Monetizing data - An Evening with Eight of Chicago's Data Product Management Leaders

Product Development Management Association

Monetizing Big Data:
An Evening with Eight of Chicago’s
Data Product Management Leaders

March 19, 2013
Pazzo’s at 311 S. Wacker

Randy Horton
Managing Principal, 94 Westbound Consulting

Product Development and Management Association

1. High-level overview of the data
product management lifecycle.
– “I’m thinking about creating a data product.
What are some key concepts and considerations
that I should understand?”
2. Intro to the breadth/depth of
Chicago’s data product management
firms and talent
3. Great networking
4. Fun (including t-shirt prizes!)

•
1. What's a big data product and how does it differ from
“traditional” digital and physical products?
2. Designing a data product to fit a real need? (Identifying
needs, segmenting, knowing customer requirements)
3. Getting your data, Part 1: How to source existing databases?
4. Getting your data, Part 2: How to manufacture new
data? (Gathering, housing, analytics, structuring)
5. Legal and ethical constraints of data products: regulatory
compliance, privacy and corporate trade secrets
6. Packaging your data and pricing it
7. Successfully Marketing and Selling Your Data
8. Winning elements of a big data product team

DESIGNING A DATA PRODUCT
TO FIT A REAL NEED

Kamal Tahir, Experian

Identifying needs , Segmenting, Knowing
Customer Requirements

Using data, technology, analytics and strategy, I help drive profit, volume & share
across digital, social and traditional channels by improving acquisition, conversion,
retention and engagement

• 500 million vehicles • 235 million consumers • Global commercialization
• Registration, accident, • 113 million households of Nielsen Answers BI
emissions, odometer • Behavioral, attitudinal platform
• States, dealers, OEMs, • 3K+ elements • Global lead for data and
insurance, auction • Plus Web search data analytical asset delivery
• Sales performance • Automated profiling and platform $1.5B, 35K users,
• Predictive purchase targeting solutions 33 countries, 12 languages
models • Digital effectiveness

• EDI based volume data for
• First Global data
500+ national
solution for
agricultural pesticides
environmental
wholesalers to drive
compliance
marketing plans
• Product-assembly-
component-base material

22
22

THE KEYS – OCDix™

Owners Inspire Value to you
Capability Delivery
Objectives Improvise Value to
Competence Devices
Implement user
Outcomes Capacity Data
Value <> $

23

Put data in context of needs to build a roadmap to
solution
who What
• is the audience? • is the need?
More than one? • problems to be solved?
• will you design for? • decisions to be made?
• will you not design • questions to be
for? answered
• other questions may
come up

HOW CAN I HELP

24

How will it be used

User Type
• Internal or Usage Style
external • Summary rollups
• Tech vs. non tech • Alerts and signals
• Onsite/Remote/ • Ad-hoc analysis
Mobile
• Interactive

Delivery & Devices
• Website Usage Type
• FTP • Single use
• Integrations • Subscription
• Tapes (yes) • Ad-hoc
• Tablet, phone, CAN I HELP YOU
custom devices

25

Success -Ability to solve, deliver, use - for You & user

YOU User

Competency & Competency-
Competition
core competency for
Can they use the new
information
you?

Capability & Capacity- Capability & Capacity
Can you address it? –
What else is on your
How soon will user
start using it
plate?
Are other pieces to
Can you deliver if it is execute available?
built? CAN IT BE BUILT?
SHOULD I Build it Complexity &
Complexity & Constraints
Constraints-
size, usage, frequency,
reliability, How much advisory &
ROI consulting needed
regulatory?
Opportunity Cost

26

Don't get high on your
own supply

27

Big data for big challenges?

Big, small, medium, Solve incremental
petite, grande, venti, issues along the way
Big and tall..look for quicker ROI
beyond the label
Fund future
Big problems = big initiatives and get
investment + evolutionary gains
complexity & along the way to
constraints = revolutionary gains
longer duration for
ROI.

