PRIVATE & CONFIDENTIAL
Market UpdateJanuary 2020
Driven by your success.Page 1
B Canaccord Genuity Overview / Update
A Machine Learning & Artificial Intelligence Market Update
Deep Learning
Machine Learning
Artificial
Intelligence
f (x)
Driven by your success.Page 2
Machine Learning (“ML”) and Artificial Intelligence (“AI”) continue to generate strong levels of attention and excitement in themarketplace, based on the promise of self-correcting algorithms driving increased intelligence and automation across a number ofmission critical applications and use cases
First we look to define and better understand ML/AI technology, both the underlying algorithms as well as data science platforms, operational frameworks and advanced analytics solutions which leverage and / or optimize core ML/AI technologies – these are also described as “AI Infrastructure”
While there is real innovation and traction occurring in ML/AI, in some cases it is still difficult to understand where certain companies truly play in the ML ecosystem and the unique value that each brings to the table – this presentation aims to provide a framework to understand the ML landscape
From a category perspective, we focus primarily on horizontal platforms which can provide data science frameworks and / or advanced analytics solutions across a number of verticals, as well as the underlying software platforms which ingest, store, manage, test and integrate data sources and models
When ML & AI were first introduced as concepts that would impact the IT landscape, most companies in the sector were limited to collections of data scientists or technologies in search of use cases – today there are defined categories emerging and companies with real traction in ML/AI, as well as a growing set of tangible use cases
We also take a look at selected vertical application players that leverage ML/AI as a core source of differentiation – many of these businesses are gaining traction faster than horizontal platforms, as there can be a sharper value proposition and path to market as customers can more clearly understand and quickly leverage the benefits associated with these applications
We highlight activity in the space by some of the largest platform players in the broader Cloud / IT platform sectors
Driven by your success.Page 3
▪ ML/AI at the highest level describes the ability for machines and algorithms toself-learn and think and act more like humans.
‒ Artificial Intelligence: the ability of machines to perform tasks that requirehuman intelligence (e.g., visual perception, speech recognition, decision-making, translation)
‒ Cognitive Computing: the simulation of human thought processes in acomputerized model through self-learning systems that mimic the way thehuman brain works (e.g., data mining, pattern recognition and naturallanguage processing)
‒ Machine Learning: a subset of AI techniques which use statistical methods toautomate the ability of a system to iteratively learn from data and extractinsights without being explicitly programmed through algorithms
‒ Deep Learning: a branch of Machine Learning that data scientists use to buildmodels based on artificial neural networks (interconnected systems that learnto perform tasks by analyzing examples across the many systems withoutbeing programmed with task-specific rules and guidelines)
▪ Predictive/Advanced Analytics Solutions provide the platforms and tools tobuild and deploy predictive models and analytics applications using ML andother statistical algorithms.
▪ The increasing demand for Machine Learning is being driven by a number oftrends, including the ongoing data explosion, the rapid adoption of cloud,mobile & IoT technologies and strong need for deep and predictiveintelligence.
‒ The exploding volume and increasing complexity of data that the world is now“swimming in” has quickly driven the need for ML/AI solutions
‒ The movement of applications and infrastructure into the Cloud (where lotsof data also resides) provides a strong platform for the development of ML/AIframeworks and applications, while the proliferation of mobile & IoT devicesallows that data to be created, accessed and processed at the edge
▪ As more than 50% of enterprise IT organizations are experimenting with ML/AIin various forms, the Global AI market is forecasted to reach over $51.3 billionin the next three years, growing at a CAGR of 49.6% from 2018 to 2022.(1)
▪ As many of these solutions will also reside in or be delivered from the Cloud,the global market for Machine Learning as a Service (“MLaaS”) is estimated togrow to $5.4 billion by 2021, at a CAGR of 39.2%.(2)
Deep Learning
Machine Learning
Artificial
Intelligence
f (x)
1. Statista2. Transparency Markets Research
Machine Learning and Deep Learning: Subsets
of the Broader AI Opportunity
Artificial Intelligence (AI) –
A process where a
computer solves a task in a
way that mimics human
behavior. Today, narrow AI –
when a machine is trained to
do one particular task – is
becoming more widely used,
from virtual assistants to
self-driving cars to
automatically tagging your
friends in your photos on
Facebook.
