Monetizing data - An Evening with Eight of Chicago's Data Product Management Leaders
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Transcript of 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
Product Development and Management Association
Product Development and Management Association
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!)
Product Development and Management Association
•
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
Product Development and Management Association
Product Development and Management Association
Product Development and Management Association
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
Product Development and Management Association
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.
Focus Topic: Sourcing Internal Data
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
Focus Topic: Sourcing Internal Data
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
Product Development and Management Association
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
Insert Presentation Title and Any Confidentiality Information
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
Insert Presentation Title and Any Confidentiality Information
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?
Insert Presentation Title and Any Confidentiality Information
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
Insert Presentation Title and Any Confidentiality Information
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.
• www.pewresearch.org/2013/03/04/twitter-reaction-to-
events-often-at-odds-with-overall-public-opinion/
Is All This Necessary?
Insert Presentation Title and Any Confidentiality Information
47
• 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?
Insert Presentation Title and Any Confidentiality Information
Product Development and Management Association
Product Development and Management Association
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
Product Development and Management Association
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
Version 1.0: Broadcast Enhancement Provider
62 Proprietary and Confidential
Version 2.0: Proprietary Sports Data & Multi-Platform Capabilities
63 Proprietary and Confidential
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?
65 Proprietary and Confidential
Pricing the Data: How much is it worth?
66 Proprietary and Confidential
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
67 Proprietary and Confidential
• 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
68 Proprietary and Confidential
Product Development and Management Association
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
71 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
Introduction – Brandon Cox
(1997)
(1999)
(2004)
(2012)
(2013)
72 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
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
73 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
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
74 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
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?
75 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
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?
76 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
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
77 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
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
78 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.
Product Development and Management Association
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 and Management Association
Product Development Management Association