  • What is that “Other Contribution?”
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  • History at TR Over 24 years of technology experience (started as programmer)Held many leadership positions across TR (Application development, data center, Shared Platforms, BU CTOs)Current Role as CTO Content Marketplace
  • Content marketplace is helping our business make sense of the content it owns, understand ever expanding attributes about that content, and understand and build relationships between content. Its improving the customer experience for our users as well as helping us identify new product opportunities. Let’s talk about how we are doing that.
  • CLEAR is an investigative platform designed for professionals who need information about individuals and companies. With fast access to a vast collection of public and proprietary records, CLEAR helps you find out about people and their connections.Marriage license: 2 names, common addressNews article: Indicates Company and phone number and mentions someone who works thereBoat license: owner and boat IDCar Lease: Owner and make, model, serial number of car
  • A crime happens and someone remembers a CA license plate but only remembers the FOR part of the number. We look up the FOR in CA and find many hits but only a few in the bay area. We find not only the current owner of the car but the previous owner. The previous owner had several run-ins with the law so we look up his place of business, his current home address, and even his relatives.
  • Most organizations have a disjointed information infrastructure – disparate capabilities deployed in a project- or application-specific manner, with little consistency. The challenges emerging from the growth of ‘big data’ as well as business demand for more flexible, timely and well governed information in support of new use cases, is causing existing approaches to information architecture to break. Organizations that establish a road map toward a cohesive, application-independent and information source-independent set of information management technology capabilities are best positioned to support long-term enterprise information management (EIM) goals. Content marketplace is our approach to this challenging problem.Cannot approach as an integration of all data into a warehouse. Need a federated model.Internal use case: Who’s Who in China. Utilizing the people and company information we have gathered across the organization and building a UI for journalists to find information to enhance their stories. Realizing the transformational value of new products and services via big data and content marketplace.Focused on high priority entities (relating to Political handover of power in China)Major State owned companies and political organizations End user interface for journalists lead generation
  • Information architecture at its simplest is structuring, labeling, and organizing information. This is a critical role in Content Marketplace and a role that hasn’t appeared (officially) in any technology group I’ve worked with in the past 20+ years. Content Marketplace has a group of about 8 now.IA has shown itself most often in web design. IA presented unexplored terrain as the Web and related technologies were beginning to explode (30B pieces of content shared each month, some estimates indicate doubling humanity’s total output every 5 years). Someone was going to have to organize all that content. But IA is not just confided to the web. Consider the information architecture of something familiar to us, the book with its tables of contents, indices, pagination, chapters, and the details on the spine and cover. The enterprise setting is typically a large information space made up of disjointed silos controlled by different business units. Users are confronted by a counter-intuitive architecture that closely resembles the enterprise's org chart. Politics, culture, geography, technology, and other interesting constraints complicate this particular context. Enterprise IA is a unique IA genre, as are the architectures for ecommerce sites or entertainment sites. So we can prioritize and often proscribe which aspects of an IA will succeed in this particular genre and which won't. For example, for obvious reasons it's difficult to manually tag all documents using controlled vocabulary terms in an enterprise environment. It's easier to implement site-wide search, which doesn't necessarily require bothering content authors or manually touching their content. We can look at the entire IA palette and make some reasonable suggestions as to what will work in an enterprise environment, what won't, and when. A well-designed information architecture maps the needs of users to available content, all against the backdrop of a specific business context. Must take a balanced approach with focus on users and the processes that serve them.
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  • CLEAR is an investigative platform designed for professionals who need information about individuals and companies. With fast access to a vast collection of public and proprietary records, CLEAR helps you find out about people and their connections.Marriage license: 2 names, common addressNews article: Indicates Company and phone number and mentions someone who works thereBoat license: owner and boat IDCar Lease: Owner and make, model, serial number of car
  • A crime happens and someone remembers a CA license plate but only remembers the FOR part of the number. We look up the FOR in CA and find many hits but only a few in the bay area. We find not only the current owner of the car but the previous owner. The previous owner had several run-ins with the law so we look up his place of business, his current home address, and even his relatives.
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  • These items are no particular order but will need to be addressed in varying levels to be successful.As larger amounts of data become digitized and travel across organization boundaries, privacy, security, intellectual property, and even liability policies become increasingly important.Building a technology stack to handle big data and enabling a phased approach for legacy systems to participate must be planned for.Skills and talents of employees need to be considered.Access to data may need to broaden.The industry structure can indicate how easy or difficult the transformational value will be to obtain.Let’s start with recommendations for issues around Data Policies.