What Makes a Machine Intelligent?
While AI is the headliner, there are actually subsets of the technology that can be applied to solving human problems in different ways.
Machine Learning (ML) –
Algorithms that allow
computers to learn from
examples without being
explicitly programmed.
Deep Learning (DL) –
A subset of ML that uses
deep artificial neural
networks as models and
does not require feature
engineering.
Driven by your success.Page 4
Human Only
Human + Data
Informed
Human + Machine
Assisted
Machine Only
Inte
llig
en
ceA
lgo
rith
mic
Re
aso
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g
Manual AutomaticAction
Capable of creative, instinct driven tasks
that require deep context. Ask questions
and hypothesize about abstract answers,
but are not scalable and can only process a
limited amount of data
Can process massive amounts of data.
Data visualization and analytics have
become key for web performance,
business intelligence, stock analysis, etc
Leveraging AI, predictive analytics,
and big data, applications go beyond
just data visualization to provide
targeted recommendations
Fully automated algorithms but still
designed and monitored by humans.
These solutions are highly scalable
without the need of human touch
Two of the most widely adopted Machine Learning methods
are supervised learning and unsupervised learning – while
hybrid forms are also emerging
• Supervised learning
‒ The algorithm receives a set of inputs along with the
corresponding “correct“ outputs and continuously modifies
its model until the actual output equals the targeted
outputs
‒ Commonly used where historical data predicts future
events, e.g., predicting fraud
• Unsupervised learning
‒ The algorithm must explore data and find some structure
within; the system is not shown the “right answer”
‒ Works well on transactional data, e.g., identifying similar
segments of customers for marketing campaigns
• Semi-supervised learning
‒ The algorithm receives some labeled data (i.e., correct
answers) as training and a large amount of unlabeled data
‒ Useful when the cost of fully-labeled data is too high, e.g.,
facial recognition
• Reinforcement learning
‒ The algorithm uses trial and error to determine which
actions yield the greatest rewards over a given amount of
time
‒ Often used for robotics, gaming, and navigation
Machine Learning MethodsPath to Machine Learning
Sources: x.ai and SAS
Driven by your success.Page 5
• Manage the infrastructure and platforms required to
support the complete lifecycle of building and delivering
analytics applications
• Many have launched or are developing ML solutions which
can run on top of their Cloud platforms
Data Science Platforms Advanced Analytics ML/AI Platforms
Open Source-Focused Vendors / Platforms Stream Processing / Real-Time Analytics Data Integration / Preparation / Governance
Cloud / IT Platform Players Broader BI / Search / Data Analytics
Data / Analytics OptimizationHadoop / NoSQL / Graph Datastores Next-Gen / New SQL Databases
• Data science platforms are generally frameworks and tools for
bringing data pipelines / ML algorithms into production apps
• Leverage heavy ML expertise and IP but are generally agnostic to
specific types of analytics and the resulting applications
• Predictive analytics and other categories of advanced analytics use
sophisticated quantitative methods to produce insights above and
beyond traditional query and reporting
• Generally offer specific types of analytics solutions across a targeted
range of verticals
• Vendors adding value or commercial support on
top of specific open source platforms
• Often developed in a collaborative and public
manner, which generates a more diverse design
perspective and evolution of the core platforms
• Analytics and data management
platforms which ingest, analyze and
take action on fast data streams
• Highly relevant for IoT use cases in
particular and other environments
which involve real-time information
• Data integration involves preparing,
normalizing and transforming data across
disparate sources, which reside on-premise
or in the Cloud
• These solutions allow ML/AI and analytics
solutions to be more effective out of the box
• These vendors are more established providers of data processing, analytics, and
