Knowledgelevers expanded
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The document presents a strategy for a company called KnowledgeLevers, which focuses on unlocking value in data by creating a marketplace for trading actionable data in real time. It outlines the company's mission, potential market size, innovative tools, and unique selling propositions in the data exchange space, as well as an overview of their business model and exit strategy targeting a valuation of over $500 million in five years. The document emphasizes the revolutionary approach of monetizing data by facilitating seamless transactions between data producers and consumers.
Knowledgelevers expanded
- 1. Presentation to Investors – December 2011
- 2. Unlocking Value in Data “The future belongs to the companies and people that turn data into products” O’Reilly Radar Report 1. Mission 2. Executive Summary 3. Knowledgelevers 4. Data Exchange 5. The Data Federation and Exchange Space 6. Job To Be Done 7. Knowledgelevers Tool Sets 8. IP Protection for Knowledge Levers and Derivative Applications I 9. IP Protection for Knowledge Levers and Derivative Applications II 10. Upside Potential 11. Differentiators 12. Staging Our Income Pyramid 13. Facilitating Data Trading 14. Traders Need Tools 15. Tools and Development Progress 16. Strengths - Needs - Risks 17. Our Founder 18. Evolving The Team 19. Exit Strategy 20. Bottom Line and Summary Appendix
- 3. Mission Disrupt enterprise data products through “just in time” notifications for CRM, Supply Chains, and Business Intelligence. Data is the “oil” of the 21st century Copyright 2011 Compages
- 4. Executive Summary Unlocking value in data through enabling a new market — a hybrid between what did for used goods, did for retailers and for the music industry. We will implement and protect methods and systems to collect fees for enabling data to be traded and operated upon in real time. A multi-billion dollar opportunity 3.5 Million invested into software and IP Concept and technology validated by currently working installations Robust IP with supportive prototyping Copyright © 2011 for Knowledgelevers.com 1
- 5. Experienced in data management Deep understanding of problems faced by researchers and risk managers Projected valuation takes us to $500 million in 2016 Multiple sales and growth channels – Broad market Diverse market for buyers of data. Diverse producers of data who want to sell it. Effort to identify which data to buy or sell. Need for actionable intelligence for risk assessment and competitive advantage Resistance Opportunity Copyright © 2011 for Knowledgelevers.com 2
- 6. Data Exchange Data Federators and Distributors Data Accumulators and Aggregators Critical Research Enterprises - Cut losses Gallup, Gartner – Distribute the right data from useless research and liabilities from to customers missed indicators. A Market in Search of a Trading Platform Data Based Risk Mitigators Data Creators and Producers Stock Fund Managers or Homeland Security All businesses, especially retailers and – Notify the right person as the dots get financial institutions – Sell fallow data to connected. buyers. Copyright © 2011 for Knowledgelevers.com 3
- 7. The Data Federation and Exchange Space Node51 Warehousing Visualization Data Cloud Apps, Business Consulting Odd Fellows and Linking for and Transformation Appliances, Intelligence Analytics, Specialized Computation Management Suites Extraction, Data Exchange and Storage Collaboration Customers or Joint Venture Channel Channel OEM Outlets Sales Outlets Joint Sales Potential Partners Partners Partners or Competitors Competitors Nobody in the space has monetized automated chains of data or triggered actions. Copyright © 2011 Knowledgelevers.com 4
- 8. Job To Be Done Be the global leader for brokering actionable data in real time. Problem Solution Data exchange is constricted due to Software and infrastructure to No effective marketplace for offering or discovering data Post/offer and discover data to a central location No easy way to buy or sell Establish standardized data exchange PRICING agreements No easy way to determine a price Provide a mechanisms for supply-side or demand-side pricing Multiple data formats Collect and federate data in real time or bypass federation No standardized data updates Enable updating and event triggering No standardized tools for triggering actions based on data Provide a self-service interface for simple data sharing Every Internet User - a Data Trader Every Business - a Data Vendor or Consumer Every Employee or Researcher – a Data Creator 5
- 9. Knowledgelevers Tool Sets Connection Tools Risk Communication Calculation Reduction Tools Tools and CRM Combination Tools Copyright © 2011 Knowledgelevers.com 6
