information 3.0 - data + technology + people

Information 3.0: The Future of Data ManagementLisa 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

Technology Trigger

Peak ofInflated Expectations

Trough of DisillusionmentSlope of Enlightenment Plateau of

Productivity

time

expectations

Years to mainstream adoption:

less than 2 years 2 to 5 years 5 to 10 years more than 10 yearsobsoletebefore plateau

As of July 2011

Human Augmentation

Quantum Computing

3D Bioprinting

Computer-Brain Interface

Video Analytics for Customer Service

Social TV

"Big Data" and Extreme Information Processing and Management

Mobile RobotsNatural Language Question Answering

Speech-to-Speech Translation

Context-Enriched Services

3D PrintingGamification

Group BuyingSocial Analytics

Wireless Power

Internet TVNFC PaymentPrivate Cloud Computing

Augmented RealityCloud ComputingMedia Tablet

Virtual AssistantsImage Recognition

Cloud/Web Platforms

Hosted Virtual Desktops

E-Book Readers

ConsumerizationQR/Color Code

Idea Management

Location-Aware Applications

Predictive Analytics

Speech Recognition

Internet of Things

Activity Streams

In-Memory Database Management Systems

Gesture Recognition

Mesh Networks: Sensor

Machine-to-Machine Communication Services

Virtual Worlds

Biometric Authentication MethodsMobile Application Stores

Big Data

5

"Big Data" and Extreme Information Processing and Management

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


• Introduction



– Charlie Vanek



14

Technology

Software Development

Data Center

Shared Platforms

Business UnitsContent Marketplace

Software Development

LEADERSHIP

15

16

CONTENT MARKETPLACE

is an information architecture– a set of common standards and policies

for the way we create, consume, describe,

manage and distribute our content –

that enables content interoperability

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

PeopleAuthority

Technology: CLEAR Product Example

Documents Entity Extraction

Company Authority

People Warehouse

CompanyWarehouse

Relationships and Attributes

R

R

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.

Officers and Directors

Attorneys

FINANCIAL

LEGAL

TAX & ACCT

SCIENCE

MEDIA

Accountants

Journalists

Researchers

20

Who’s Who In China

Technology: Content Marketplace Skills

21

INFORMATION ARCHITECTURE


• Introduction



– Charlie Vanek



22

PeopleAuthority

Technology: CLEAR Product Example

Documents Entity Extraction

Company Authority

People Warehouse

CompanyWarehouse

Relationships and Attributes

R

R

23

!

Technology: Business of Law Example

Mary Vasaly, Maslon Edelman

Defense

Client: AMD

Hon. R. Jones: Judge, 9th Circuit

Sam Carson, Orrick, Herrington

Plaintiff

Client: Intel


PeopleAuthority

Data Sources“Things we’re interested in”

Company Authority

People Warehouse

CompanyWarehouse

Relationships and Profiling Information

R

R

28

Documents Entity Extraction Relationships and Attributes

ERM

EM

Third-party data

T & B



Placeholder for Intranet Portal screen shot


R

30

The Marketing View: Segmentation

!

31

ERM

EM

Third-party data

T & B


Top 25 Companies for Practice Area X by Billing

With a relationship strength index of Z

With YoY revenues of +5%

With whom we’ve done specific corporate work

• Company Name

• GC

• Revenue/Turnover

• Billing

• Attorneys with Strongest relationships

• Contacts at the Company

• Telephone #’s

Questions Answers


32

Technology: Transformational Value

33


• Introduction



– Charlie Vanek



34

People

35

CMO CIO

53%

65%

40%

55%

50%

44%

People: CMO – CTO Collaboration

36

CMO CIO

69%

46%

26%

19%

24%

51%

47%

21%

58%

21%


37

CMO CIO


38

CMO CIO

46%

44%

41%

36%

36%

18%

38%

30%

13%

46%

39%


39

CMO CIO


• Introduction



– Charlie Vanek



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.

• Employee credentialing and background checks• Management of employee access• Encrypted logging and log monitoring• Training

• High security network zone with monitoring and intrusion detection

• Security audits• Enhanced configuration management

Customer Misuse

External system or password compromise

Employee Misuse

• Customer credentialing and enhanced due diligence• Password enhancement• Restrictions for IP, domestic and foreign• Monitoring for usage anomalies and VIP searching

Risk Mitigation


44

Recommendations for Infrastructure

• 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

A strong information infrastructure assumes a phased implementation approach that utilizes legacy systems, designs for scale, and prioritizes by

information valuation

45

Information Valuation - Identify information that matters most


46

Recommendations for Organizational Change and Talent

47

Recommendations for Organizational Change and Talent

48

FROM MAD MEN…

TO MATH MEN!


49

Recommendations for Access to Data

50

Recommendations for Access to Data

51


52

CMO – CTO Collaboration

53

CMO CIO

46%

44%

41%

36%

36%

18%

38%

30%

13%

46%

39%

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.

Com

petit

ive

Adva

ntag

e

Business Intelligence

OPTIMIZATION

PREDICTIVE MODELING

FORECASTING AND PLANNING

STATISTICAL ANALYSIS

THRESHOLD ALERTS

PRE-DEFINED DRILLING

AD-HOC REPORTING

PRE-DEFINED REPORTING

LARGE DATA EXTRACTS

DRILL ANWHERE

HIERARCHIES DATA MAPPING

DECISION TREE ANALYSIS

DATA MINING

INFO

RMAT

ION

DELI

VERY

What is the best way to improve, what else can we do?

What will happen next, how will it happen?

What will happen if these trends continue?

Why is this happening?

What actions are needed?

What exactly is the problem?

How many, how often, who, when, where?

What happened?

What data has been captured?

What else is causing this problem?

Where exactly is this happening?

What will this lead to?

What else is affected by this?

Exhibit identifies the different ways in which data is used and structured by an organization, and the competitive advantage that organizations achieve

Source: IBM 54

Example of Big Data Transformational Value

55

Established science to lead generation– drive highly targeted leads based on customer cues indicating the right time to pursue a sales

Eligibility Propensity

Behavioral Metrics:• Ancillary Usage• Warning Screen Declines• Free Trial Usage• New Print Purchases• Docket Appearances

Propensity

Statistical Modeling:• Up-sell Scores• Case Notebook• People Map• ProDoc

Estimated Practice Areas:

• Estimated Practice Area (EPA)• Bankruptcy Fillings• IP – Patent / Trademark Filings

Usage in Defined Content Sets:

Usage Spikes:

Print Purchases

Docket Appearance:

Jury Verdicts:

Behavior (Trigger Events)

Future Price Increases

Password Increases:

Eligibility

Geography

Firm Size

Renewal Eligibility

WestPack Runway

Significant Print Spend


PeopleAuthority

Data Sources“Things we’re interested in”

Customer Warehouse

Relationships and Profiling Information

56

CRM

Accounting System

Third-party data

User Behavior

on WL


57

• 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

Thanks to SMART Leads, leads are more accurate/effective

80%

80%

Using SMART Leads saves me time compared to my previous process

67%

SMART Leads has increased my number of new prospects

60%

SMART Leads has prompted me to pitch products that I wouldn’t have otherwise considered

33%

Thanks to SMART Leads, I am more effective at converting leads 53%

Percent of field reps that tend to Agree or Strongly Agree with these statements . . . .

Thanks to SMART Leads, I can sell more in less time 47%

Results:


Questions

58

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