presentation of business information
• Many of these players have introduced or acquired ML technologies already and
should continue to develop and acquire ML-related offerings going forward
• Hadoop is an open source data store w/ vendor
support now largely centered around one
vendor (Cloudera)
• NoSQL and graph DBs continue to address
emerging real-time use cases
• Vendors which are focused on
providing traditional relational / SQL DB
functionality in cloud-native, scale out
platforms, often with analytics and
transactional capability as well
• Technologies and associated vendors
focused on optimizing data access and
management through virtualization,
caching or other optimization-oriented
techniques
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Data Science Platforms Advanced Analytics ML/AI Platforms
Open Source-Focused Vendors / Platforms Stream Processing / Real-Time Analytics Data Integration / Preparation / Governance
Cloud / IT Platform Players Broader BI / Search / Data Analytics
Data / Analytics OptimizationHadoop / NoSQL / Graph Datastores Next-Gen / New SQL Databases
/
//
/
Disclaimer: Landscape for corporate logos is not meant to be fully comprehensive
Driven by your success.Page 7Sources: Data Scientist Insights and McKinsey
Descriptive analytics quantitatively describe the main features of a collection of data
Predictive causal analytics proactively identifies the cause
for an event
Predictive analytics predicts correlational relationships between known random
variables to predict future occurrences
Prescriptive analytics couples decision science to predictive capabilities in order to identify
actionable objectives that directly impact a desired goal
Data Analytics Data Science
What? Why? When? How?
Descriptive Diagnostic Predictive Prescriptive
Know What
We Know
Know What We
Don’t Know
Don’t Know
What We Know
Don’t Know What
We Don’t Know
What were our
customers using our
product for in the
last year?
Why were our
customers using
our product?
When will our
customers no longer
need our product?
How do we ensure the
customer continues
using our product next
year?
Driven by your success.Page 8
1. DataRobot2. Dataiku3. Hitachi ML Model Management workflow4. Sisense ML integration example
▪ Many of the early market leaders in AI Infrastructure are data science and model management platforms (such as DataRobot and Dataiku), which
allow their customers to more effectively leverage 3rd party open source models/algorithms (rather than offering their own), saving time on the
front end and allowing more time for customization/iteration.
‒ The associated time savings allow Data Science platforms to produce significantly more models than previously possible; for example, DataRobot
creates more than 2.5 million new models for its customers daily(1)
▪ Increasingly, these early market leaders are also moving toward a more user-friendly interface with a hybrid of automated and manual solutions,
where customers can input a few key data points, questions they are looking to answer and the platform directs the user to which
models/algorithms are appropriate for specific situations.
‒ Early market leaders have maintained functionality that allows more advanced data scientists to engineer their own models and other value-
added features, but have also built out pre-selected feature sets that allow novice scientists to leverage and create analytic applications using
their platforms
Example Data Science Workflows (3,4)
Driven by your success.Page 9Sources: Crunchbase, S&P Capital IQ , Pitchbook
Data Science
Founded: 2012
HQ: Boston, MA
Employees: 1,035
Invested Capital: $431M
Description:
DataRobot offers a
Machine Learning platform
for data scientists of all skill
levels to build and deploy
accurate predictive
models. The Company’s
technology addresses the
critical shortage of data
scientists by changing the
speed and economics of
predictive analytics.
Founded: 2011
HQ: Corvallis, OR
Employees: 50
Invested Capital: $10M
Description:
BigML has pioneered the
Machine Learning as a
Service (MLaaS) wave of
innovation through its
consumable,
programmable, and
scalable software
platform streamlining the
creation and deployment
of smart applications
powered by state-of-
the-art predictive
models.
