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

Product Development and Management Association

Randy Horton Managing Principal, 94 Westbound Consulting


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

http://www.seatquest.com/

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

22

• Global commercialization

of Nielsen Answers BI

platform

• Global lead for data and

analytical asset delivery

platform $1.5B, 35K users,

33 countries, 12 languages

• First Global data

solution for

environmental

compliance

• Product-assembly-

component-base material

• 500 million vehicles

• Registration, accident,

emissions, odometer

• States, dealers, OEMs,

insurance, auction

• Sales performance

• Predictive purchase

models

• 235 million consumers

• 113 million households

• Behavioral, attitudinal

• 3K+ elements

• Plus Web search data

• Automated profiling and

targeting solutions

• Digital effectiveness

• EDI based volume data for

500+ national

agricultural pesticides

wholesalers to drive

marketing plans

22

THE KEYS – OCDix™

23

Owners

Objectives

Outcomes

Capability

Competence

Capacity

Delivery

Devices

Data

Inspire

Improvise

Implement

Value to you

Value to user

Value <> $

24

What

• is the need?

• problems to be solved?

• decisions to be made?

• questions to be

answered

• other questions may

come up

who

• is the audience?

More than one?

• will you design for?

• will you not design

for?

HOW CAN I HELP

Put data in context of needs to build a roadmap to

solution

25

Usage Style

• Summary rollups

• Alerts and signals

• Ad-hoc analysis

• Interactive

User Type

• Internal or

external

• Tech vs. non tech

• Onsite/Remote/

Mobile

CAN I HELP YOU

How will it be used

Usage Type

• Single use

• Subscription

• Ad-hoc

Delivery & Devices

• Website

• FTP

• Integrations

• Tapes (yes)

• Tablet, phone,

custom devices

CAN IT BE BUILT?

SHOULD I Build it

26

User

Competency-

Can they use the new

information

Capability & Capacity

–

How soon will user

start using it

Are other pieces to

execute available?

Complexity &

Constraints-

How much advisory &

consulting needed

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

YOU

Competency &

Competition

core competency for

you?

Capability & Capacity-

Can you address it?

What else is on your

plate?

Can you deliver if it is

built?

Complexity & Constraints

size, usage, frequency,

reliability,

regulatory?

ROI

Opportunity Cost

27

Don't get high on your own supply

Big data for big challenges?

Big, small, medium,

petite, grande, venti,

Big and tall..look

beyond the label

Big problems = big

investment +

complexity &

constraints =

longer duration for

ROI.

Solve incremental

issues along the way

for quicker ROI

Fund future

initiatives and get

evolutionary gains

along the way to

revolutionary gains

28

SUMMARY- building a wining product

29

Owners

Objectives

Outcomes

Capability

Competence

Capacity

Delivery

Devices

Data

Inspire

Improvise

Implement

Value to you

Value to user

Value <> $

• Really know your users &

their goals

• Call out all limitations,

capacity, complexity etc

• Product variance by user

type

• No/Low value- Walk

away

• Don’t Overbuild

• Think Incremental gains

• Use the force

Sourcing Existing Data…

…Mark Slusar / Allstate Research Fellow

...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’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

Perspectives from a research

organization

Getting Your Data, Part 2:

Manufacturing New Data Sources

41

• 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

What is NORC?

Insert Presentation Title and Any Confidentiality Information

42

• 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 of High-Quality, Primary Data Collection


43

• 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

Characteristics, continued


44

• 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?


45

• 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

How Do We Do It, continued


46

• 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/

Is All This Necessary?


http://www.pewresearch.org/2013/03/04/twitter-reaction-to-events-often-at-odds-with-overall-public-opinion/





















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• 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

How Do These Data Sources Help Me?


