knowledgelevers expanded
DESCRIPTION
Evolving a data supply chain and disrupting the Google model of ignoring data ownership and the Facebook model of co-opting data ownership. The data supply chain model assumes the person or the owner of the device that creates data is the owner of that data and should have the right to trade in in an open marketplace.TRANSCRIPT
Knowledgelevers
Presentation to Investors – December 2011
Unlocking Value in Data“The future belongs to the companies and people that turn data into products”
O’Reilly Radar Report
1. Mission2. Executive Summary3. Knowledgelevers4. Data Exchange 5. The Data Federation and Exchange Space6. Job To Be Done7. Knowledgelevers Tool Sets 8. IP Protection for Knowledge Levers and Derivative Applications I9. IP Protection for Knowledge Levers and Derivative Applications II10. Upside Potential11. Differentiators12. Staging Our Income Pyramid13. Facilitating Data Trading 14. Traders Need Tools15. Tools and Development Progress16. Strengths - Needs - Risks17. Our Founder18. Evolving The Team19. Exit Strategy20. Bottom Line and Summary Appendix
Mission
Disrupt enterprise data products through “just in time” notifications for CRM, Supply Chains, and Business Intelligence.
Copyright 2011 Compages
Data is the “oil” of the 21st century
Copyright © 2011 for Knowledgelevers.com 1
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.
Robust IP with supportive prototyping
Concept and technology validated by currently working installations
3.5 Million invested into software and IP
A multi-billion dollar opportunity
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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
Effort to identify which data to buy or sell.
Need for actionable intelligence for risk assessment and
competitive advantage Resistance
Opportunity
Diverse market for buyers of data. Diverse producers of data who want to sell it.
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Data Exchange
Data Federators and
Distributors Gallup, Gartner –
Distribute the right data to customers
Data Accumulators and AggregatorsCritical Research Enterprises - Cut losses
from useless research and liabilities from missed indicators.
Data Based Risk MitigatorsStock Fund Managers or Homeland Security
– Notify the right person as the dots get connected.
Data Creators and ProducersAll businesses,
especially retailers and
financial institutions – Sell
fallow data to buyers.
A Market in Search of a Trading Platform
Copyright © 2011 Knowledgelevers.com
Warehousing and Linking for
Specialized Data Exchange
Visualization and Computation
Data Transformation
Cloud Apps, Appliances,
Management and Storage
Business Intelligence
Suites
Consulting Odd Fellows Analytics, Extraction,
Collaboration
The Data Federation and Exchange Space
4
Customers or Potential
Competitors
Joint Venture Partners
Channel Partners Channel Partners or Competitors
OEM Outlets Sales Outlets Joint Sales
Nobody in the space has monetized automated chains of dataor triggered actions.
Node51
5
Job To Be Done
Be the global leader for brokering actionable data in real time.
Problem SolutionData exchange is constricted due to
No effective marketplace for offering or discovering data
No easy way to buy or sell
No easy way to determine a price
Multiple data formats
No standardized data updates
No standardized tools for triggering actions based on data
Software and infrastructure to
Post/offer and discover data to a central location
Establish standardized data exchange PRICING agreements
Provide a mechanisms for supply-side or demand-side pricing
Collect and federate data in real time or bypass federation
Enable updating and event triggering
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
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Risk Reduction and CRM
Connection Tools
Calculation Tools
CombinationTools
CommunicationTools
Knowledgelevers Tool Sets
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Big Picture: Patent methods and systems involving pricing and fees associated with data trading. Protect prices and fees for Gateways to Datasets1. Transmission from electronic devices like Smart Phones that offer GPS locations