Founded: 2007
HQ: Boston, MA
Employees: 100
Invested Capital: $53M
Description:
RapidMiner provides
enterprises with
predictive analytics in any
business process, closing
the loop between insight
and action. The
Company’s solution
makes predictive
analytics lightning-fast
for today’s modern
analysts, radically
reducing the time to
unearth opportunities
and risks.
Founded: 2013
HQ: Paris, France
Employees: 400
Invested Capital: $147M
Description:
Dataiku provides a
software platform for data
applications. It offers Data
Science Studio, a software
platform that aggregates
the steps and big data
tools necessary to get
from raw data to
production ready
applications. The
company’s Data Science
Studio enables companies
to build their data lab and
start extracting value from
their data.
Founded: 2014
HQ: San Francisco, CA
Employees: 27
Invested Capital: $9M
Description:
SigOpt is a standardized,
scalable, enterprise-
grade optimization
platform and API
designed to unlock the
potential modeling
pipelines. This fully
agnostic software
solution accelerates,
amplifies, and scales the
model development
process.
Driven by your success.Page 10Sources: Crunchbase, S&P Capital IQ , Pitchbook
Founded: 2008
HQ: Menlo Park, CA
Employees: 60
Invested Capital: $106M
Description:
Symphony Ayasdi is an
advanced analytics
company that offers a
machine intelligence
platform and intelligent
applications. The
Company enables its users
to solve their big data and
complex data analytics
challenges and to
automate formerly manual
processes using their own
unique data.
Founded: 2015
HQ: Austin, TX
Employees: 75
Invested Capital: $17M
Description:
Cerebri provides
enterprise software and
Machine Learning models
for production in
enterprise grade
software infrastructure.
Cerebri offers real-time
data inputs and Machine
Learning models in a
multi-model setup for:
production, failover, QA,
and learning models, all
running simultaneously.
Founded: 2013
HQ: Austin, TX
Employees: 165
Invested Capital: $50M
Description:
CognitiveScale develops
industry-specific
augmented intelligence
software for financial
services, healthcare, and
digital commerce
markets. Its products are
built on its Cortex
augmented intelligence
platform and enable
enterprises use artificial
intelligence (AI) and
blockchain technology.
Founded: 2012
HQ: Palo Alto, CA
Employees: 80
Invested Capital: $73M
Description:
MAANA designs and
develops industrial data
analytics and digital
knowledge technology
software solutions. It
offers Maana Knowledge
Platform, a knowledge-
centric platform for
operational problem
solving and Knowledge
Graph, a platform for
extracting knowledge
from data and
information sources for
contextual relationships.
Founded: 2000
HQ: Franklin, TN
Employees: 185
Invested Capital: $140M
Description:
Digital Reasoning Systems
builds data analytic
solutions for processing
and organizing
unstructured data into
meaningful data
automatically. The
company offers
Synthesys, an entity
oriented analytics
software for the automatic
categorization, linking,
retrieval, and profiling of
unstructured data.
Advanced Analytics
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Traditional leader in
commercial data analytics
Software suite that
captures, stores, modifies,
analyzes and presents data
Offers statistical functions
and a GUI for more rapid
learning and query language
similar to SQL
Popular in academics and
research, as well as the
corporate world
Open-source interpreted
programming language and
software for statistical
computing and graphics
Open-source counterpart of
SAS, making its way into the
business world via growing
support and incorporation
into commercial BI software
Active community
contributing functions and
extensions
Open-source interpreted
language known for
simplicity and clarity
Widely used in web
development and scientific
computing
Incorporates libraries and
functions for a vast array of
statistical operations
R functionality can be
accessed from Python
scripts
Open-source software
library for computation
using data flow graphs
Originally developed by
researchers and engineers
working on the Google Brain
Team for the purposes of
conducting Machine
Learning and deep neural
networks research
Open-source, in-memory cluster computing platform
Spark provides an interface for programming entire clusters
with implicit data parallelism and fault-tolerance
Spark ML (Machine Learning libraries) that data scientists
are increasingly using, is deployed on top of Spark
Most advanced analytics vendors have moved from Hadoop
to Apache Spark because of the available Machine Learning
libraries and speed of in-memory processing
Employs a data structure called the resilient distributed
dataset (RDD), useful in iterative algorithms that continually
query a dataset (e.g., training algorithms)
▪ Open-source Machine Learning engines are becoming increasingly pervasive.
