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


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PDMA Chicago - March 19, 2013

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

  1. 1. Product Development Management Association
  2. 2. Monetizing Big Data:An Evening with Eight of Chicago’sData Product Management Leaders March 19, 2013 Pazzo’s at 311 S. Wacker
  3. 3. Randy Horton Managing Principal, 94 Westbound ConsultingProduct Development and Management Association
  4. 4. Product Development and Management Association
  5. 5. Product Development and Management Association
  6. 6. 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
  7. 7. • 1. Whats 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 teamProduct Development and Management Association
  8. 8. Product Development and Management Association
  9. 9. Product Development and Management Association
  10. 10. Product Development and Management Association
  11. 11. DESIGNING A DATA PRODUCT TO FIT A REAL NEED Kamal Tahir, ExperianIdentifying needs , Segmenting, KnowingCustomer Requirements
  12. 12. Using data, technology, analytics and strategy, I help drive profit, volume & shareacross 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
  13. 13. THE KEYS – OCDix™Owners Inspire Value to you Capability DeliveryObjectives Improvise Value to Competence Devices Implement userOutcomes Capacity Data Value <> $ 23
  14. 14. Put data in context of needs to build a roadmap to solutionwho 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
  15. 15. How will it be usedUser Type• Internal or Usage Style external • Summary rollups• Tech vs. non tech • Alerts and signals• Onsite/Remote/ • Ad-hoc analysis Mobile • InteractiveDelivery & Devices• Website Usage Type• FTP • Single use• Integrations • Subscription• Tapes (yes) • Ad-hoc• Tablet, phone, CAN I HELP YOU custom devices 25
  16. 16. Success -Ability to solve, deliver, use - for You & userYOU UserCompetency & Competency-Competitioncore competency for Can they use the new informationyou?Capability & Capacity- Capability & CapacityCan you address it? –What else is on your How soon will user start using itplate? Are other pieces toCan 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 neededregulatory? Opportunity Cost 26
  17. 17. Dont get high on your own supply 27
  18. 18. Big data for big challenges?Big, small, medium, Solve incrementalpetite, grande, venti, issues along the wayBig and tall..look for quicker ROIbeyond the label Fund futureBig problems = big initiatives and getinvestment + evolutionary gainscomplexity & along the way toconstraints = revolutionary gainslonger duration forROI. 28
  19. 19. 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 typeOwners Capability Delivery Inspire Value to youObjectives Competence Devices Improvise Value to userOutcomes Capacity Data Implement Value <> $ 29
  20. 20. Product Development and Management Association
  21. 21. Sourcing Existing Data…...10001_ADVERTISMENT_010110101000111001100110011010110001010110101000111001100ERROR_4041010110001010110101000111001100110011010110001010110101000CLICK_HERE1001100110101100010101101010NEW_FRIEND_REQUEST00110011010110001010110101000111001100110011010110001010110101000111_VIDEO_0011001100110101111110101101INSTANT_CREDIT010001110011001100110101100010101BANNER_ADS1010100011100110011UPSELL_CROSSSELL11111111010110001010110101000111XHTML?0011010110001010110SQL00000111001INTERNET_OF_THINGS00110101100010LOGISTIC_REGRESSION111001100110011010110001010TABLET_HANDSET11010100011100110011001101_SEARCH101011010100011100110DATA_101100010101101010001110011ANALYTICS10101100010101010INTELLIGENCE0101011010100011100110011001101001... …Mark Slusar / Allstate Research Fellow
  22. 22. Mark’s Experience & CompanyFormal 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 SystemsMid Career: Transition to Product & Team Leadership 2004Past 5 years @ Navteq & Nokia: Technology Research, Mentorship, ProductPrototyping, Service Design, Invention, and Portfolio ManagementBusiness Owner of Allstate Enterprise Analytic EcosystemA Data Scientist’s Paradise!BI, Descriptive Analytics, NLP, Predictive Analytics, Prescriptive Analytics. UsingHadoop, Exadata, Vertica, et al.
  23. 23. Mark’s Product ResponsibilitiesPeople – Analysts, Actuaries, Analytics Engineers, Developers, Testers, Statisticians, Mathematicians, and more! – Train, Mentor, Manage, Collaborate, Lead, PartnerProcess – 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..
  24. 24. Focus Topic: Sourcing Internal DataIdentify 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.
  25. 25. Focus Topic: Sourcing Internal DataCo-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 MLSIterate, Document, Test, Automate, Be Smart, Be Inquisitive
  26. 26. Focus Topic: Sourcing Internal DataSourcing 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 & noiseThe “Dollars Per Gigabyte” model died with the DVD -- Value comesfrom how fast and well you assimilate, process, and distribute data
  27. 27. “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
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  29. 29. Getting Your Data,Part 2:Manufacturing NewData SourcesPerspectives from a researchorganization
  30. 30. 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 interestInsert Presentation Title and Any Confidentiality Information 41
  31. 31. 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 dataInsert Presentation Title and Any Confidentiality Information 42
  32. 32. 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-consumingInsert Presentation Title and Any Confidentiality Information 43
  33. 33. 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…)Insert Presentation Title and Any Confidentiality Information 44
  34. 34. 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 sampleInsert Presentation Title and Any Confidentiality Information 45