- 10. IP Protection for Knowledge Levers and Derivative Applications I Big Picture: Patent methods and systems involving pricing and fees associated with data trading. Protect prices and fees for Gateways to Datasets 1. Transmission from electronic devices like Smart Phones that offer GPS locations and point of sale transactions 2. Enrollment into data trading venues through data strings like Matrix Codes, RFID tags, and direct to web services connections 3. Transmission to or from social networking sites like Facebook and Twitter in the event the Supreme Court determines ownership to be by the producer of the data or the owner of the device originating the data Protect prices and fees for Improvement of Datasets 1. Iterative additions to a dataset 2. Alternate versions of a dataset 3. Immediate utility of the data format (Data Item Pair) Copyright © 2011 Knowledgelevers.com 7
- 11. IP Protection for Knowledge Levers and Derivative Applications II Protect prices and fees for Interaction with Datasets 1. Setting up triggers to initiate server actions upon changes in a dataset 2. Tracking interaction with a GUI associated with a dataset 3. Linking enrollees (contributors) to data protocols and associated datasets 4. Linking recipients of reports or server actions to data protocols and associated datasets Protect prices and fees for assigning Value to a Data Item 1. Popularity of the item 2. Reputation of the source for the data 3. Importance of the item relative to other data items Protect prices and fees for Financial Transactions Involving Data 1. Uploading data to a parent dataset 2. Use of validation keys to connect contributors with financial institutions 3. Enrollment of a new contributor or recipient into a data supply chain Copyright © 2011 Knowledgelevers.com 8
- 12. Upside Potential As data strings or matrix codes are used for rapid enrollment into social media sites As data strings or matrix codes are expanded into enrollment of consumers for feedback and risk management If ownership of data generated upon or within an electronic device resides with the owner of the device If user expectations shift from analytics or statistics to actionable intelligence Copyright © 2011 Knowledgelevers.com 9
- 13. Differentiators We understand and can match our competitors capability and technology, but we are the first “transactional and actionable ” data firm – hence our name – Knowledgelevers. Competitors Knowledgelevers Databases – “Big” data Data Items – “Small” data Data Federation and Aggregation Data Chains, Streams, Combinations Data Transformation and Analysis Data Assessment for Actionable Value Data Mining Data Triggering and Notifications Data Storage and Warehousing Forward and Backward Redistribution Software Sales and Consulting Income Transactional Income IT Departments - Centralized Management Local End Users - Distributed (Individual Users) Siloed by Organization or Function Socially Networked Scheduled Real-time Value Proposition is “Organized Data” Value Proposition is “Actionable Information” Copyright © 2011 Knowledgelevers.com 10
- 14. Staging Our Income Pyramid 10 Million Users for 200 Billion Data Points Stage 5 SaaS $.01 per field/3% transaction – Continuous income Data Market 100,000 Buyers Stage 4 SOFTWARE -Direct to Researchers & $80,000 per sale VC Capital Enterprise Risk 15% Maintenance Managers Continuous Income Stage 3 3,000 Licensees Skip if VC OEM LICENSES - for Data Distribution Businesses $50,000 per license capital Stage 2 500,000 Buyers SHAREWARE - Self-service Consumers - to set up Skip if exchanges, wrangle data, trigger actions and notifications $99 each VC capital 125,000 Buyers Stage 1 CURRENT CUSTOMERS - Expanded sales of upgraded Employee $25,000 per sale Beta testing Performance and Risk Management Software to the public sector and and validation hospitals. 15% Maintenance Continuous Income Year 5 = Exit at Year 1 Year 2 Year 3 Year 4 $500,000,000 to $268,000 $4,000,000 $32,000,000 $246,000,000 $800,000,000 Copyright © 2011 Knowledgelevers.com 11
- 15. Facilitating Data Trading Access to multiple data types and owners: Tables, spreadsheets, and distributed databases Ability to drill down or roll up for federation or subsets: Aggregating by the data item, the data item pair, the data stream, or the dataset. Ease collecting from multiple devices, messaging services, observers, and consumers: Track changes, create and audit data Flexibility in MONETIZING AND SETTING VALUE: Rarity, reputability, integrity, usability, compatibility, popularity, recency, format friendly Streaming: Ongoing real-time or scheduled data updates Setting THRESHOLDS AND TRIGGERS FOR ACTIONS: Notification and/or other automated actions based on schedule and/or new or changed data Copyright © 2011 for Knowledgelevers.com 12
- 16. Traders Need Tools Implement a data marketplace to automate uploading and downloading, pricing, payment, and action upon data in real time. User friendly and secure applications to monetize data Universally post and exchange data Security and authentication for data transport Easily input pricing variables to enable fair compensation or reciprocity for data Price per question Contributor Specific utility and answer pair Price per field Popularity rating (rarity/recency/ Automated actions reputation rating compatibility) Enable fees and charges for exchange and payment process Device uploads and downloads Payment and transaction tools Membership fees, activation fees, convenience fees, subscription fees, volume discounts . Data is most valuable as and when it changes. Copyright © 2011 for Knowledgelevers.com 13