and point of sale transactions2. Enrollment into data trading venues through data strings like Matrix Codes, RFID
tags, and direct to web services connections3. 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 Datasets4. Iterative additions to a dataset5. Alternate versions of a dataset 6. Immediate utility of the data format (Data Item Pair)
IP Protection for Knowledge Levers and Derivative Applications I
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Protect prices and fees for Interaction with Datasets1. Setting up triggers to initiate server actions upon changes in a dataset2. Tracking interaction with a GUI associated with a dataset3. Linking enrollees (contributors) to data protocols and associated datasets4. Linking recipients of reports or server actions to data protocols and
associated datasets Protect prices and fees for assigning Value to a Data Item5. Popularity of the item 6. Reputation of the source for the data7. Importance of the item relative to other data items Protect prices and fees for Financial Transactions Involving Data8. Uploading data to a parent dataset9. Use of validation keys to connect contributors with financial institutions10.Enrollment of a new contributor or recipient into a data supply chain
IP Protection for Knowledge Levers and Derivative Applications II
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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
Upside Potential
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Differentiators
Competitors
Databases – “Big” data
Data Federation and Aggregation
Data Transformation and Analysis
Data Mining
Data Storage and Warehousing
Software Sales and Consulting Income
IT Departments - Centralized Management
Siloed by Organization or Function
Scheduled
Value Proposition is “Organized Data”
Knowledgelevers
Data Items – “Small” data
Data Chains, Streams, Combinations
Data Assessment for Actionable Value
Data Triggering and Notifications
Forward and Backward Redistribution
Transactional Income
Local End Users - Distributed (Individual Users)
Socially Networked
Real-time
Value Proposition is “Actionable Information”
We understand and can match our competitors capability and technology, but we are the first “transactional and actionable ” data firm – hence our name – Knowledgelevers.
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SaaS Data Market
SOFTWARE -Direct to Researchers & Enterprise Risk
Managers
OEM LICENSES - for Data Distribution Businesses
SHAREWARE - Self-service Consumers - to set up exchanges, wrangle data, trigger actions and notifications
CURRENT CUSTOMERS - Expanded sales of upgraded Employee Performance and Risk Management Software to the public sector and
hospitals.
Staging Our Income Pyramid
Stage 4VC Capital
Stage 2Skip ifVC capital
Stage 3Skip if VCcapital
Stage 1Beta testingand validation
Stage 5
100,000 Buyers $80,000 per sale 15% Maintenance Continuous Income
10 Million Users for 200 Billion Data Points $.01 per field/3% transaction – Continuous income
500,000 Buyers $99 each
3,000 Licensees $50,000 per license
125,000 Buyers $25,000 per sale 15% Maintenance Continuous Income
Year 1 $268,000
Year 2 $4,000,000
Year 3 $32,000,000
Year 4 $246,000,000
Year 5 = Exit at $500,000,000 to
$800,000,000
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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
Facilitating Data Trading
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Traders Need Tools
Implement a data marketplace to automate uploading and downloading, pricing, payment, and action upon data in real time.
.
Enable fees and charges for exchange and payment process Device uploads and downloads
Payment and transaction toolsMembership fees, activation fees, convenience fees, subscription fees, volume discounts
Easily input pricing variables to enable fair compensation or reciprocity for data
Price per question and answer pair Price per field
Contributor reputation rating Popularity rating
Specific utility (rarity/recency/compatibility)
Automated actions
User friendly and secure applications to monetize data
Universally post and exchange data Security and authentication for data transport
Data is most valuable as and when it changes.