28

SUMMARY- building a wining product

• Really know your users & • No/Low value- Walk
their goals away
• Call out all limitations, • Don’t Overbuild
capacity, complexity etc • Think Incremental gains
• Product variance by user • Use the force
type

Owners Capability Delivery Inspire Value to you
Objectives Competence Devices Improvise Value to user
Outcomes Capacity Data Implement Value <> $

29

Sourcing Existing Data…
...10001_ADVERTISMENT_010110101000111001100110011010110001
010110101000111001100ERROR_4041010110001010110101000111001
100110011010110001010110101000CLICK_HERE100110011010110001
0101101010NEW_FRIEND_REQUEST001100110101100010101101010001
11001100110011010110001010110101000111_VIDEO_0011001100110
101111110101101INSTANT_CREDIT01000111001100110011010110001
0101BANNER_ADS1010100011100110011UPSELL_CROSSSELL111111110
10110001010110101000111XHTML?0011010110001010110SQL0000011
1001INTERNET_OF_THINGS00110101100010LOGISTIC_REGRESSION111
001100110011010110001010TABLET_HANDSET11010100011100110011
001101_SEARCH101011010100011100110DATA_1011000101011010100
01110011ANALYTICS10101100010101010INTELLIGENCE010101101010
0011100110011001101001...

…Mark Slusar / Allstate Research Fellow

Mark’s Experience & Company
Formal Education: Undergrad: Art; Grad: Business (Marketing)
Informal Education: WWW, Events, Books, Tutorials, Friends, Family, Music, Art,
Movies, Reflection, Life Experiences, Successes, and Failures.

Early Career: Developer & Designer of “Web 1.0” Sites, Portals, CMS,
E-Commerce, Advertising, and Loyalty Systems

Mid Career: Transition to Product & Team Leadership 2004

Past 5 years @ Navteq & Nokia: Technology Research, Mentorship, Product
Prototyping, Service Design, Invention, and Portfolio Management

Business Owner of Allstate Enterprise Analytic Ecosystem
A Data Scientist’s Paradise!
BI, Descriptive Analytics, NLP, Predictive Analytics, Prescriptive Analytics. Using
Hadoop, Exadata, Vertica, et al.

Mark’s Product Responsibilities
People
– Analysts, Actuaries, Analytics Engineers, Developers, Testers, Statisticians,
Mathematicians, and more!
– Train, Mentor, Manage, Collaborate, Lead, Partner

Process
– Research (Economic, Fraud, Pricing, Marketing)
– Operations (Menlo Park, Northbrook, Belfast N. Ireland)
– Go Agile Methodology!!

Technology
– Hardware (Big Box, Hadoop, GPUs, VMs, Cloud, Legacy, ESB)
– Software (Open Source, Commercial, Custom, and Secret Sauces : )

New ideas and approaches percolate just about every day..

Focus Topic: Sourcing Internal Data
Identify Your Sources:
Any Data can be Big, you’ve heard about the 3 Vs + C? (Frequently Cited: volume, variety, velocity, and complexity)

• Customer
– Broad (purchases, returns, credit, age, gender)
– Narrow (mouse movements, eye tracking, voice monitoring)
• Transactional (customers, vendors, marketplace, ESB, and ??)
• Employee & Employee Generated
• Operational & Logistics
• Sensor
• Location (one of my favorites)
• Public Domain
• Semantic Linkages & Relationships
• Audio & Video
• Unexplored digital areas
• and more…

Remember: if you don’t have it, you can always start gathering it.

Co-mingling Tactics:

• Blending, Joining, Fuzzy-Joining, Inferencing

• Character Sets, Language, Transliteration, Localization, Regional Dialects

• Format & Structure (raw text, structured text, images, spatial, video,
audio, xml, csv)

• Transition with ease (avoid flattening, respect schema)

• Nurture your taxonomies & ontology, hire an MLS

Iterate, Document, Test, Automate, Be Smart, Be Inquisitive

Sourcing Advice:

• Get Permission to use data
• Be careful, outsiders can model your data and spy on you (srsly)
• Standardize Source Data Analysis
– Better Yet, Automate it
– Even Better, Run it all the time, Obsess over quality
• Source with your customers in mind --
• Source with your competition in mind
• Understand both signal & noise

The “Dollars Per Gigabyte” model died with the DVD -- Value comes
from how fast and well you assimilate, process, and distribute data

“Interchangeable” Key Take-Aways
• Rookie: Exciting Times
– Data and the tools we interact with it are hyper-evolving, this
will be a wild and fun ride! Learn something everyday.

• Manager: Stay Focused
– Embrace both Quantitative Metrics & Qualitative Metrics

• Director: Ask The Tough Questions
– Data is always half as good as it appears to be

• Business Unit Manager: Build Smart Organizations
– Go watch the “I Love Lucy” Chocolate Factory video
…that’s big data

Thanks for listening!!
Time for the next speaker

Getting Your Data,
Part 2:
Manufacturing New
Data Sources

Perspectives from a research
organization

What is NORC?