- 17. Tools and Development Progress 2012 2010 Prepare for Growth Expand Patent Protection ● Complete Prototypes 2009 ● Fold Legacy Applications ● Monetize Weighting together with Prototypes Architect Prototype ● Monetize Handshakes ● Up-sell current customers ● Recruit Developers ● Monetize Popularity and ● Secure Venture Capital/Partners ● Fold in Legacy Software Recency ● Expand Management Team ● Confirm Customer Need ● Monetize GUI 2006 ● Further Design and Protect ● Establish Coding and Design ● Embed Systems, Tools, and Patent Application Methods into IP Methods for Data Pricing and Exchange ● Research & Analysis ● Business Model Created ● Cost out Development Agenda Copyright © 2011 Knowledgelevers.com 14
- 18. Strengths – Needs - Risks Strengths Needs Ownership of IP - defensible competitive position Expand senior management team to drive growth Design and implement flexible/modular software Sales and marketing skill and capacity architecture Unique database design with supporting code Financial backing to fund development Data administration capability and experience Cultivate strategic partnerships Loyal customer base for current software - Recruit and organize development team receptive to upgrading Passion for data and its potential to improve and Experience scaling change lives and reduce risks Risks Mitigation Ownership of data not attributable – unclear data Retain focus on High Risk Researchers and Risk rights Managers – grow through OEM rather than SaaS Patents not enforceable or not issued First mover advantage Copyright © 2011 Knowledgelevers.com 15
- 19. Our Founder Stan Smith Founded Compages » data driven systems Limited 1980 » organization intervention consultation business Converted » software company automating survey research Compages into the Human Factor 1983 » survey research instrumentation Converted The Human Factor into » real time data supply chain software company Human Patterns » 7 current installations doing performance evaluation and risk management 1998 Developed many psychometric and » Human Patterns - a psychometric tool which now has a network of over 200 Certified Administrators survey instruments » applied in hundreds of businesses, universities, and organizations Multi-year » Ensera (acquired by ADS) consulting » Applied Biosystems (developed the code to drive the equipment for the Human Genome Project) engagements with » Propellerhead Software (acquired through a chain of acquisitions by Symantec) startups involved in data supply and » Alliance One (initiated and spun off alert® Food Safety Alert System) research automation » Workplace Options (implemented “Network Advantage” support systems for EAP’s) Copyright © 2011 Knowledgelevers.com 16
- 20. The Evolving Team Person Role Experience To Be Identified CEO Adam Chasen Architecture rPath Systems Automation Product Development To Be Identified VP Sales and Marketing Reed Altman COO, Implementation and Involved in first iteration of our data design and Training, Customer Relations, approach. Long term customer relationships on and Software Maintenance strengths of our technology and maintenance. To Be Identified Exhibition Sales and Marketing Joseph Tate Python Developer Developed patent for data form conversions SaaS Developer Copyright © 2011 for Knowledgelevers.com 17
- 21. Exit Strategy We can generate a valuation of >$500 million in 5 years Sale to major All improve position Multi-billion $ enterprise behemoths with by offering a platform to trade the world’s software capacity and cash to most ubiquitous buy commodity! DATA vendors Copyright © 2011 for Knowledgelevers.com 18
- 22. Bottom Line and Summary KnowledgeLevers is a global data exchange company enabling data producers and consumers to price and trade actionable data instead of leaving it dormant in enterprise databases or siloed on local systems. "Everything should be made as simple as possible, but not simpler." Copyright © 2011 for Knowledgelevers.com 19
- 23. Thank you! Contact: Stan@humanpatterns.com 1-803-792-0103 land, 1-919-740-5010 mobile Copyright © 2011 Knowledgelevers.com 20
- 24. APPENDIX “90 % of all data has been generated in the last 2 years” IBM 1. The Size of Market 2. IP to Revolutionize Data Trading 3. Secret Sauce – New Technology 4. Code and Architecture for Data Production and Consumption 5. Sales Divisions and Markets 6. Budget Projection for First Year 7. The Easiest Customer - The Distributor 8. Our Highest Margin Customer 9. Everybody Pays to Play in Our Cloud 10. Many Products – One Source Code Copyright © 2011 Knowledgelevers.com
- 25. The Size of the Market 30 Consumers 50 123 8 Clinical Research 16 20 25 Risk Managers 50 84 6 Data Integrators 12 16 5 Non-CRO Researchers 20 25 0 20 40 60 80 100 120 140 Worst Case Best Case Total Market Billions Total Market Size is between 100-268 Billion Our Best Case Estimate of our share of the total market = $148 Billion Our Worst Case Estimate of our share of the total market = $25 Billion Graph shows numbers assuming larger market. Copyright © 2011 Knowledgelevers.com 1
