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Ayasdi Case Study

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Ayasdi (ai-yaz-dee), a Silicon Valley start-up, has created technology that may prove to redefine an entire industry. Ayasdi provides a highly differentiated platform for data analysis based on the concept of Topological Data Analysis, first documented in the 1700’s – a platform that has the potential to shift the direction of future technology development. This case study briefly explores the “Big Data” industry as it is today, and the future implications that Ayasdi may have on the industry; including the strategic challenges Ayasdi has in positioning themselves as a contender and prospective leader within the “Big Data” and Enterprise Technology market segments.

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Ayasdi Case Study

  1. 1. Ayasdi: Demystifying the Unknown Jessica Marie and Craig Morgan: Saint Mary's College of California Executive MBA Ayasdi (ai-yaz-dee), a Silicon Valley start-up, has created technology that may prove to redefine an entire industry. Ayasdi provides a highly differentiated platform for data analysis based on the concept of Topological Data Analysis, first documented in the 1700’s – a platform that has the potential to shift the direction of future technology development. This case study briefly explores the “Big Data” industry as it is today, and the future implications that Ayasdi may have on the industry; including the strategic challenges Ayasdi has in positioning themselves as a contender and prospective leader within the “Big Data” and Enterprise Technology market segments. D i s c o v e r w h a t y o u d o n ’ t k n o w
  2. 2. Page | 2 TABLE OF CONTENTS 1 | Abstract 3 | Early Beginnings 4 | Changing the Paradigm of Data Analysis 4 | Data Science and Domain Expertise: Ayasdi’s Specialties 5 | Traditional Analytics 5 | Topological Data Analysis 7 - 10 | Topological Data Analysis in Action 11 | Ayasdi’s Iris Insight Discovery Platform: Advancing Machine Learning and TDA 11 - 12 | The Demand for Data Scientists and Domain Experts 12 - 13 | The Challenge 13 - 14 | The "Big Data" Sea 14 - 15 | The Road Ahead: The CEO’s Dilemma 16 - 17 | Exhibits 18 | Bibliography
  3. 3. AYASDI: DEMYSIFYING THE UNKNOWN Early Beginnings Ayasdi (ai-yaz-dee) means “to seek” in Cherokee, an adapt name for a company whose mission statement is to help organizations make groundbreaking discoveries that lead to rapid innovation, faster growth, increased cost savings, and perhaps most importantly, saving lives through breakthroughs in translational medicine – by “seeking” and solving complex data relationships. Ayasdi was officially founded in 2008 to bring a revolutionary new approach to solving the world’s most complex problems after a decade of data modeling research at Stanford, DARPA and NSF.1 However, the roots of this research can be traced back to the 1970’s, when Gunnar Carlsson, Harlan Sexton and Benjamin Mann met as PhD students at Stanford’s mathematics program. Over the next 30 years, they conceptualized computational topology and Topological Data Analysis (TDA), which today is the foundation of Ayasdi’s technology.1 In the 1980’s, Gunnar Carlsson joined Harlan Sexton on a project to apply algebraic methods to parallel computation in signal processing. Later that decade, US government research agencies DARPA and NSF awarded $10M in grants to Stanford, with Gunnar Carlsson as the principal investigator, to apply TDA to real world problems. This effort was assisted by Ayasdi co-founder Page | 3 Harlan Sexton. In 2005, while Ayasdi’s CEO, Gurjeet Singh, was a Ph.D. mathematics student, he joined Stanford professor Gunnar Carlsson on the TDA research project and created the first software program (Mapper) applying Topological Data Analysis, which in 2008, led Gurjeet, Gunnar and Harlan, to found Ayasdi to commercialize their research.1 Since 2011, Ayasdi has received seed funding and Series A and B funding rounds through Floodgate, Khosla Ventures, GE Ventures, IVP, and Citi Ventures. 1 "Ayasdi Was Started in 2008 to Bring a Groundbreaking New Approach to Solving the World's Most Complex Problems after a Decade of Research at Stanford, DARPA and NSF." Ayasdi. N.p., n.d. Web. 01 Sept. 2013. <http://www.ayasdi.com/company/>.