• Survey research organization established in 1941
• Affiliated with the University of Chicago
• Reputation for producing high-quality,
foundational data sources
• General Social Survey (GSS)
• National Longitudinal Survey of Youth
• National Immunization Survey
• National Social Life, Health and Aging Study
• National Survey of Children’s Health
• Survey of Consumer Finance
• Work in the public interest
Insert Presentation Title and Any Confidentiality Information 41

Characteristics of High-Quality,
Primary Data Collection

• Research objectives are carefully conceived and
very clear
• Design questionnaire items and rigorously test
them for comprehension, validity and reliability
• Information collected directly from respondent
• Robust statistical dimension
• Sample design that ensures the data represent the
population
• Identifying and managing potential for bias in the
sample that might skew the truth
• Cleaning, preparing and weighting data

Characteristics, continued

• Respondent Right to Consent
• Institutional Review Board approval
• Transparency and Credibility
• Methods are documented and published
• Data must withstand the scrutiny of the
government and the research community
• Use in peer-reviewed publications
• Slow, steady, precise approach
• Can be costly, time-consuming


How Do We Do It?

• Determine the best sample for the research need
• Random Digit Dial
• Area probability sampling
• List Samples
• Census
• Design your instrument and decide the best way
(mode) to ask your questions
• Telephone interview
• Face-to-face interview
• Web survey
• Fancier ways (cameras, diaries, sensors, drones…)

How Do We Do It, continued

• Lots of quality checks:
• Instrument development and testing
• Consistent training and certification of interviewers
• Real-time data review and consistency checks to
make sure instrument (and interviewers!) are working
properly
• Data cleaning and preparation steps
• Statistical weighting to offset any bias in the
sample


Is All This Necessary?

• Different data needs demand different degrees of
statistical rigor
• Statistical underpinnings provide confidence that
the data represent the population
• All data have some degree of error, but we know
exactly what that error is
• Pew Study (2013) on public opinion surveys vs.
Twitter
• www.pewresearch.org/2013/03/04/twitter-reaction-to-
events-often-at-odds-with-overall-public-opinion/


How Do These Data Sources
Help Me?

• Taming the Wild West of Big Data
• These “primary” data sources provide a
foundation for testing the validity and viability of
new data sources
• You need a gold standard against which to introduce a
new currency
• Recent assessments of Google and Twitter flu data


Legal and Ethical Constraints on Data
Products:
Managing to Regulatory Compliance, Consumer Privacy
and Corporate Trade Secrets

Jackie Beaubaire, Director, Content Licensing & Governance
March 19, 2013

Lets Talk about Me

 Background:
 Degree in Health Information Management
 Rush Presbyterian St. Luke's Medical Center
 North Shore University Health System
 HealthStar PPO
 Deloitte Consulting
 Truven Health Analytics (FKA Sachs Group, Solucient, Thomson,
Thomson Reuters, etc, etc

51

Truven Health Analytics

• In the data/analytics business since the 80s…..but
different names
• Clients include:
– hospitals
– health plans
– Employers
– Pharmaceutical
– federal and state government
• Our solutions support marketing, planning, clinical
analysis, claims analysis….improve outcomes and
decrease costs
• Approx $600M in annual revenues
• We use client supplied data and purchased
intellectual properity from 3rd party vendors

Me, Continued

• Director, Content Licensing and Governance
– Acquire content from 3rd parties
• Data and Methodologies
– State and federal data
– Reference Data
– Other large data vendors
• Sometimes we negotiate multi-year complex deals and sometimes we
just sign on the doted line
• Data costs range from free to $1M per year
– Govern the use/release of the content
• Ensure that the release rules and obligations are woven into the fabric
of the business

Lots and Lots of Data with lots and lots of rules

• Regardless of where you get the data, there are usually rules to
follow.
• Some are specific to Healthcare and some are not
– HIPAA – Privacy and Security
– SOX
– DOJ
– Other rules around use of SS#. claims data and marketing
– Contractual obligations
• You need to understand the rules that impact your industry and
data type
• Misuse of data can lead to fines, public announcements,
potential jail time, reputation issues and loss of the data
stream….all of which can impact revenue
• Some contracts have incident notification clauses and some
don’t. There is an ethical line that you don’t want to cross

Tips For Using Client Supplied Data

• If you are using client supplied data:
– Client contracts must support your use/release
• “XYZ company retains the world wide rights to use your data as long
as we….”
• Sometimes this requires reading all of your client agreements to
ensure the use rights are there.
– Make sure that the client is authorized to provide this data to you
– Sometimes you give a small part of the product away for the wider
use of the data
– You need to understand the clients security, privacy, confidentiality,
ethical and other concerns and then support them. They do not
want to give their data to have you misuse it
– Misuse of data can lead to fines, reputation issues and loss of the
data stream….all of which can impact revenue