- 26. IP to Revolutionize Data Trading Process Patent Number or Defensive Value Offensive Value Application Number Discovering Data 7,860,760 High High 12/930/280 Building a User and Contributor Hierarchy 7,860,760 Low High 12/932/798 Formulating an Exchange Agreement 7,860,760 Med Med 12/930/280 13/134,596 Assigning Data Access Rights and Roles 7,860,760 Low Med 12/932,798 12/932,797 Federating Data 7.860,760 High High 13/134,596 Uploading Data from Devices, Message Services, RFID 13/134,596 High High Tags and Transmitters New application not assigned a number Pricing Parsimonious Data 13/135,420 High Mod Folding Data into Triggers 7,860,760 High High Assigning Value 7,860,760 High High 12/932,798 12/932,797 Setting Chains or Loops for Server Actions 7,860,760 Low High Copyright © 2011 for Knowledgelevers.com 2
- 27. Secret Sauce – New Technology Exchanging data across any electronic device or tag (RFID) or messaging system (Twitter - IM) Bypass need to federate datasets – link and post by the item, stream, or dataset Act upon data in real time with forward and backward chaining Easy GUI for building triggers for actions upon data Variable pricing of data items, data streams, and datasets Automated payment implementation per transaction Optional implementation of Data Item Pairs (question with answers) for researchers Copyright © 2011 Compages Limited for 3 Knowledgelevers.com
- 28. Code and Architecture for Data Production and Consumption A simple calculator-like GUI for building triggers for server actions A simple GUI to import entire enterprise-wide participant hierarchies A rigorous build and versioning method for research protocols A simple GUI to configure and implement authentication and rights schemas for levels of users across a network of data owners and contributors Real time routing of specific data points with specific context Real time distribution of notifications, updates, views, dashboard postings and updating of data sources Real time forward and backward chaining of computer driven server events based upon calculated thresholds or values Encryption and parsimonious storage at the bit level of observations entered into research protocols “Handshake” initiation based on search term results Background calculation of the pricing formula Linkage to Search engines, VPNs, and financial institutions Copyright © 2011 for Knowledgelevers.com 4
- 29. Sales Divisions and Markets BUSINESS MARKET AVERAGE SALE AVERAGE SALES COST PER RECURRING DIVISION IMPLEMENTATION METHOD SALE INCOME OR SERVICE COST Employee Public sector $25,000 $6,000 Conferences $3,000 15% Performance (Law and and Risk Enforcement) Exhibitions Management and hospitals Shareware Web Users $99 $2 SEO and $3.50 Sales – if VC Shareware funding not Outlets obtained Joint Ventures Patent Unknown Unknown Patent $0 Potentially with Niche enforcement Infringement Data FUD and Attorney Federators cooperative alliances OEM Licenses Data Vendors $50,000 $6000 Direct Sales $3000 Variable and Buyers Software Risk Managers $80,000 $3000 Direct Sales $3000 Variable Hooking into Enterprise Software SaaS Anyone Variable $1 Subscription $1 Variable Copyright © 2011 Knowledgelevers.com 5
- 30. Budget Projection for First Year Business Unit Employee Allocation Employee Cost Contractors for Rapid Expenses Sales Income Ramp Up to Stage 5 Administration – .7 Founder $126,000 Infrastructure $16,000 Architecture-Investor .4 CEO $72,000 Office and Phone Relationships .1 Sales and Marketing $12,000 $8,000 Manager .3 Software Architect $60,000 Employee Performance .3 Sales and Marketing $40,000 5 .NET Developers Travel $15,000 $100,000 and Risk Management Manager $300,000 Conferences $40,000 Exhibitor Demonstration /Closer $65,000 .5 Implementation Staff $85,000 Shareware Sales .5 Web $45,000 6 Python Developers Expenses $4,500 $88,000 Developer/Master $360,000 .1 Implementation Staff $8,000 Joint Ventures with .2 CEO $36,000 Travel $15,000 $50,00 Niche Data Federators .3 Sales and Marketing $40,000 Manager .2 Founder $36,000 .4 Developer $40,000 OEM Licenses .2 CEO $36,000 Travel $15,000 $80,000 .2 Founder $36,000 .4 Developer $40,000 Software Hooking into .2 CEO $36,000 5 Enterprise Developers Expenses $15000 Enterprise Software .3 Sales and Marketing $40,000 $300,000 Manager .1 Founder $18,000 .4 Developer $40,000 SaaS .5 Web $45,000 9 SaaS Developers Expenses $4000 Developer/Master $540,000 .4 Implementation Staff $32,000 TOTALS $1,028,000 $1,500,000 $147,500 $268,000 Copyright © 2011 Knowledgelevers.com 6
- 31. The Easiest Customer to Capture – The Income from OEM Distributor Licenses –Include Consultation and Integration our basic software Into the OEM’s Database with their offering User Hierarchies (LDAP) Utility Trigger Building Utility Data Wrangling Utility Data Federation Utility Data Data Contributor Download Utility Utility The premise of OEM and Data Distributor pricing is that OEMs and Distributors fold our “Utilities” into their offerings to enable consumers to pull triggered real time notifications from the database and/or for data contributors to push data to federated databases. Copyright © 2011 for Knowledgelevers.com 7