  4. 4. Changing the Paradigm of Data Analysis What does ‘data’ mean to you? Perhaps you think of numbers. Perhaps you think of statistics or facts collected for reference or analysis. Or maybe you are reminded of the massive streams of social media feeds. But have you ever considered that data has shape?2 “Big Data”, an artifact of modern business, with massive amounts of data being produced and acquired faster than ever before, represents a significant organizational asset. Yet, simply gathering huge quantities of data and applying random statistical analysis can lead to incomplete results. Powerful computers and sophisticated statistical algorithms are able to process data to find patterns and trends, but they cannot explain the data, nor make predictions that make sense. How is technology transforming this process? Data Science and Domain Expertise: Ayasdi’s Specialties In the past, data scientists were found in finance and actuarial professions. Many of them were trained statisticians and mathematicians who eventually found themselves in C-Level positions. Today, we find data scientists in nearly every industry – many of them moving forward to become domain experts, specializing in life sciences, genetics, physical sciences and financial services, with academic and applied research backgrounds. Today’s data scientists, like diamonds, are multifaceted and rare. They have a unique combination of skills that go beyond traditional mathematics and statistics. However, while they do possess highly technical knowledge consisting of computer science, programming, advanced algorithms, and statistics, they don’t necessarily possess the domain expertise, and lack an understanding of the complex nuances of the data. Each day, job boards are filled with new listings of opportunities for aspiring data scientists, while news headlines tell us about new discoveries made by these individuals. And today, we are seeing well-established, large enterprises making huge investments in data scientists. General Electric has become very serious about big data, investing in software development and data scientists, to develop predictive analytic models. Citi has invested in data scientists to help automate the data-to-value process, while Merck analyzes datasets for drug discovery and genomic research. Imagine if companies had the opportunity to become a 'data company', one that is data driven and leverages the talents of data scientists and advanced technology to extract actionable insights from massive datasets. Those companies would indeed have a sustainable competitive advantage, empowering the entire organization to exceed its business goals. Page | 4 2 Marie, Jessica. "Ayasdi: Capitalizing on the Shape of Data." Web blog post. Inside Analysis. N.p., 14 Mar. 2013. Web. 01 Sept. 2013. <http://insideanalysis.com/2013/03/ayasdi-­‐capitalizing-­‐on-­‐the-­‐shape-­‐of-­‐data/>.
  5. 5. Traditional Analytics Existing analytical methods of studying data often consist of asking specific questions of your data. We are taught that in order to find actionable insights, we must have an analytics engine that requires codes, queries and models. With Business Intelligence tools, Mathematics Software and traditional databases, the data capacity ranges from low to high, yet the time to insights can take months. In addition, these methods require the user to understand and use codes, queries and models to mine the data for insights.3 What are you supposed to do when the datasets are so massive and diverse that you don't know what to ask? Or even where to begin? Topological Data Analysis The Ayasdi’s Iris platform for Topological Data Analysis, uncovers hidden data relationships in massive and diverse datasets – a paradigm shift juxtapose traditional analytical approaches of data analysis. At its core, topology is the mathematical study of shapes and spaces. It is a major area of mathematics concerned with the most basic properties of space, such as connectedness, continuity and boundary. It is the study of properties that are preserved under continuous deformations including stretching and bending. Topology developed as a field of study out of geometry and set theory, through analysis of such concepts as space, dimension, and transformation.4 Topology has its history beginning with The Seven Bridges of Königsberg, a well-known problem in mathematics: find a way to pass through the city while crossing each bridge only once. Mathematician Leonhard Euler proved this to be impossible in 1735, and in doing so invented graph theory. In 1736, he published a paper on the solution to the Königsberg bridge problem entitled Solutio problematis ad geometriam situs pertinentis, which translates into English as “The solution of a problem relating to the geometry of position.” Graph theory was a new type of geometry that considered shape but not specific dimensions, and from this evolved the mathematical field of topology. In brief, topology is the mathematics of relationships within space. It deals with properties of shape that are preserved under deformation caused by bending and stretching.5 The ultimate goal of data analysis is to obtain insights and knowledge. Traditional methods involve data warehousing, data marts and a plethora of analytical approaches. As the world 3 Exhibit A 4 Carlsson, Gunnar. "Topology and Data." BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY, 29 Jan. 2009. Web. 20 Aug. 2013. <http://www.ayasdi.com/_downloads/Topology_and_Data.pdf>. 5 Marie, Jessica. "Ayasdi: Capitalizing on the Shape of Data." Web blog post. Page | 5 Inside Analysis. N.p., 14 Mar. 2013. Web. 01 Sept. 2013. <http://insideanalysis.com/2013/03/ayasdi-­‐capitalizing-­‐on-­‐the-­‐shape-­‐of-­‐data/>.