Tips For Using Vendor Data

• You are purchasing someone else's intellectual property. This is
how they make their money and you should respect that.
• Some data can be found and other data have only one source.
This dramatically changes the relationship and negotiation
• Vendors will outline your use rights and obligations in the
contract
• Sometime you can negotiate and other times you can’t
• Obligations can include, Client data use agreements,
aggregation, cell suppression, royalty, citations, market sales
limitations, etc
• Misuse of data can lead to fines, reputation issues and loss of
the data stream….all of which can impact revenue

Data Governance

• If you are a data company…..data is your most important asset
It is a good idea to protect it
• It does not have to be large, but you do need a presence
• Ensure that your products and services are compliant BEFORE
launch or contract signature
• Examples:
– My team is at gate meetings and can stop a product from releasing
– I work with legal and the sales team on new/unique deals to ensure
that we can sell what we want sell. Shutting a deal down right
before contract signature is not fun

PDMA - Monetizing Big Data Panel:
Packaging & Pricing Your Data

Mike Jakob – President & COO

March 2013

Sportvision Company Highlights

• Leading provider of sports media and data solutions
• 10,000+ live events
• 100M+ viewers annually
• 18 Olympic, Pro and College sports

• History of cutting-edge new product innovation
• 10 Emmy Awards, Invented Iconic sports products
• Fast Company “The World’s 50 Most Innovative Companies”
• Sports Business Journal Technology of the Year

• Positioned to benefit from growing market for sports data
• Fans want interactive content across devices
• Data becoming critical for teams, leagues and broadcasters

• YouTube video link about Sportvision
• http://www.youtube.com/watch?v=lxDHYKXZa6w

61
Proprietary and Confidential

Version 1.0: Broadcast Enhancement Provider

62

Version 2.0: Proprietary Sports Data & Multi-Platform Capabilities

63

Sportvision is Collecting Big Data

Sport Live Event Presence Data Collected:

Baseball
• Speed, location, and trajectory of every
• MLB, MiLB, WBC, KBO
pitch, hit, player, throw

Football

Motorsports
• NASCAR: • Car speed, location, acceleration, time
behind leader, RPM, brake, throttle
Cup, Nationwide, Truck percentage, pit stop data

Hockey

Sailing
• Boat speed, location, acceleration, time
• All AC Series races behind leader, infractions, course
boundaries

64

Packaging the Data: Vertically Integrated or Data Provider?

• What are the potential markets for my Data? Which are the
most valuable segments & who accrues the most value?

• Do I have the skills, expertise, credibility and capital for each
addressable market? Can I acquire more through
partnerships?

• Can I play in multiple markets at once?

65

Pricing the Data: How much is it worth?

Tim Lincecum’s August 2010 “Slump”

The release slot of all of his pitches were higher than average. Shown here are the
differences between his cut fastball and slider.

66

Pricing the Data

• Tim Lincecum’s ERA drops from 7.82 in August 2010 to 1.94
in September 2010
– Picks up 5 post-season wins in October, Giants win first World Series
since 1954
– Lincecum signs a new two-year deal after the 2011 season worth
$40.5m

• What’s this Data worth to the Giants? To Lincecum?

• How much did we get paid for it?

67

A few Takeaway Lessons

• Proprietary Data is valuable and often enables a barrier to
entry for competitors

• Much of the value often goes to the “last mile” in the value
chain…so do more than just collect it

• Even if you are not able to charge what the data is worth…if
you create value for your customers they will keep coming
back for more

68

Market Making with Data
PDMA Event: Monetizing Big Data

March 2013

Brandon Cox

Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
Copyright © 2012 Nielsen. Confidential and proprietary.

Introduction – Brandon Cox

(2013)

(2012)

(2004)

(1999)

(1997)
71

Big data and big computing have big roots in Chicago

Arthur C. Nielsen founds
1923 A.C. Nielsen in Lake View

A.C. Nielsen creates a syndicated
1932 retail index and invents the
concept of “Market Share”

2101 W. Howard Street, Chicago

A.C. Nielsen invests $150,000
1948 in the building of the first non-
government UNIVAC

72

Commercialization demands understanding your clients

Key Questions
• Who buys from my target client?
• Who, in addition to the buyer, does my
Market client need to influence or incentivize?
Who
Ecosystem • Who does my client compete with for
share (wallet or mind)?
• Who uses the data for decisions?
• What decisions do my clients want to
Selling activate in the market?
What
Conversation • What content or analysis is required?
• What is the importance of common
language among stakeholders?