- 32. Our Highest Margin Customer – The Risk Mitigator (Medical and Pharma Research – Homeland Security) Income from Consultation and Integration Into the Risk Mitigation Database straight software sale of our second Notification Hierarchies User Hierarchies (LDAP) (LDAP) Utility stage software Utility Trigger Building Utility Data Wrangling Utility Data Federation Utility Internal Data Blind Contributor Download Contributor Utility Utility Utility The premise of Risk Mitigation Pricing is that the price includes “Utilities” to enable the Risk Mitigator to configure and push secure triggered notifications in real time to users who may not be contributors and for contributors to push data to the federated database “blind.” Copyright © 2011 for Knowledgelevers.com 8
- 33. Everybody Pays to Play in Our Cloud Software as a Service Income – 3% of the price from the seller of the data. Number of search Number of server actions Search transactions triggered Utility Number of VPN Number of users with transactions rights to server actions VPN Number of fields Utility Number of banking included in triggered transactions server actions Number of data Number of users sources involved in receiving Banking notifications exchange Utility Relative weight of the sources of Handshake the data between data Data creators and data Contributor Relative federators Utility value of the data field Data Entry Utility Basic Pricing Incremental Value Pricing Knowledgelevers operates as the data distributor for the Cloud using the tools of the OEM and the Risk Mitigator. The premise of the “Cloud” is that data creators and data federators pay only for actual use of the resource and that fees are configurable, incremental, and transparent. Copyright © 2011 Knowledgelevers.com 9
- 34. Many Products – One Source Code Example - Food Security When a trigger gets tripped the following should occur in real time: 1. A Facebook Page post onto a “Food Safety” page should be generated 2. A Twitter from @FoodSafety should be sent 3. The National Food Safety Website should be updated with an alert 4. An email blast should go to all members of the food product’s supply chain 5. An SMS message should go to all members of the food product’s supply chain 6. SMS and Email alerts should also be sent to all Public Health agencies and EMS units As the FDA or CDC becomes aware of a risk, the management of the risk is automated . Copyright © 2011 Knowledgelevers.com 34
- 35. End of appendix Thank You Contact: Stan@humanpatterns.com 1-803-792-0103 land, 1-919-740-5010 mobile Copyright © 2011 Knowledgelevers.com 35
Editor's Notes
- #3: The Appendix slides are optional if the group receiving the presentation has an interest in more detail.This is a focused article on emerging technology issueshttp://cdn.oreilly.com/radar/2010/06/What_is_Data_Science.pdf.This is a very recent article on large scale trendshttp://www.theaustralian.com.au/australian-it/emc-targets-big-data-in-cloud-push/story-e6frgakx-1226053292958“According to Gartner, global computer data volumes are expected to explode over the course of the decade, rising from about 1.2 zettabytes (10 to the 21 first power) now to 35 zettabytes by 2020.”
- #5: This is a disruptive technology because it reduces costs of enterprise databases and costs for consultation and setup and maintenance fees for these enterprise systems. Any entity that produces and/or consumes data can be up and running within hours. To illustrate the circular reasoning some are still using, consider this recent article.http://tdwi.org/articles/2011/05/18/introduction-to-next-generation-data-integration.aspxAlso consider the older models for data wrangling that are still driving much of the thinking in this space.http://www.digitalroute.com/products-solutions/data-integration/
- #6: KnowledgeLevers.com is a domain name owned by Compages Limited. We have developed and installed secure authenticated multi-user data entry platforms for collecting, scoring, and validating research observations that post encrypted data into a database. As part of that platform, we also have developed an interface for designing and implementing rigorously versioned research protocols. A further development for research was our enabling a “publisher – subscriber” capability for all the protocols and the accumulated data.The current business model does not significantly leverage these assets other than through upgrading current customers with pricing and federation capability. We anticipate that the need for rigorous and secure data accumulation and sharing will increase over time, but we are perhaps still four or more years ahead of the curve and do not think it is time to focus on this asset other than marketing it as just another multiple-sourced data posting tool to use to track and evaluate performance.
