Ayasdi Case Study

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

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/>.

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/>.

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.

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.

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>.

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>.

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>.

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>.

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>.

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>.

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

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>.

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.

Page | 16
Exhibit A:
http://www.ayasdi.com/product/
Exhibit B:
http://www.ayasdi.com/product/deployment/

Page | 17
Exhibit C:
http://www.ayasdi.com/product/

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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Ayasdi Case Study