  6. 6. becomes larger and more complex, and digital data continues to expand, there are going to be new problems to solve and old problems that deserve better solutions. Solving those problems may require a new approach to analyzing data: the application of qualitative methods, as well as, quantitative methods. Ayasdi has taken this historical discovery and advanced its use for analyzing data. Think of it like this… Each group of data is a node, and when multiple nodes are connected, a visual network of the data emerges (see below). This topological network exposes relationships that correspond to patterns in the data. And from those patterns knowledge can be extracted. We are not looking at datasets any more, but at the shape to the data. This is the essence of topology.6 Fig 1: Ayasdi Iris showing a Topological Data Analysis on diverse datasets Ayasdi’s fundamental innovation is to employ topology to provide meaningful insights about data. The Topological Data Analysis it employs embodies a geometric approach to pattern recognition within data. Being able to recognize those patterns is important to finding meaningful insights about data groups and sub-groups. Topological Data Analysis is a revolutionary method for analyzing and discovering important relationships within datasets. The bottom line is this… Ayasdi has created technology that is query-free, model-free and code-free, which makes it a solution with many applications to real-world problems. It’s technology and approach is already Page | 6 6 Marie, Jessica. "Ayasdi: Capitalizing on the Shape of Data." Web blog post. Inside Analysis. N.p., 14 Mar. 2013. Web. 01 Sept. 2013. <http://insideanalysis.com/2013/03/ayasdi-­‐capitalizing-­‐on-­‐the-­‐shape-­‐of-­‐data/>.
  7. 7. being used in a variety of industries, such as life sciences, manufacturing, sports, and financial services.6 Topological Data Analysis in Action Many companies are using TDA to uncover insights that traditional analysis would not have been able to solve without large amounts of time and money. Topology is already being used in a variety of industries from financial services for fraud detection to Pharmaceutical companies for cancer research – the application possibilities are endless. “We’re excited by Ayasdi’s unique capability to let users find insights automatically from large, complex data sets. Their ability to abstract away complexity thereby making powerful machine Page | 7 learning tools accessible to ordinary business users is particularly promising.” – Ramneek Gupta, Managing Director, Citi Ventures7 Using Credit Card Data for Customer Segmentation Financial institutions can use credit card data to identify their most desirable clients, as well as, develop retention strategies for current clients. Ayasdi's technology makes it possible to automatically map new customers and datasets, and ultimately classify and manage risk. In this specific example, Ayasdi analyzed transaction sequences from over 100,000 credit card users, and identified over 90 unique patterns that clustered into distinct customer groups. Each group displayed distinct spending and borrowing patterns. Understanding these groups allowed the company to better target marketing and product offerings to the best creditors, while identifying groups who posed a potential credit risk.8 7 "Our Work with the World's Leading Organizations." Ayasdi. N.p., n.d. Web. 05 Sept. 2013. <http://www.ayasdi.com/customers/>. 8 "Customer Segmentation from Credit Card Transaction Data." Ayasdi. N.p., n.d. Web. 05 Sept. 2013. <http://www.ayasdi.com/product/deployment/customer-­‐segmentation.html>.