- #7: For Clinical Research Organizations – a good links are http://www.acrohealth.org/61 http://www.contractpharma.com/expertopinions/2011/03/16/taking_the_leap%3a_how_the_economic_downturn_has_created_the_best_opportunity_for_innovation_in_decadeshttp://www.bizjournals.com/triangle/stories/2003/06/09/smallb1.html#ixzz1LKBRGgQQRisk mitigators are not just Homeland Security folks, but stock fund managers and insurers. We just happen to have connections to the law enforcement and homeland security community.A great deal of data is captured and entered by people in the course of their workday. Usually this data is entered into spreadsheets or small scale databases that are seldom, if ever, federated into larger datasets where patterns and linkages can be discovered. The advantage of being able to link data that might have utility that was not anticipated when the data was created has a huge potential impact on research of all types. The most obvious of these is the arena of homeland security. Data was distributed in many databases, but the “linking of the dots” that could have prevented 9-11 was done too late. This set of systems and methods will go a long way to enabling real time research and instant notification of emergent risk levels.The enterprise software vendors are also preparing to develop this aspect of the market. Here is SAP’s current approach: http://news.google.com/news/story?ncl=http://www.zdnet.com/blog/btl/sap-in-memory-to-hit-all-applications-collaboration-mobility-in-focus/48857&hl=en&geo=usTwitter is now selling subsets of tweets http://www.businessinsider.com/twitter-starts-selling-its-data-by-the-tweet-2011-2According to Gartner, "By 2015, 75 percent of knowledge-based project work in the Global 2000 will be completed by distributed virtual teams.“ Read more: http://www.sfgate.com/cgi-bin/article.cgi?f=/g/a/2011/06/09/prweb8555582.DTL#ixzz1PBWCWUt3http://venturebeat.com/2010/09/13/startups-find-strong-opportunities-in-3-%E2%80%9Cbig-data%E2%80%9D-markets/http://www.iaventures.com/the-right-investors-for-the-mission#more-232
- #8: "Data is a $100 billion market worldwide," said Pete Forde, founder and chief technical officer at BuzzData, during a session at the O'Reilly Strata conference - http://www.infoworld.com/d/data-explosion/big-opportunities-brewing-in-marketplace-big-data-321Our best positioned competitor is InfoChimps which describes itself as “in the business of curating, housing and providing API access to large data sets” Our approach differs significantly. We focus on the agreement to price and exchange data between the producer and consumer. The curating, housing, and access are simply by-products for us. A point of head to head competition is in the area of discovering relevant datasets. While our technology is different, our objective is the same. http://techcrunch.com/2010/12/14/data-consolidation-infochimps-buys-yc-startup-data-marketplace/Many new entrants are appearing for even smaller niches. An example is http://www.irxreminder.com/As these niche players evolve, they are not always in just one niche. We have categorized them according to their primary functionality, though they will tend to cross over into adjacent arenas.An interesting view of the direction of the business analytics market is http://www.readwriteweb.com/enterprise/2011/01/business-analytics-predictions.phpHere is a link to the major issues related to warehousing (InfoChimps) and Federation (Knowledgelevers. http://semanticweb.com/data-integration-whats-the-way-you-like-it_b20586Congress is beginning to take an interest in protecting the privacy of personal data which may create a new market for folks to sell their personal data, http://www.clickz.com/clickz/news/2068976/online-privacy-bills-hit-congressA major article in Time Magazine http://www.time.com/time/printout/0,8816,2058114,00.html, covers the current status of use of personal data by marketers and a link in that article explores the potential impact of the FTC’s “do not track” option for Internet browsers; http://techland.time.com/2011/03/08/will-ftcs-do-not-track-go-even-further-than-expected/ . A new and interesting entrant into the market is http://www.i-allow.com/, allows users to control who has access to their personal data.Another entrant is Iltellidyn, http://www.intellidyn.com/ which track personal data for marketers.Here is another player in the virtualization space. http://www.compositesw.com/news-events/pages/composite-software-next-generation-data-virtualization-platform-composite-6/Another entrant is http://www.dataflux.com/News-and-Events/News-and-Events-Home/PressReleases/2011-Q2/DataFlux-Energizes-Northern-Virginia-Electric-Coop.aspxBuzzData is a data-sharing hub that emphasizes user visibility and interaction. While several data web services have launched over the last year (DataMarket, Timetric, Infochimps), many of them tend to focus largely on a “broadcast” model of data distribution — in that they compile the data and then offer it to their subscribers, a largely one-way street.The BuzzData team has been greatly influenced by the success and philosophy of Github, and has been building the platform’s infrastructure similarly, with a community-first angle that predisposes users to connect with each other through data, rather than simply connecting to data alone.http://searchdatamanagement.techtarget.com/news/2240037791/HealthNow-picks-Informatica-data-virtualization-over-IBM-and-Composite