  • Our CLEAR example showed an example of big data that is at the high end of security and policy concern. Public records content contains information that is highly regulated, with policy demands that differ by state and country. Thomson Reuters has several teams focused on careful management of this personally identifiable information. There are three main areas of focus, Employee misuse, customer misuse, and external compromise.
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  • Making use of large digital data sets will require the assembly of a technology stack from storage and computing through analytical and visual software applications. (Use exadata and file distribution as an example). Think of capabilities in terms of publishers and consumers of content. What do they need.Legacy systems need to be taken into account. (Use WCA ID v OA ID as example). This is where information architecture becomes critical. Utilize information architecture to identify patterns, define key terms, establish policy, build subject area map (high level categorization of types of info available in the enterprise), entity maps, information flow diagrams, etc. All of this development must consider the end customer’s needs. Information valuation will help prioritize how to approach. (Use Org Auth example)
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  • Organizational leaders need to understand that big data can unlock value – and how to use it to that effect.The US alone, by 2018, could face a shortage of 140k – 190k people with deep analytical skills as well as 1.5M managers and analysts to analyze big data and make decisions based on their findings, that’s 50-60% greater than the projected supply.
  • Technologies are available to combine web analytics, subscriber analytics, social analytics, and sentiment data fro multiple sources to build predictive models and determine more relevant ad placement and content matching. Skill sets for data scientists’ statistical, modeling, and processing skills are scarce.
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  • The relative ease, or difficulty, of capturing value from big data will sometimes depend on the structure of a particular sector or industry. Lack of competitive intensity and performance transparency likely to slow fully leveraging the benefits of big data.
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  • Transcript

    • 1. Information 3.0: The Future of Data Management
      Lisa Schlosser, CTO Thomson Reuters Content Marketplace
      & Charlie Vanek, Sr. Director, Product Marketing, Hubbard One
    • 2. The Future of Data Management
      Introduction
      Data: Big Data Drives Transformational Value
      Technology: Case Studies in Big Data
      Lisa Schlosser
      Charlie Vanek
      People: Collaboration between CTO & CMO
      Recommendations to overcome Impediments to Transformational Value
      2
    • 3. Big Data
      Big Data: datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze
      3
    • 4. Big Data
      4
    • 5. expectations
      time
      Peak of
      Inflated Expectations
      Plateau of Productivity
      Technology Trigger
      Trough of Disillusionment
      Slope of Enlightenment
      Years to mainstream adoption:
      obsolete
      before plateau
      less than 2 years
      2 to 5 years
      5 to 10 years
      more than 10 years
      Internet TV
      NFC Payment
      Activity Streams
      Private Cloud Computing
      Wireless Power
      Social Analytics
      Augmented Reality
      Group Buying
      Cloud Computing
      Gamification
      Media Tablet
      3D Printing
      Virtual Assistants
      Image Recognition
      In-Memory Database Management Systems
      Context-Enriched Services
      Gesture Recognition
      Speech-to-Speech Translation
      Machine-to-Machine Communication Services
      Internet of Things
      Natural Language Question Answering
      Mesh Networks: Sensor
      Mobile Robots
      Location-Aware Applications
      "Big Data" and Extreme Information Processing and Management
      Speech Recognition
      Predictive Analytics
      Social TV
      Cloud/Web Platforms
      "Big Data" and Extreme Information Processing and Management
      Mobile Application Stores
      Video Analytics for Customer Service
      Biometric Authentication Methods
      Computer-Brain Interface
      Hosted Virtual Desktops
      Idea Management
      Quantum Computing
      QR/Color Code
      Virtual Worlds
      Human Augmentation
      Consumerization
      3D Bioprinting
      E-Book Readers
      As of July 2011
      Big Data
      5
    • 6. Big Data
      6
    • 7. Big Data
      7
    • 8. Big Data
      8
    • 9. Big Data
      Big Data: are there instances where similar investments yielded outsized results?
      9
    • 10. Big Data
      10
    • 11. Big Data
      Tailored to business, KPIs
      Deployed sequentially, building capabilities over time
      IT Investment evolved simultaneously with managerial innovation
      11
    • 12. Big Data
      Big Data: will it produce sector-wide productivity gains like it did for Big Iron?