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2006 Patent Application
● Research & Analysis● Business Model Created● Cost out Development Agenda
2009 Architect Prototype
● Recruit Developers● Fold in Legacy Software● Confirm Customer Need ● Establish Coding and Design
2010 Expand Patent Protection
● Monetize Weighting● Monetize Handshakes● Monetize Popularity and Recency● Monetize GUI● Embed Systems, Tools, and Methods into IP
2012 Prepare for Growth
● Complete Prototypes● Fold Legacy Applications together with Prototypes● Up-sell current customers● Secure Venture Capital/Partners● Expand Management Team● Further Design and Protect Methods for Data Pricing and Exchange
Tools and Development Progress
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Strengths – Needs - Risks
Strengths
Ownership of IP - defensible competitive position
Design and implement flexible/modular software architecture
Unique database design with supporting code
Data administration capability and experience
Loyal customer base for current software - receptive to upgradingPassion for data and its potential to improve and change lives and reduce risks
Needs
Expand senior management team to drive growth
Sales and marketing skill and capacity
Financial backing to fund development
Cultivate strategic partnerships
Recruit and organize development team
Experience scaling
Risks Mitigation
Ownership of data not attributable – unclear data rights
Retain focus on High Risk Researchers and Risk Managers – grow through OEM rather than SaaS
Patents not enforceable or not issued First mover advantage
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» software company automating survey research » survey research instrumentation
» Human Patterns - a psychometric tool which now has a network of over 200 Certified Administrators » applied in hundreds of businesses, universities, and organizations
» Ensera (acquired by ADS)» Applied Biosystems (developed the code to drive the equipment for the Human Genome Project)» Propellerhead Software (acquired through a chain of acquisitions by Symantec)» Alliance One (initiated and spun off alert® Food Safety Alert System)» Workplace Options (implemented “Network Advantage” support systems for EAP’s)
» real time data supply chain software company» 7 current installations doing performance evaluation and risk management
Multi-year consulting
engagements with startups involved in
data supply and research automation
Developed many psychometric and
survey instruments
Converted The Human Factor into
Human Patterns 1998
Converted Compages into the
Human Factor 1983
Founded Compages Limited 1980
» data driven systems » organization intervention consultation business
Our FounderStan Smith
Copyright © 2011 for Knowledgelevers.com
The Evolving Team
17
Person Role ExperienceTo Be Identified CEO
Adam Chasen Architecture Product Development
rPath Systems Automation
To Be Identified VP Sales and Marketing
Reed Altman COO, Implementation andTraining, Customer Relations, and Software Maintenance
Involved in first iteration of our data design and approach. Long term customer relationships on strengths of our technology and maintenance.
To Be Identified Exhibition Sales and Marketing
Joseph Tate Python DeveloperSaaS Developer
Developed patent for data form conversions
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Exit Strategy
We can generate a valuation of >$500 million in 5 years
Sale to major enterprise software vendors
Multi-billion $ behemoths with
capacity and cash to buy
All improve position by offering a platform
to trade the world’s most ubiquitous
commodity! DATA
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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."
First mover advantage with proof of concept implemented
Fundamentally changing the exchange and sale of data
Business model is highly efficient and scalable
Large market; recurring revenue stream
Defensible IP and team with deep domain expertise
Seeking $3-$5 M funding for management and development expansion
Copyright © 2011 Knowledgelevers.com 20
Thank you!
Contact:[email protected] land, 1-919-740-5010 mobile
Copyright © 2011 Knowledgelevers.com
APPENDIX“90 % of all data has been generated in the last 2 years”
IBM
1. The Size of Market2. IP to Revolutionize Data Trading3. Secret Sauce – New Technology4. Code and Architecture for Data Production and Consumption 5. Sales Divisions and Markets6. Budget Projection for First Year 7. The Easiest Customer - The Distributor8. Our Highest Margin Customer9. Everybody Pays to Play in Our Cloud10. Many Products – One Source Code
Copyright © 2011 Knowledgelevers.com 1
The Size of the Market
Non-CRO Researchers
Data Integrators
Risk Managers
Clinical Research
Consumers
0 20 40 60 80 100 120 14025
16
84
20
123
20
12
50
16
50
5
6
25
8
30
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 BillionOur Worst Case Estimate of our share of the total market = $25 Billion
Graph shows numbers assuming larger market.