- #9: Our IP covers all of the criteria listed above for leveraging data. What if everyone could subsidize their phone by trading their data? What if there were hundreds of real-time data accumulation mobile applications for tracking side effects, or band schedules and venues, or community watch observations?These are links to an Oracle Presentation of a data federation approach and an evaluation of an SAP federation approach that illustrate the unnecessary complexity and of the enterprise databases as they tackle the problem.http://www.oracle.com/us/dm/h2fy11/accelerate-your-business-dis-355070-pt.pdfhttp://www.infosysblogs.com/sap/2011/05/consolidation_with_a_federatio.htmlThis is a link to a discussion of the relative merits of federation versus warehousing. https://semanticweb.com/data-integration-whats-the-way-you-like-it_b20586
- #14: Notice that our first “strength” is the ownership of relevant IP. Development of the tools and methods to implement our IP is not essential for our business model to work. There are many entities that would want to license some or all of our IP and develop tools and methods of their own based on our IP. While we are interested in evolving a business and products based on automating the data supply chain, we would be amenable to licensing our IP and working with others on development and implementation.
- #15: There is a possibility between Stages 1 and 2 of licensing our software to other software companies in niche markets that need an add-on for their own product to provide notification and triggering. Examples of this market are the one-off add-ons to enterprise software packages like Halogen Software. There are probably around 6,000 little firms that might be prospects. We could probably structure a royalty between 10 to 20 percent of their sale price, but we would need to be selective about who we chose to integrate with because of the coding effort to do the integration. Many of these businesses have uncertain long term prospects and that also limits the strategic advantage of folding our technology into theirs.Here is information about a startup that illustrates how social networking can integrate into cumulative datasets.http://www.readwriteweb.com/archives/big_data_gets_big_investment_20m_for_social_sharin.phpHere is a new entrant to cloud analytics/http://www.kdnuggets.com/2011/03/hadapt-big-data-big-analytics-cloud.htmlThis is an interesting article on how Informatica is rationalizing its development agenda. http://tdwi.org/articles/2011/06/15/virtualization-and-data-integration-issues.aspx
- #16: Our IP covers all of the criteria listed above for leveraging data.Real effort is being made to establish standards for pharma research as illustrated by this link: http://www.greatreporter.com/content/semantic-lego-information-framework-drive-drug-discoveryhttp://www.sys-con.com/node/1905992 on how New challenges threaten the reign of enterprise data warehousing http://www.darkreading.com/blog/231001411/federated-data-and-security.html - The value proposition is to be able to bring disparate systems together and consume data regardless of the underlying format and The real trend is for applications to be able to access and analyze different sources regardless of the form data takes.http://radar.oreilly.com/2011/07/the-good-the-bad-and-the-ugly.htmlThis extracted from the O’Reilly post – The implications for our data marketplace is significant!“You'll notice that none of the social networks have subscription options. Nobody says "pay me $100/yr and I'll keep all your data private and you can have an ad-free experience." My hypothesis is that this is because your data is worth more to Google, Facebook, and Twitter than you can justify paying for it: they don't want $100 from you when they can earn $500 or $1,000 targeting advertising to you as you use their sites. They certainly don't have a federation model.”“Nobody's thinking beyond a centralized profit model, either. AdSense made money for small website publishers, who previously didn't have a way to commercialize what they did. Mac App Store has made it so easy to make money from software that people now sell rather than give away. There's no vision in Google Plus to reinvent social networking in a similar platform fashion, creating more value than they capture.”
