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Data Monetization Overview

Data Monetization Overview. What Is Data Monetization?. Data monetization is generating revenue from available data sources or real time streamed data by instituting the discovery, capture, storage, analysis, dissemination, and use of that data.

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Data Monetization Overview

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  1. Data Monetization Overview

  2. What Is Data Monetization? • Data monetization is generating revenue from available data sources or real time streamed data by instituting the discovery, capture, storage, analysis, dissemination, and use of that data. • Said differently, it is the process by which data producers, data aggregators and data consumers, large and small, exchange sell or trade data.

  3. The Approach We use the Business Model Canvas as described in the book Business Model Generation by Alexander Osterwalder and Yves Pigneur

  4. Our Approach

  5. Revenue An organization serves one or several Customer Segments. It seeks to solve customer problems and satisfy customer needs with Value Propositions. Value Propositions are delivered to customers through communication, distribution, and sales Channels. Customer Relationships are established and maintained with each Customer Segment. Revenue Streams result from Value Propositions successfully offered to customers.

  6. Cost Structure Key Resources are the assets required to offer and deliver the previously described elements by performing a number of Key Activities. Some activities are outsourced and some resources are acquired outside the enterprise through Key Partnerships. These are the business model elements that result in the Cost Structure.

  7. Customer Segments Customers represent the heart and soul of an enterprise as it exists to serve its customers. In order to better address the needs of its customer they may group into distinct segments based on common needs. Customer groups represent distinct segments if: • Their needs require and justify a distinct offer • They are reached through different Distribution Channels • They require different types of relationships • They have substantially different profitability • They are willing to pay for different aspects of the offer

  8. Value Proposition The customer value proposition is a clearly defined bundle of products or services that meets the requirements of a specific customer segment. In essence the value proposition is the aggregation of benefits that the enterprise offers its customers. • The Value Proposition addresses a Critical Business Issue(s) shared by the customer segment. • A Critical Business Issue is an opportunity that if solved correctly creates a sustained material change in the vector and velocity of cash flow.

  9. Channels Channels are the touch points between the enterprise and its customers and play a definitive role in the customer experience. • Direct Sales • Indirect Sales • Account Management • Trade Shows & Events • Web Site • Newsletters • Technical Documents • Social Media

  10. Customer Relationships The customer relationship block describes how the enterprise will interact with its customers at the levels of acquisition, retention, upsell and service.

  11. Revenue Streams The enterprise must answer the questions: what is each customer segment willing to buy and how much they are willing to pay for each product in the value proposition bundle.

  12. Key Activities Key activities are the most important actions an enterprise must take to operate successfully. Like key resources they are required to create and offer value propositions, reach markets, maintain relationships with customers and earn revenues.

  13. Key Resources The key resources allow an enterprise to create and offer value propositions, reach markets, maintain relationships with customers and earn revenues.

  14. Key Partnerships An enterprise creates alliances to optimize their business model, reduce risk or acquire required resources.

  15. Cost Structure Creating and delivering value, maintaining customer relationships and generating revenue all incur cost. Once the key resources, key activities and key partnerships are identified it is fairly straight forward to compute cost.

  16. The Reality of Data • It is messy • It changes constantly • It does not always link • It is hard to find external value • It is processing intensive • Not all data is created equal

  17. Ways That Data Can be Monetized • Raw • Aggregated • Comparative • List • Modeled • Transactional

  18. The Importance of PII “Personally identifiable information" (PII), as used in US privacy law and information security, is information that can be used on its own or with other information to identify, contact, or locate a single person, or to identify an individual in context.

  19. FCRA The Fair Credit Reporting Act (FCRA) is a United States federal law that regulates the collection, dissemination, and use of consumer information, including consumer credit information • "consumer reporting agency" as any person "which, for monetary fees, dues, or on a cooperative nonprofit basis, regularly engages in whole or in part in the practice of assembling or evaluating consumer credit information or other information ... for the purpose of furnishing consumer reports to third parties ...“ • "consumer report" as the communication of "any information" by a CRA that bears on a consumer's "credit worthiness, credit standing, credit capacity, character, general reputation, personal characteristics, or mode of living" that is "used or expected to be used or collected in whole or in part" for the purpose of serving as a factor in establishing eligibility for credit or insurance to be used primarily for personal, family, or household purposes, employment purposes, or any other purpose authorized under Section 604.

  20. What the Market Is Telling Us • PII enabled data is worth roughly 3 times the value of non PII enabled data. • Real-time transactional data is worth 2 to 5 times other forms of data. • Real-time data must be combined with other data to provide a meaningful contextually relevant engagement. • Data aggregation companies (Experian, Acxiom etc.) are interested in new data sources (if unique). • The ability to predict/measure outcomes is critical to value creation

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