  8. 8. The TDA analysis of credit card data resulted in better customer segmentation through automatically segregating customers and assigning risk rating to each group based on specific interactions and information. This enabled the financial institution to develop a precise strategy for reducing costs associated with customer churn by 3-10%.9 Fraud Detection Failure to detect fraudulent transactions can cost companies millions to billions of dollars each year. It must be analyzed by a robust system in real-time. Perhaps the most challenging aspect of detecting fraud is that the strategies of perpetrators are constantly changing, as they continue to exploit loopholes in organizations defenses, therefore organizations must remain vigilant and on the defensive. With Ayasdi, fraud detection becomes tactical and vigorous, allowing for the automatic discovery of anomalies. What follows is an example of purchase data from an online retailer that demonstrates how Topological Data Analysis is capable of distinguishing fraudulent transactions based on highly dimensional machine data. Using ground truth from over 5,000 known fraudulent transactions, Ayasdi identified new patterns of fraud in a dataset containing 600,000 transactions; this detection has historically been done by writing complex queries, requiring extensive coding. However, with the advancement of Ayasdi's Iris Platform, these precarious patterns automatically surface. Page | 8 9 "Customer Segmentation from Credit Card Transaction Data." Ayasdi. N.p., n.d. Web. 05 Sept. 2013. <http://www.ayasdi.com/product/deployment/customer-­‐segmentation.html>.
  9. 9. Biomarker Discovery Public data sources such as, The Cancer Genome Atlas10 are invaluable for launching new initiatives to research and develop new drug therapies. With Ayasdi, Life Science researchers can leverage advanced mathematics and machine learning to extract valuable patterns without writing a single line of code. The following example demonstrates how Topological Data Analysis (TDA) is able to quickly stratify groups based on topological networks built from expression and point mutation data. Using TDA, Ayasdi was able to identify specific genes and biomarkers that characterize subtypes enabling researchers to identify genetic pathways that correspond to different subtypes. Page | 9 10 "Biomarker Discovery from Expression and Sequencing Data." Ayasdi. N.p., n.d. Web. 01 Sept. 2013. <http://www.ayasdi.com/product/deployment/biomarker-­‐discovery.html>.
  10. 10. The Topological Pattern shown above show how easy it is toggle between expression and mutation networks. With TDA, the data can be segmented into meaningful groups and relevant patterns can be found. In this particular example, the researcher was able to find gene pathways that are influenced by patterns of expression and genetic variance.11 Targeted Marketing Strategies Topological Data Analysis has also used to identify customers and target markets for marketing campaigns. By analyzing and recognizing patterns in mobile application usage from 1 million users and 200 mobile applications, Ayasdi has been able to segment mobile users by purchase patterns, geo-location, and time spent using each application. These insights resulted in initiating targeted Page | 10 advertising campaigns that are predicted to increase advertising pipeline by 15%.12 11 "Biomarker Discovery from Expression and Sequencing Data." Ayasdi. N.p., n.d. Web. 01 Sept. 2013. <http://www.ayasdi.com/product/deployment/biomarker-­‐discovery.html>. 12 Solutions Use Cases for the Brilliant Enterprise. "Structuring Precise Marketing Strategies From Mobile Log Files Ayasdi. N.p., n.d. Web. 28 Aug. 2013. <http://www.ayasdi.com/solutions>.