- #17: Our IP protects many possible pricing schemas. http://www.oracle.com/us/industries/045922.pdfhttp://www.businesswire.com/news/home/20091210005301/en/Research-Markets-Future-Healthcare-Market-2015-%E2%80%93http://www.gsnmagazine.com/article/21107/homeland_security_market_grow_more_5_annually_concMark Sloman, the CEO of the Homeland Security Research Corp. told GSN in a phone interview on July 22 that he and his colleagues were most surprised to realize how ripe with business opportunities are the state and local governments. He acknowledged that penetrating this decentralized market can sometimes present serious challenges, but nonetheless encouraged HLS suppliers to target these potential state and local customers.
- #19: Notice that our first “strength” is the ownership of relevant IP. Development of the tools and methods to implement our IP is not essential for our business model to work. There are many entities that would want to license some or all of our IP and develop tools and methods of their own based on our IP. While we are interested in evolving a business and products based on automating the data supply chain, we would be amenable to licensing our IP and working with others on development and implementation.
- #21: We are seeking a CEO and a Sales and Marketing executive to move us to the next level. We do not intend to permanently hire additional staff in the short term, but will continue to identify and use independent contractors who can work with us for many years.
- #22: This is a link to acquisitions in the database space in 2010 http://www.tdan.com/view-featured-columns/13041These indicates we have anticipated the curve!http://blogs.forbes.com/oreillymedia/2011/01/05/3-big-data-trends-for-the-year-ahead/http://www.dbta.com/Articles/Editorial/Trends-and-Applications/Five-Big-Data-Trends-for-2011-and-Beyond-74595.aspxhttp://nosql.mypopescu.com/post/4321304120/four-bigdata-trendshttp://erwin.com/expert_blogs/detail/seven_big_trends_driving_big_data/http://mmakai.com/post/2472579493/oreillys-3-big-data-trends-for-2011http://tdwi.org/articles/2011/02/02/struggles-with-big-data.aspx“We’re seeing the amount of data that has to be analyzed going from a billion rows of data in a table to 20 or 30 billion rows”
- #25: http://www.ibm.com/smarterplanet/global/files/us__en_us__smarter_computing__ibm_data_final.pdf
- #26: The portions of these markets that relate to our capabilities are only “guesstimates.” The range of the market size may be as low as 40 Billion or as high as 300 Billion. Since we are creating an entirely new marketplace as well as folding into existing markets, we will need to verify and modify our estimates through actual experience. Our competitors usually describe the market as a $100 billion market worldwide. http://www.infoworld.com/d/data-explosion/big-opportunities-brewing-in-marketplace-big-data-321Some blog postings of interest are:http://gigaom.com/collaboration/data-as-a-service-factual-infochimps-google-squared/http://gigaom.com/collaboration/data-as-a-service-factual-infochimps-google-squared/http://blog.datamarket.com/2010/12/08/a-list-of-data-markets/The catch phrase these days is “Big Data.”http://event.gigaom.com/bigdata/
- #27: We envision a real time distributed organic dataset with millions of contributors and millions of users or organizations collecting or contributing only the data they wish to contribute and drawing down only the data they wish to draw down and evaluating that data for the achievement of thresholds configured by users themselves through a user friendly GUI to trigger server actions when thresholds are reached.
- #32: The easiest sale is probably do a large data federator who does not have a need to collect from a widely distributed group of data contributors. Their need is simply for a method to reduce friction between their supply of data and their customer’s need a specific subset of the data. They would simply make a data download Utility available for their customer. Where the significant added value emerges is in their ability to enable triggering and real time exchanges and notifications. Many of these are already in the marketplace, but they typically enable only a push from the Data Distributor, not a pull from the customer or end user. Their motivation build a tool for pulling data was low due to the clumsy design of their trigger building and notification tools and inability to configure unique pricing schemata.Green text indicates a utility or Utility provided to remote or linked users.
- #33: The risk mitigation market is much broader than is initially apparent. Our entry into risk mitigation was as an add-on to multi-rater performance appraisal tools where “early intervention” with a problem employee was a significant benefit from leveraging data already being collected throughout an enterprise. We have already designed a series of loops and response requirements as well as additional triggered server responses that enable automatic escalations of notifications upline in an organization or initiation of programs to start or shut-off applications. This enables a rigorous chain of accountability as well as some “fail safe” capability, such as turning off or turning on an business process that could significantly impact an organization’s risk exposure.Other applications of the real time alerting capability inherent in our systems and methods can be as simple and useful as a dashboard posting to a CFO when a budget line item cost is exceeded by any department anywhere within an enterprise.
- #34: The Cloud application is the more complex end-point that utilizes all the inherent capacity of our systems and methods. At that point any Data Federator, Data Distributor, Risk Mitigator, and Data Creator can participate in a full and comprehensive data supply chain where payment and activity is fully automated and requires little setup effort.