      12
    • 13. Big Data: Transformational Value
      13
    • 14. The Future of Data Management
      Introduction
      Data: Big Data Drives Transformational Value
      Technology: Case Studies in Big Data
      Lisa Schlosser
      Charlie Vanek
      People: Collaboration between CTO & CMO
      Recommendations to overcome Impediments to Transformational Value
      14
    • 15. Technology
      Leadership
      Software Development
      15
    • 16. CONTENTMARKETPLACE
      is an information architecture
      –a set of common standardsandpolicies
      for the way we create, consume, describe,
      manageanddistributeourcontent –
      that enables content interoperability
      16
      16
    • 17. Technology
      Content Marketplace enabling content interoperability across Thomson Reuters – People Data
      Content Marketplace is helping us innovate, locate, and understand our content Thomson Reuters-wide
      17
    • 18. Technology: CLEAR Product Example
      Documents
      Entity Extraction
      Relationships and
      Attributes
      People Warehouse
      People
      Authority
      R
      Company
      Warehouse
      R
      Company Authority
      18
    • 19. Technology: Entities, attributes and relationships in action
      R
      19
    • 20. Technology: Content Marketplace
      Content Marketplace is enabling content interoperability across Thomson Reuters. Big data will be solved through a combination of enhancements to the people, processes and technology strengths of an enterprise.
      Financial
      Science
      Officers and Directors
      Researchers
      Legal
      Who’s Who In China
      Media
      Attorneys
      Tax & Acct
      Journalists
      Accountants
      20
    • 21. Technology: Content Marketplace Skills
      21
      Information Architecture
    • 22. The Future of Data Management
      Introduction
      Data: Big Data Drives Transformational Value
      Technology: Case Studies in Big Data
      Lisa Schlosser
      Charlie Vanek
      People: Collaboration between CTO & CMO
      Recommendations to overcome Impediments to Transformational Value
      22
    • 23. Technology: CLEAR Product Example
      Documents
      Entity Extraction
      Relationships and
      Attributes
      People Warehouse
      People
      Authority
      R
      Company
      Warehouse
      R
      Company Authority
      23
      !
    • 24. Technology: Business of Law Example
    • 25. Technology: Business of Law Example
    • 26. Technology: Business of Law Example
      Hon. R. Jones: Judge, 9th Circuit
      Sam Carson, Orrick, Herrington
      Plaintiff
      Client: Intel
      Mary Vasaly, Maslon Edelman
      Defense
      Client: AMD
    • 27. Technology: Business of Law Example
    • 28. Technology: Business of Law Example
      “Things we’re interested in”
      Relationships and
      Profiling Information
      Data Sources
      Documents
      Entity Extraction
      Relationships and
      Attributes
      People Warehouse
      ERM
      People
      Authority
      EM
      R
      T & B
      Company
      Warehouse
      R
      Company Authority
      Third-party data
      28
    • 29. Technology: Business of Law Example
      Placeholder for Intranet Portal screen shot
    • 30. Technology: Business of Law Example
      The Marketing View: Segmentation
      R
      30
      !
    • 31. 31
      Technology: Business of Law Example
      Answers
      Questions
      ERM
      With a relationship strength index of Z
      EM
      With whom we’ve done specific corporate work
      T & B
      Top 25 Companies for Practice Area X by Billing
      Third-party data
      With YoY revenues of +5%
    • 38. Technology: Business of Law Example
      32
    • 39. Technology: Transformational Value
      33
    • 40. The Future of Data Management
      Introduction
      Data: Big Data Drives Transformational Value
      Technology: Case Studies in Big Data
      Lisa Schlosser
      Charlie Vanek
      People: Collaboration between CTO & CMO
      Recommendations to overcome Impediments to Transformational Value
      34
    • 41. People
      35
      CMO
      CIO
      53%
      55%
      65%
      50%
      40%
      44%
    • 42. People: CMO – CTO Collaboration
      36
      CMO
      CIO
      69%
      51%
      46%
      47%
      26%
      21%
      24%
      21%
      19%
      58%
    • 43. People: CMO – CTO Collaboration
      37
      CMO
      CIO
    • 44. People: CMO – CTO Collaboration
      38
      CMO
      CIO
      46%
      18%
      44%
      38%
      41%
      30%
      36%
      46%
      36%
      13%
      39%
    • 45. People: CMO – CTO Collaboration
      39
      CMO
      CIO
    • 46. The Future of Data Management
      Introduction
      Data: Big Data Drives Transformational Value
      Technology: Case Studies in Big Data
      Lisa Schlosser
      Charlie Vanek
      People: Collaboration between CTO & CMO
      Recommendations to overcome Impediments to Transformational Value
      40
    • 47. Transformational Value
      41
    • 48. Impediments to Big Data Transformational Value
      42
    • 49. Recommendations for Data Policies and Security
      43
      Enhanced security and compliance will mitigate risks of loss, theft, misuse and breach of sensitive content. Public records content, as used with CLEAR, is at the high end of protected content.