  11. 11. Ayasdi’s Iris Insight Discovery Platform: Advancing Machine Learning and TDA Ayasdi Iris is a powerful data-visualization platform that utilizes TDA to highlight the underlying geometric shapes in data and allowing for real-time interaction to produce immediate insights by autonomously finding abstract connections – either distinct patterns or anomalies within data.13 Iris is offered as a multi-tenant cloud or as an on-premise solution14 (multitenancy refers to a software architecture where a single instance of the software runs on a server, serving multiple client-organizations (tenants)), capable of working with both public and proprietary datasets. Iris uses hundreds of algorithms and TDA to mine huge disparate datasets before presenting the results in a visually accessible way, which can be manipulated by researchers. The machine learning algorithms include unsupervised, supervised, semi-supervised learning and statistical tests – tied together using TDA. Using algebraic topology, Iris automatically shifts through huge, disparate, multiple datasets15 to assimilate data points close in nature and then maps these out to reveal a network of patterns for a researcher to decipher – closely related nodes of information will be connected and clustered together – thereby illuminating patterns and relations between data points. According to Gurjeet Singh, Co-Founder and CEO at Ayasdi, “The answers to today’s most important scientific, business and social problems lie in data. The biggest challenge in big data today is asking the right questions of data. There are so many questions to ask that you don’t have the time to ask them all, so it doesn’t even make sense to think about where to start your analysis. The power of Iris is its unique ability to automatically discover insights – regardless of complexity – without asking questions. Ayasdi’s customers can finally learn the answers to questions that they didn’t know to ask in the first place. Simply stated, Ayasdi is ‘digital serendipity’.”16 Indeed, Iris' unique and proprietary architecture14 removes the human element that goes into data mining – and, as such, the associated human bias. By providing an intuitive platform that is query-free, model-free and code-free, researchers are freed from the burden of having to formulate a question, as the system will – undirected – deliver patterns a human might not have thought to look for – now that’s not only insightful, but clever. The Demand for Data Scientists and Domain Experts Innovation is transforming the way we obtain insights from data. Everyone now has access to the unique skills necessary to analyze data. And because of this, the idea of the data scientist is rapidly changing. Page | 11 13 "The Ayasdi Platform." Ayasdi. N.p., n.d. Web. 01 Sept. 2013. <http://www.ayasdi.com/product/>. 14 Exhibit C. 15 Exhibit B. 16 "No Questions Asked: Big Data Firm Maps Solutions without Human Input." Wired UK. N.p., n.d. Web. 20 Aug. 2013. <http://www.wired.co.uk/news/archive/2013-­‐01/16/ayasdi-­‐big-­‐data-­‐launch>.
  12. 12. Connections between data and the problem are not always obvious. Currently, domain experts work directly with data scientists to derive insights from their data. However, with advancements in technology, domain experts are able to leverage advanced data analysis techniques to find insights faster. By augmenting their current skills with technology they will be able to increase productivity and tackle problems more efficiently and effectively. With the right technology, domain experts will have greater expertise and theoretical understanding to infer conclusions, as well as, find clear and effective ways to communicate their findings. Empowering a new breed of business strategists and innovators. Advancements in technology, combined with the knowledge and experience of data scientists and domain experts, will transform how organizations currently use data to solve problems, generating significant revenue opportunities by solving complex and expensive problems that ultimately enrich our lives. The Challenge With the latest Series B investment of $30.6 M secured in 2013, Ayasdi aims to accelerate the development of machine learning systems and TDA-based approaches to help organizations achieve brilliant outcomes. Page | 12 Ayasdi’s strategic course (as outlined by Singh):17 o Continue automation of Ayasdi’s machine learning techniques to discover insights from complex data in a matter of seconds; o Enhanced operational workflow capabilities for integrating Ayasdi into core enterprise IT environments and real-time business operations; o Access to preloaded public datasets providing immediate insights for enterprises to pair with their proprietary datasets; o Doubling the size of the company within the next 12 months. But these ambitions are not without challenges. Ayasdi not only has advanced technology, it is the only one in the “Big Data” race that uses Topological Data Analysis to gain insights from data. With fierce competition in this market, how does this Silicon Valley startup differentiate themselves from the vast array of big data companies? 17 Institutional Venture Partners. Ayasdi Raises $30.6 Million in Series B Funding from Institutional Venture Partners (IVP), GE Ventures, and Citi Ventures. IVP: Institutional Venture Partners. N.p., n.d. Web. 25 Aug. 2013. <http://www.ivp.com/news/press-­‐release/ayasdi-­‐raises-­‐-­‐30-­‐6-­‐ million-­‐in-­‐series-­‐b-­‐funding-­‐from-­‐institutional-­‐venture-­‐partners-­‐-­‐ivp-­‐-­‐-­‐ge-­‐ventures-­‐-­‐and-­‐citi-­‐ventures>.
