Information 3.0 - Data + Technology + People
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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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Information 3.0 - Data + Technology + People Information 3.0 - Data + Technology + People Presentation Transcript

  • Information 3.0: The Future of Data Management
    Lisa Schlosser, CTO Thomson Reuters Content Marketplace
    & Charlie Vanek, Sr. Director, Product Marketing, Hubbard One
  • 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
  • Big Data
    Big Data: datasets whose size is beyond the ability of typical software tools to capture, store, manage and analyze
    3
  • Big Data
    4
  • 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
  • Big Data
    6
  • Big Data
    7
  • Big Data
    8
  • Big Data
    Big Data: are there instances where similar investments yielded outsized results?
    9
  • Big Data
    10
  • Big Data
    Tailored to business, KPIs
    Deployed sequentially, building capabilities over time
    IT Investment evolved simultaneously with managerial innovation
    11
  • Big Data
    Big Data: will it produce sector-wide productivity gains like it did for Big Iron?
    12
  • Big Data: Transformational Value
    13
  • 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
  • Technology
    Leadership
    Software Development
    15
  • CONTENTMARKETPLACE
    is an information architecture
    –a set of common standardsandpolicies
    for the way we create, consume, describe,
    manageanddistributeourcontent –
    that enables content interoperability
    16
    16
  • 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
  • Technology: CLEAR Product Example
    Documents
    Entity Extraction
    Relationships and
    Attributes
    People Warehouse
    People
    Authority
    R
    Company
    Warehouse
    R
    Company Authority
    18
  • Technology: Entities, attributes and relationships in action
    R
    19
  • 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
  • Technology: Content Marketplace Skills
    21
    Information Architecture
  • 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
  • Technology: CLEAR Product Example
    Documents
    Entity Extraction
    Relationships and
    Attributes
    People Warehouse
    People
    Authority
    R
    Company
    Warehouse
    R
    Company Authority
    23
    !
  • Technology: Business of Law Example
  • Technology: Business of Law Example
  • 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
  • Technology: Business of Law Example
  • 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
  • Technology: Business of Law Example
    Placeholder for Intranet Portal screen shot
  • Technology: Business of Law Example
    The Marketing View: Segmentation
    R
    30
    !
  • 31
    Technology: Business of Law Example
    Answers
    Questions
    • Company Name
    • GC
    • Revenue/Turnover
    • Billing
    • Attorneys with Strongest relationships
    • Contacts at the Company
    • Telephone #’s
    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%
  • Technology: Business of Law Example
    32
  • Technology: Transformational Value
    33
  • 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
  • People
    35
    CMO
    CIO
    53%
    55%
    65%
    50%
    40%
    44%
  • People: CMO – CTO Collaboration
    36
    CMO
    CIO
    69%
    51%
    46%
    47%
    26%
    21%
    24%
    21%
    19%
    58%
  • People: CMO – CTO Collaboration
    37
    CMO
    CIO
  • People: CMO – CTO Collaboration
    38
    CMO
    CIO
    46%
    18%
    44%
    38%
    41%
    30%
    36%
    46%
    36%
    13%
    39%
  • People: CMO – CTO Collaboration
    39
    CMO
    CIO
  • 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
  • Transformational Value
    41
  • Impediments to Big Data Transformational Value
    42
  • 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
    • Management of employee access
    • Encrypted logging and log monitoring
    • Training
    Employee Misuse
    Customer Misuse
    • Customer credentialing and enhanced due diligence
    • Password enhancement
    • Restrictions for IP, domestic and foreign
    • Monitoring for usage anomalies and VIP searching
    • High security network zone with monitoring and intrusion detection
    • Security audits
    • Enhanced configuration management
    External system or password compromise
  • Impediments to Big Data Transformational Value
    44
  • 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
  • Impediments to Big Data Transformational Value
    46
  • Recommendations for Organizational Change and Talent
    47
  • Recommendations for Organizational Change and Talent
    48
    From Mad Men…
    To Math Men!
  • Impediments to Big Data Transformational Value
    49
  • Recommendations for Access to Data
    50
  • Recommendations for Access to Data
    51
  • Impediments to Big Data Transformational Value
    52
  • CMO – CTO Collaboration
    53
    CMO
    CIO
    46%
    18%
    44%
    38%
    41%
    30%
    36%
    46%
    36%
    13%
    39%
  • 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
  • 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
    • Warning Screen Declines
    • Free Trial Usage
    • New Print Purchases
    • Docket Appearances
    Usage Spikes:
    Geography
    Print Purchases
    Firm Size
    Statistical Modeling:
    • Up-sell Scores
    • Case Notebook
    • People Map
    • ProDoc
    Docket Appearance:
    Renewal Eligibility
    WestPack Runway
    Jury Verdicts:
    Significant Print Spend
    Estimated Practice Areas:
    • Estimated Practice Area (EPA)
    • Bankruptcy Fillings
    • IP – Patent / Trademark Filings
    Password Increases:
    Future Price Increases
    55
  • 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
  • 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)
    • 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
  • Questions
    58
    Thank You!
    Charles.vanek@thomsonreuters.com
    Lisa.Schlosser@thomsonreuters.com