      RiskMitigation
      • Employee credentialing and background checks
      • 50. Management of employee access
      • 51. Encrypted logging and log monitoring
      • 52. Training
      Employee Misuse
      Customer Misuse
      • Customer credentialing and enhanced due diligence
      • 53. Password enhancement
      • 54. Restrictions for IP, domestic and foreign
      • 55. Monitoring for usage anomalies and VIP searching
      • 56. High security network zone with monitoring and intrusion detection
      • 57. Security audits
      • 58. Enhanced configuration management
      External system or password compromise
    • 59. Impediments to Big Data Transformational Value
      44
    • 60. Recommendations for Infrastructure
      A strong information infrastructure assumes a phased implementation approach that utilizes legacy systems, designs for scale, and prioritizes by information valuation
      Recognize that different project team members use different application, formats, and standards to exchange information. Look for common ways to normalize and extract meaning from all types of content to that it can be exchanged across the organization
      Assess current range of information management infrastructure capabilities – identifying gaps (missing capabilities) and overlap/redundancies (multiple approaches to a capability, and/or multiple technologies supporting it)
      Use existing systems and designs as starting points to develop common models that can then be shared by different processing components and system entities
      Identify an initial set of information management “common capabilities” and begin to leverage these in support of in-demand cases
      45
      Information Valuation - Identify information that matters most
    • 61. Impediments to Big Data Transformational Value
      46
    • 62. Recommendations for Organizational Change and Talent
      47
    • 63. Recommendations for Organizational Change and Talent
      48
      From Mad Men…
      To Math Men!
    • 64. Impediments to Big Data Transformational Value
      49
    • 65. Recommendations for Access to Data
      50
    • 66. Recommendations for Access to Data
      51
    • 67. Impediments to Big Data Transformational Value
      52
    • 68. CMO – CTO Collaboration
      53
      CMO
      CIO
      46%
      18%
      44%
      38%
      41%
      30%
      36%
      46%
      36%
      13%
      39%
    • 69. Example of Big Data Transformational Value
      Exhibit identifies the different ways in which data is used and structured by an organization, and the competitive advantage that organizations achieve
      Each of these functions addresses a range of questions about an organization’s business processes. Analytics provides a higher level and proactive solution to these questions.
      Source: IBM
      54
    • 70. Example of Big Data Transformational Value
      Established science to lead generation– drive highly targeted leads based on customer cues indicating the right time to pursue a sales
      Behavior (Trigger Events)
      Propensity
      Eligibility
      Propensity
      Eligibility
      Usage in Defined Content Sets:
      Behavioral Metrics:
      • Ancillary Usage
      • 71. Warning Screen Declines
      • 72. Free Trial Usage
      • 73. New Print Purchases
      • 74. Docket Appearances
      Usage Spikes:
      Geography
      Print Purchases
      Firm Size
      Statistical Modeling:
      Docket Appearance:
      Renewal Eligibility
      WestPack Runway
      Jury Verdicts:
      Significant Print Spend
      Estimated Practice Areas:
      • Estimated Practice Area (EPA)
      • 78. Bankruptcy Fillings
      • 79. IP – Patent / Trademark Filings
      Password Increases:
      Future Price Increases
      55
    • 80. Technology: Business of Law Example
      “Things we’re interested in”
      Relationships and
      Profiling Information
      Data Sources
      Customer Warehouse
      CRM
      People
      Authority
      Accounting System
      56
      User Behavior on WL
      Third-party data
    • 81. Example of Big Data Transformational Value
      Percent of field reps that tend to Agree or Strongly Agree with these statements . . . .
      Results:
      Thanks to SMART Leads, leads are more accurate/effective
      80%
      • Field resources have very positive opinions and recognize the improvement in lead quality (80% agree vs. 0% disagree)
      • 82. Reps achieved 105% of quota and grew sales 116% over prior year
      SMART Leads has increased my number of potential sales for existing accounts
      80%
      Using SMART Leads saves me time compared to my previous process
      67%
      SMART Leads has increased my number of new prospects
      60%
      Thanks to SMART Leads, I am more effective at converting leads
      53%
      Thanks to SMART Leads, I can sell more in less time
      47%
      SMART Leads has prompted me to pitch products that I wouldn’t have otherwise considered
      33%
      57
    • 83. Questions
      58
      Thank You!
      Charles.vanek@thomsonreuters.com
      Lisa.Schlosser@thomsonreuters.com