  13. 13. This challenge extends beyond simply reeducating the market and developing clever PR strategies. It involves the task of nearly overhauling the market all together, changing attitudes, shifting the paradigm of traditional data warehousing, data processing and data analysis. The rise of this type of technology is important because it has the potential to shift the entire industry, and therefore shift the direction of future technology. Just as virtualization and cloud computing have dismantled the technology industry’s use of hardware, Topological Data Analysis has the potential to outperform the competition and possibly even eliminate the need for current market technology. What core competencies should they leverage in order to gain a competitive advantage? How will Ayasdi cross the chasm to become the dominant design? Should Ayasdi focus on the technology? It’s ease-of-use? Or emotional appeal stemming from innovative uses of the product (i.e., drug discovery)? Ayasdi is also competing against well-established companies that have literally hundreds of millions of dollars to spend on marketing – Ayasdi’s technology represents significant risks to their revenues by making their technologies obsolete. Being a startup with limited resources, this also poses a big challenge for Ayasdi. How will Ayasdi gain market share and acceptance, without ‘waking the sleeping giants’? Page | 13 The "Big Data" Sea ABI Research recently estimated that global spending on “Big Data” services would reach $114 billion by 2018. That’s an increase from the estimated $31 billion that will be spent on the industry this year.19 “This is a critical time in the evolution of how organizations utilize data — a time when some will take a great leap forward. Ayasdi’s vision is to transform how the world uses data to solve problems and to enable every organization to become a Brilliant Enterprise,” said Singh. “Brilliant Enterprises will effectively find insights and operationalize them to create billions of dollars in growth, bring down costs, and solve many of our world’s most complex and expensive problems.”19 We are also seeing great leaps in machine learning that will inevitably shift the paradigm even further. As ABI Research recently reported: “Machine learning and its application in advanced analytics is one area that will make both the public and private sectors data-savvier than anything we’ve seen so far,” said Dan Shey, practice director at ABI. “Big players such as IBM and HP are understandably moving to this direction, but at the same time we can also see analytics startups, like Ayasdi and Skytree, that have machine learning in their very DNA. Eventually, such innovations will put analytics within any
  14. 14. domain expert’s reach. At that point, data will stop being ‘big’ again.”18 What does this mean for Ayasdi? Great opportunities and challenges! Most companies within the “Big Data” industry do the same thing, with very similar architectures and technology. Some analytics engines are faster than others, and some can process more data than others, but no other company uses Topological Data Analysis. The challenge to Ayasdi’s differentiation may be the industry itself. They are often put into the “Big Data” bucket by default, even being recognized with multiple Big Data awards. Ayasdi does much more than “Big Data” transactional processing. They are innovators of machine learning and data science. While the recognition can be excellent public relations, and provides them with the press to be renowned within enterprise software, it also makes it difficult to differentiate themselves and their products/expertise from the competition. The Road Ahead: The CEO’s Dilemma Clearly, Singh has many challenges ahead. As indicated in the recent Press Release from IVP, which stated “Ayasdi has an ambitious course ahead, focusing on technology while expanding the employee base.”9 If they build it, they will come? Is the strategic direction outline by Singh the correct course for Ayasdi to chart? Ayasdi has recently partnered with Cloudera19, should it also partner with a world recognized firm, such as IBM? Should it license its technology? These are all important questions to consider when evaluating Ayasdi’s goals, technology strategy and industry strategies. Further Concepts to Consider Competitors can enhance a firm’s ability to differentiate itself by serving as a standard of comparison. Without a competitor, buyers may have more difficulty perceiving the value created by the firm, and may, therefore, be more price or service sensitive. As a result, buyers may bargain harder on price, service or product quality. When evaluating Ayasdi’s technology strategy, it’s important to keep in mind the following: 1. Identify all the distinct technologies and sub-technologies in the value chain. 2. Identify potentially relevant technologies in other industries or under scientific Page | 14 development. 18 Patterson, Sean. "Big Data Industry to Hit $114 Billion by 2018." Web log post. WebProNews. N.p., 09 Sept. 2013. Web. 09 Sept. 2013. <http://www.webpronews.com/big-­‐data-­‐industry-­‐to-­‐hit-­‐114-­‐billion-­‐by-­‐2018-­‐2013-­‐09>. 19 Institutional Venture Partners. Ayasdi Raises $30.6 Million in Series B Funding from Institutional Venture Partners (IVP), GE Ventures, and Citi Ventures. IVP: Institutional Venture Partners. N.p., n.d. Web. 25 Aug. 2013. <http://www.ivp.com/news/press-­‐release/ayasdi-­‐raises-­‐-­‐30-­‐6-­‐ million-­‐in-­‐series-­‐b-­‐funding-­‐from-­‐institutional-­‐venture-­‐partners-­‐-­‐ivp-­‐-­‐-­‐ge-­‐ventures-­‐-­‐and-­‐citi-­‐ventures>.