  35. 35. 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 • events-often-at-odds-with-overall-public-opinion/Insert Presentation Title and Any Confidentiality Information 46
  36. 36. How Do These Data SourcesHelp 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 dataInsert Presentation Title and Any Confidentiality Information 47
  37. 37. Product Development and Management Association
  38. 38. Product Development and Management Association
  39. 39. Legal and Ethical Constraints on Data Products: Managing to Regulatory Compliance, Consumer Privacy and Corporate Trade SecretsJackie Beaubaire, Director, Content Licensing & Governance March 19, 2013
  40. 40. Lets Talk about Me Background:  Degree in Health Information Management  Rush Presbyterian St. Lukes Medical Center  North Shore University Health System  HealthStar PPO  Deloitte Consulting  Truven Health Analytics (FKA Sachs Group, Solucient, Thomson, Thomson Reuters, etc, etc 51
  41. 41. 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
  42. 42. 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
  43. 43. 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
  44. 44. 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
  45. 45. Tips For Using Vendor Data• You are purchasing someone elses 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
  46. 46. Data Governance• If you are a data company… 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
  47. 47. Product Development and Management Association
  48. 48. PDMA - Monetizing Big Data Panel: Packaging & Pricing Your Data Mike Jakob – President & COO March 2013
  49. 49. 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 • 61Proprietary and Confidential
  50. 50. Version 1.0: Broadcast Enhancement Provider 62Proprietary and Confidential
  51. 51. Version 2.0: Proprietary Sports Data & Multi-Platform Capabilities 63 Proprietary and Confidential
  52. 52. Sportvision is Collecting Big DataSport Live Event Presence Data Collected:Baseball • Speed, location, and trajectory of every • MLB, MiLB, WBC, KBO pitch, hit, player, throwFootballMotorsports • NASCAR: • Car speed, location, acceleration, time behind leader, RPM, brake, throttle Cup, Nationwide, Truck percentage, pit stop dataHockeySailing • Boat speed, location, acceleration, time • All AC Series races behind leader, infractions, course boundaries 64 Proprietary and Confidential
  53. 53. 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? 65Proprietary and Confidential
  54. 54. 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. 66Proprietary and Confidential
  55. 55. 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? 67Proprietary and Confidential
  56. 56. 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 68Proprietary and Confidential
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  58. 58. 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.
  59. 59. Introduction – Brandon Cox (2013) (2012) (2004) (1999) (1997) Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary. 71
  60. 60. 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 Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary. 72
  61. 61. 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? Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary. 73
  62. 62. The Who: clients’ market ecosystem is at the core of value Product Flow Data UsersSelected 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? Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary. 74
  63. 63. The What: activation at the point of sale is the barometer of need Product Flow Network FlowSelected 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? Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary. 75
  64. 64. The Which: winning out over alternative sourcesFlash 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 TDLinx1) High quality store list with 1) High quality store list with high quality geocoding good geocoding2) Basic retail classifications that are mostly accurate VS 2) Industry standard hierarchy 3) Scoring functionality to “link”3) Mapping source code store-based data sources4) No scoring functionality to 4) Constant feedback loop by align other data sets cleansing client submissions5) But it’s free! 5) ~$1 per store  Why is your answer the best one? Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary. 76
  65. 65. So here’s what we look for in making powerful data markets Markets We Generally Find Receptive to Data-Driven Propositions1) Markets in which brands are very meaningful to consumers, but in which the owners of brands do not have a direct relationship with the consumer2) 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 interaction3) 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. Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary. 77
  66. 66. Copyright © 2013 Cox IndustriesTM LLC - Confidential and proprietary.78
  67. 67. Product Development and Management Association
  69. 69. 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
  70. 70. COMPETING WORLD VIEWS DRIVE NEEDTraditional Future  Datasets; Lists;  Solutions; Prediction; Attributes; Implied Machine integration; Benefits Micro to macro
  71. 71. HARMONIOUS CONFLICT STRETCHES A TEAM Sales – expand data Quality – narrow dataOperations – streamline Analytics – insight, artisanmechanize new innovation
  72. 72. MBA’S VS. PH.D’S ANALYSTS VS. SCIENTISTSWe have the answers The data has the answer
  73. 73. KEYS AND INTEGRATION Is data responsible for Obama winning the election? Integration Predictability Application
  74. 74. UNLOCKING HIDDEN MEANINGBreaking down thedetails for new truthsSeeing patternsCrowd-sourcingOED:- Details- Rules based- Crowd sourced
  75. 75. 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
  76. 76. 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
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  78. 78. Product Development Management Association