‒ Commercial software vendors are already responding to this challenge in different ways, typically focusing on the top of the stack (the end user
experience), while the middle and bottom of the analytics stack increasingly becomes open source
‒ Many Machine Learning frameworks are using Open Source Platforms for data stream mining and to deploy models/algorithms
▪ In addition to specialized vendors offering commercial support, large tech companies are investing in, or acquiring, open source products and services.
‒ Turi, which has promoted open-source GraphLab, was acquired by Apple; PredictionIO was acquired by Salesforce; in 2015, Microsoft acquired
Revolution Analytics; and SAP acquired Hadoop-as-a-service startup Altiscale
‒ Microsoft also acquired Github in 2018 for $7.5 billion as its first, large-scale push into open source software- it completed two add-on acquisitions in
2019 to further expand its open source footprint
‒ TensorFlow, created in-house by Google, has also received significant investment as it has expanded its offering to include a free Crash Course and the
recently announced TensorFlow 2.0
Driven by your success.Page 12Sources: Crunchbase, S&P Capital IQ, Pitchbook
Founded: 2014
HQ: San Francisco, CA
Employees: 1,100
Invested Capital: $897M
Description:
StreamSets develops and
provides data ingest
technology for big data
applications. The
company’s tool is used for
retrieving and transporting
log messages from files,
syslog, or gathering
collected metrics; to
ingest data into the
Hadoop and surrounding
ecosystem; and to
connect applications to
Kafka.
Founded: 2011
HQ: Mountain View, CA
Employees: 215
Invested Capital: $147M
Description:
H2O.ai develops an open
source parallel
processing prediction
engine for machine
learning and predictive
analytics on big data. It
offers H2O and H2O
Driverless AI, designed
for data scientists and
developers who need in-
memory machine
learning for smarter
applications.
Founded: 2014
HQ: San Francisco, NY
Employees: 32
Invested Capital: $18M
Description:
Skymind operates a
business intelligence and
enterprise software
company. It builds
solutions that classify,
cluster, and make
predictions about text,
image, video, time series,
and sound to locate and
quantify patterns that
impact businesses.
Founded: 2011
HQ: Austin, TX
Employees: 115
Invested Capital: $48M
Description:
Anaconda develops the
Python data science
platform for companies to
adopt open data science
analytics architecture. It
offers Anaconda
Distribution, for the
distribution of data
science packages and
Anaconda Enterprise, an
open data science
platform. The company
also provides training
related to Python for data
and analytic needs.
Big Data Platforms
Founded: 2014
HQ: Forest Hills, CA
Employees: 1,910
Invested Capital: $39M
Description:
Apache NiFi is a software
project from the Apache
Software Foundation
designed to automate the
flow of data between
software systems. It is
based on the
"NiagaraFiles" software
previously developed by
the NSA. Software
development and
commercial support is
currently offered by
Hortonworks now merged
into Cloudera.
Driven by your success.Page 13Sources: S&P CapitalIQ, IBM, Ziff Davis, Imperva, StreamSets
▪ ML/AI solutions are more effective with high quality and fully integrated data, which
often resides in disparate silos within an organization. The value of analyzing an
integrated data set is often greater than the “sum of the parts” of analyzing each
silo alone.
▪ There are two aspects of data integration relevant to ML/AI:
‒ The traditional definition of data integration: combining data from disparate
sources or different versions of the same information into a “single source of truth”
‒ Using ML itself to automate the more labor-intensive, time-consuming, and error-
prone tasks in the data preparation/integration process – ML can more effectively
identify incorrect data, inappropriate sources, duplicates etc
▪ By automatically learning relationships between data sources, ML/AI can eliminate
much of the effort involved with data integration.