  15. 15. 3. Determine the likely path of change of key technologies. 4. Determine which technologies and potential technological changes are most significant for Page | 15 competitive advantage and industry structure.
  16. 16. Page | 16 Exhibit A: http://www.ayasdi.com/product/ Exhibit B: http://www.ayasdi.com/product/deployment/
  17. 17. Page | 17 Exhibit C: http://www.ayasdi.com/product/
  18. 18. Bibliography "Ayasdi Was Started in 2008 to Bring a Groundbreaking New Approach to Solving the World's Most Complex Problems after a Decade of Research at Stanford, DARPA and NSF." Ayasdi. N.p., n.d. Web. 01 Sept. 2013. <http://www.ayasdi.com/company/>. Marie, Jessica. "Ayasdi: Capitalizing on the Shape of Data." Web log post. Inside Analysis. N.p., 14 Mar. 2013. Web. 01 Sept. 2013. <http://insideanalysis.com/2013/03/ayasdi-capitalizing-on-the- Page | 18 shape-of-data/>. Carlsson, Gunnar. "Topology and Data." BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY, 29 Jan. 2009. Web. 20 Aug. 2013. <http://www.ayasdi.com/_downloads/Topology_and_Data.pdf>. "Our Work with the World's Leading Organizations." Ayasdi. N.p., n.d. Web. 05 Sept. 2013. <http://www.ayasdi.com/customers/>. "Customer Segmentation from Credit Card Transaction Data." Ayasdi. N.p., n.d. Web. 05 Sept. 2013. <http://www.ayasdi.com/product/deployment/customer-segmentation.html>. "Biomarker Discovery from Expression and Sequencing Data." Ayasdi. N.p., n.d. Web. 01 Sept. 2013. <http://www.ayasdi.com/product/deployment/biomarker-discovery.html>. Solutions Use Cases for the Brilliant Enterprise. "Structuring Precise Marketing Strategies From Mobile Log Files Ayasdi. N.p., n.d. Web. 28 Aug. 2013. <http://www.ayasdi.com/solutions>. "The Ayasdi Platform." Ayasdi. N.p., n.d. Web. 01 Sept. 2013. <http://www.ayasdi.com/product/>. "No Questions Asked: Big Data Firm Maps Solutions without Human Input." Wired UK. N.p., n.d. Web. 20 Aug. 2013. <http://www.wired.co.uk/news/archive/2013-01/16/ayasdi-big-data-launch>. Patterson, Sean. "Big Data Industry to Hit $114 Billion by 2018." Web log post. WebProNews. N.p., 09 Sept. 2013. Web. 09 Sept. 2013. <http://www.webpronews.com/big-data-industry-to-hit- 114-billion-by-2018-2013-09>. Institutional Venture Partners. Ayasdi Raises $30.6 Million in Series B Funding from Institutional Venture Partners (IVP), GE Ventures, and Citi Ventures. IVP: Institutional Venture Partners. N.p., n.d. Web. 25 Aug. 2013. <http://www.ivp.com/news/press-release/ayasdi-raises-- 30-6-million-in-series-b-funding-from-institutional-venture-partners--ivp---ge-ventures--and-citi-ventures>.

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