• Which competing data sets could satisfy
Which Alternatives the need also?
• Which aspects of need do I meet?

73

The Who: clients’ market ecosystem is at the core of value

Product Flow
Data Users

Selected Suppliers Selected Retailers
• Who buys from my client?

• Who, in addition to the buyer,
does my client need to
influence or incentivize?

Consumers
• Who does my client compete
with for share (wallet or
mind)?
• Who uses data for decisions?

• Why is this different/so what?

Who do your target clients care about?
74

The What: activation at the point of sale is the barometer of
need
Product Flow
Network Flow

Selected Suppliers Selected Retailers
• What decisions do my clients
want to activate in the
market?

• What content or analysis is
required to support that?

Consumers
• What is the importance of
common language among
stakeholders?

• So what?

What do your target clients want to know and to say to their customers?
75

The Which: winning out over alternative sources

Flash Case Study – “Battle of the Network Effects”

Sample Client Need: Diageo needs to
know where it is selling and where it isn’t

• Which competing data sets could
satisfy the need also?
• Which aspects of need do I meet?

Retail List Nielsen TDLinx
1) High quality store list with 1) High quality store list with
high quality geocoding good geocoding
2) Basic retail classifications
that are mostly accurate
VS 2) Industry standard hierarchy
3) Scoring functionality to “link”
3) Mapping source code store-based data sources
4) No scoring functionality to 4) Constant feedback loop by
align other data sets cleansing client submissions
5) But it’s free! 5) ~$1 per store 

Why is your answer the best one?
76

So here’s what we look for in making powerful data markets

Markets We Generally Find Receptive to Data-Driven Propositions
1) Markets in which brands are very meaningful to consumers, but in which
the owners of brands do not have a direct relationship with the consumer
2) Markets with diffuse but established set of competing retail businesses
(defined as any business that interacts directly with a significant subset
of the public) who gather data about that interaction
3) Markets in which marketing decisions (promotional investment, pricing,
etc.) affect or are sometimes made by other players in the ecosystem

• A compelling value proposition can be made to the players in the market ecosystem
that has these characteristics, and it doesn’t have to be mere basic volumetrics

• Examples of industries might include consumer packaged goods, new and used
automobile sales, insurance, mobile communications, other consumer durables, etc.

77

78

WINNING ELEMENTS OF Monetizing
Data

A DATA PRODUCTS TEAM

KEN TRESKE

BACKGROUND

 Direct Marketing Executive through the emerging Digital Data
Evolution

 Coolsavings – original digital coupon, redemption and modeled
emailer

 HR Competencies – amassing SME’s to define successful
competencies

 Vente – Experian Unit – selling consumer data attributes for
marketing services

 Dotomi – Personalized advertising that uses big data and dynamic
creative

COMPETING WORLD VIEWS DRIVE NEED

Traditional Future
 Datasets; Lists;  Solutions; Prediction;
Attributes; Implied Machine integration;
Benefits Micro to macro

HARMONIOUS CONFLICT STRETCHES A
TEAM

Sales – expand data Quality – narrow data

Operations – streamline Analytics – insight, artisan
mechanize new innovation

MBA’S VS. PH.D’S
ANALYSTS VS. SCIENTISTS

We have the answers The data has the answer

KEYS AND INTEGRATION

 Is data responsible for
Obama winning the
election?

 Integration

 Predictability

 Application

UNLOCKING HIDDEN MEANING

Breaking down the
details for new truths

Seeing patterns

Crowd-sourcing

OED:
- Details
- Rules based
- Crowd sourced

FINDING TALENT AND EXPERTISE

 Leaders
 Outside of data; Customer Centric; Inspiring

 Data Operations:
 Large retailers and cataloguers

 PhD’s:
 Political campaigns; Financial Services

 Sales
 Many data service companies and Media companies

 Quality
 Manufacturing – garbage in / garbage out

SUMMARY OF WINNING ELEMENTS

 Establish your vision – and be aware of long term
“machination”

 Leadership to manage through the table -stakes resources

 The new age of the scientist

 You need to lock into your target environment

 A role for crowd-sourcing and getting to elemental patterns

Monetizing data - An Evening with Eight of Chicago's Data Product Management Leaders

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