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Process Patent Number or Application Number
Defensive Value Offensive Value
Discovering Data 7,860,76012/930/280
High High
Building a User and Contributor Hierarchy 7,860,76012/932/798
Low High
Formulating an Exchange Agreement 7,860,76012/930/28013/134,596
Med Med
Assigning Data Access Rights and Roles 7,860,76012/932,79812/932,797
Low Med
Federating Data 7.860,76013/134,596
High High
Uploading Data from Devices, Message Services, RFID Tags and Transmitters
13/134,596New application not assigned a number
High High
Pricing Parsimonious Data 13/135,420 High Mod
Folding Data into Triggers 7,860,760 High High
Assigning Value 7,860,76012/932,79812/932,797
High High
Setting Chains or Loops for Server Actions 7,860,760 Low High
IP to Revolutionize Data Trading
Copyright © 2011 Compages Limited for Knowledgelevers.com
3
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 datasetAct 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
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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 sourcesReal time forward and backward chaining of computer driven server events based upon calculated thresholds or valuesEncryption 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
Code and Architecture for Data Production and Consumption
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BUSINESS DIVISION
MARKET AVERAGE SALE AVERAGE IMPLEMENTATION OR SERVICE COST
SALES METHOD
COST PER SALE
RECURRING INCOME
Employee Performance and Risk Management
Public sector (Law Enforcement) and hospitals
$25,000 $6,000 Conferences and Exhibitions
$3,000 15%
Shareware Sales – if VC funding not obtained
Web Users $99 $2 SEO and Shareware Outlets
$3.50
Joint Ventures with Niche Data Federators
Patent enforcement FUD and cooperative alliances
Unknown Unknown Patent Infringement Attorney
$0 Potentially
OEM Licenses Data Vendors and Buyers
$50,000 $6000 Direct Sales $3000 Variable
Software Hooking into Enterprise Software
Risk Managers $80,000 $3000 Direct Sales $3000 Variable
SaaS Anyone Variable $1 Subscription $1 Variable
Sales Divisions and Markets
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Business Unit Employee Allocation Employee Cost Contractors for Rapid Ramp Up to Stage 5
Expenses Sales Income
Administration – Architecture-Investor Relationships
.7 Founder
.4 CEO
.1 Sales and Marketing Manager
.3 Software Architect
$126,000$72,000$12,000
$60,000
Infrastructure $16,000Office and Phone$8,000
Employee Performance and Risk Management
.3 Sales and Marketing Manager
ExhibitorDemonstration /Closer.5 Implementation Staff
$40,000
$65,000$85,000
5 .NET Developers $300,000
Travel $15,000Conferences $40,000
$100,000
Shareware Sales .5 Web Developer/Master.1 Implementation Staff
$45,000
$8,000
6 Python Developers $360,000
Expenses $4,500 $88,000
Joint Ventures with Niche Data Federators
.2 CEO
.3 Sales and Marketing Manager.2 Founder.4 Developer
$36,000$40,000
$36,000$40,000
Travel $15,000 $50,00
OEM Licenses .2 CEO.2 Founder.4 Developer
$36,000$36,000$40,000
Travel $15,000 $80,000
Software Hooking into Enterprise Software
.2 CEO
.3 Sales and Marketing Manager.1 Founder.4 Developer
$36,000$40,000
$18,000$40,000
5 Enterprise Developers $300,000
Expenses $15000
SaaS .5 Web Developer/Master.4 Implementation Staff
$45,000
$32,000
9 SaaS Developers $540,000
Expenses $4000
TOTALS $1,028,000 $1,500,000 $147,500 $268,000
Budget Projection for First Year
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The Easiest Customer to Capture – The Distributor
Consultation and Integration Into the OEM’s
Database
Data Federation Utility
Data Contribu
torUtility
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.
Data Download
Utility
Income from OEM Licenses –Include our basic software with their offering
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User Hierarchies (LDAP) Utility
Consultation and Integration Into the Risk
Mitigation Database
Data Federation Utility
Internal Contributor Utility
Our Highest Margin Customer – The Risk Mitigator (Medical and Pharma Research – Homeland Security)
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.”
Data Download
Utility
Income from straight software sale of our second stage software
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Everybody Pays to Play in Our Cloud
Number of server actions triggered
Handshake between data
creators and data federators
Number of search transactions
Relative weight of the sources
of the data
Relative value of the data
field
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.
Data Contribu
tor Utility
Banking Utility
VPN Utility
Data Entry Utility
Search Utility
Software as a Service Income – 3% of the price from the seller of the data.
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When a trigger gets tripped the following should occur in real time:1. A Facebook Page post onto a “Food Safety” page should be
generated2. A Twitter from @FoodSafety should be sent3. The National Food Safety Website should be updated with an alert4. An email blast should go to all members of the food product’s
supply chain5. An SMS message should go to all members of the food product’s
supply chain6. 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 .
Many Products – One Source Code Example - Food Security
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End of appendix
Thank You
Contact:[email protected] land, 1-919-740-5010 mobile