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

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

Lets Talk about Me

51

• 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

Truven Health Analytics

• 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

Me, Continued

• 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

Lots and Lots of Data with lots and lots of rules

• 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 Client Supplied 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

Tips For Using Vendor Data

• 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

Data Governance

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

Mike Jakob – President & COO

March 2013

• 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

Sportvision Company Highlights

61 Proprietary and Confidential

http://www.youtube.com/watch?v=lxDHYKXZa6w

Version 1.0: Broadcast Enhancement Provider


Version 2.0: Proprietary Sports Data & Multi-Platform Capabilities


Sportvision is Collecting Big Data

64

Sport Live Event Presence Data Collected:

Baseball

• MLB, MiLB, WBC, KBO • Speed, location, and trajectory of every

pitch, hit, player, throw

Football

Motorsports • NASCAR:

Cup, Nationwide, Truck

• Car speed, location, acceleration, time behind leader, RPM, brake, throttle percentage, pit stop data

Hockey

Sailing

• All AC Series races • Boat speed, location, acceleration, time

behind leader, infractions, course boundaries

Proprietary and Confidential

• 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?

Packaging the Data: Vertically Integrated or Data Provider?


Pricing the Data: How much is it worth?


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

Tim Lincecum’s August 2010 “Slump”

• 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?

Pricing the Data


• 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

A few Takeaway Lessons


Copyright © 2012 Nielsen. Confidential and proprietary.

Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.

Market Making with Data

PDMA Event: Monetizing Big Data

March 2013

Brandon Cox


Big data and big computing have big roots in Chicago

2101 W. Howard Street, Chicago

1923

1932

Arthur C. Nielsen founds

A.C. Nielsen in Lake View

A.C. Nielsen creates a syndicated

retail index and invents the

concept of “Market Share”

1948 A.C. Nielsen invests $150,000

in the building of the first non-

government UNIVAC


Commercialization demands understanding your clients

Key Questions

• Who buys from my target client?

• Who, in addition to the buyer, does my

client need to influence or incentivize?

• Who does my client compete with for

share (wallet or mind)?

• Who uses the data for decisions?

• What decisions do my clients want to

activate in the market?

• What content or analysis is required?

• What is the importance of common

language among stakeholders?

• Which competing data sets could satisfy

the need also?

• Which aspects of need do I meet?

Market

Ecosystem

Selling

Conversation

Alternatives

What

Which

Who


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

Selected Suppliers Selected Retailers

Product Flow

Data Users

• Who buys from my client?

• Who, in addition to the buyer,

does my client need to

influence or incentivize?

• Who does my client compete

with for share (wallet or

mind)?

• Who uses data for decisions?

• Why is this different/so what? C

onsum

ers

Who do your target clients care about?


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

Selected Suppliers Selected Retailers

Product Flow

Network Flow

• What decisions do my clients

want to activate in the

market?

• What content or analysis is

required to support that?

• What is the importance of

common language among

stakeholders?

• So what? C

onsum

ers

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


The Which: winning out over alternative sources

Why is your answer the best one?

Flash Case Study – “Battle of the Network Effects”

Retail List

1) High quality store list with

high quality geocoding

2) Basic retail classifications

that are mostly accurate

3) Mapping source code

4) No scoring functionality to

align other data sets

5) But it’s free!

VS

Nielsen TDLinx

1) High quality store list with

good geocoding

2) Industry standard hierarchy

3) Scoring functionality to “link”

store-based data sources

4) Constant feedback loop by

cleansing client submissions

5) ~$1 per store

• Which competing data sets could

satisfy the need also?

• Which aspects of need do I meet?

Sample Client Need: Diageo needs to

know where it is selling and where it isn’t


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

• 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.

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

Monetizing

Data

WINNING ELEMENTS OF

A DATA PRODUCTS TEAM

KEN TRESKE

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

BACKGROUND

Traditional

Datasets; Lists;

Attributes; Implied

Benefits

Future

Solutions; Prediction;

Machine integration;

Micro to macro

COMPETING WORLD VIEWS DRIVE NEED

HARMONIOUS CONFLICT STRETCHES A

TEAM

Sales – expand data Quality – narrow data

Operations – streamline

mechanize

Analytics – insight, artisan

new innovation

MBA’S VS. PH.D’S

ANALYSTS VS. SCIENTISTS

We have the answers The data has the answer

Is data responsible for

Obama winning the

election?

Integration

Predictability

Application

KEYS AND INTEGRATION

UNLOCKING HIDDEN MEANING

Breaking down the

details for new truths

Seeing patterns

Crowd-sourcing

OED:

- Details

- Rules based

- Crowd sourced

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

FINDING TALENT AND EXPERTISE

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

SUMMARY OF WINNING ELEMENTS

Product Development Management Association

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

Technology

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