‒ After a subset of data sources has been mapped to an integrated system /
approach for queries/downstream analytics (or a “mediated schema”), this
integrated data set can be more readily leveraged by an analytic application
▪ Data governance and data privacy is also critical as data is increasingly at risk of
being breached or inappropriately accessed, or altered in error as it is leveraged by
ML/AI apps.
‒ Data governance solutions are critical in order to restrict data access to specific
users, both for security reasons and human capital efficiency
‒ Data discovery, classification and continuous data integration solutions ensure
that appropriate data sets are leveraged downstream as constantly changing “data
pipelines” are leveraged by analytics applications
‒ For example, Dataiku specifically has emphasized compliance with the EU’s
General Data Protection Regulation (“GDPR”) with an increased level of
collaborator oversight and access permissions
▪ These solutions all support emerging “DataOps” initiatives, which broadly describe
an automated approach to improve data quality and the cycle times associated with
data analytics applications.
Data Governance
DataOps
Driven by your success.
Founded: 2016
HQ: New York, NY
Employees: 24
Invested Capital: $6M
Description: Datalogue,
develops and automates
the process of data
wrangling by leveraging
machine learning and
distributed computing to
find patterns in the
structures of datasets
and transform them into
formats that data
scientists, developers,
and researchers expect.
The company caters to
the retail, health care,
pharma, and logistics
sectors.
Page 14Sources: Crunchbase, S&P Capital IQ , Pitchbook
Founded: 2012
HQ: San Francisco, CA
Employees: 205
Invested Capital: $274M
Description: Trifacta
develops data wrangling
software for data
exploration and self-
service data preparation
for analysis. It works with
cloud and on-premises
data platforms and is
designed to assist in the
exploration,
transformation, and
enrichment of raw data
into clean and structured
formats.
Founded: 2012
HQ: Cambridge, MA
Employees: 180
Invested Capital: $73M
Description: Tamr
designs and develops a
commercial-grade
solution to tackle the
challenge of connecting
and enriching data. It
offers an enterprise data
preparation platform that
combines Machine
Learning and data science
with collective human
insight to identify internal
and external data sources,
understand relationships,
and curate siloed data at
scale.
Founded: 2014
HQ: College Park, MD
Employees: 70
Invested Capital: $29M
Description: Immuta
offers a platform that
accelerates self-service
access and control of
sensitive data. It is an
automated, scalable, no
code approach to
sensitive data
governance software.
Founded: 2013
HQ: Mountain View, CA
Employees: 48
Invested Capital: $38M
Description: Waterline
Data develops a self-
service platform for
Hadoop that discovers
sensitive data,
intermediate files, and
data lineage. The
platform provides
business metadata and
multi-faceted search
services.
Integration / Preparation Governance
Driven by your success.Page 15
▪ Many of the data sets leveraged by ML/AI apps are real-time and constantlychanging, as opposed to batch-oriented data sets which are more static,representing “data pipelines” which require a series of data processing steps inorder to be leveraged by data integration and analytics applications.
▪ These “Stream Processing” techniques allow for the processing of data in motionand can be used to query and analyze continuous data pipelines. The shift towardsparallel and stream processing is being driven by the sheer volume of data beinggenerated away from centralized data centers (at the “edge”), and the need fordecreased latency, bandwidth limitations and on-site edge processing.
▪ As a result, the market for Stream Processing solutions is expected to reach $1.84billion by 2023 and is growing at a 22% CAGR during the forecast period(1) . Thesesolutions are a key component of broader Real-Time Analytics solutions, whichare estimated to grow at a 34.8% 5-year CAGR from $3.1 Billion in 2016 to $13.7Billion by 2021(2).
▪ The emergence of Real-Time Analytics represents a large and rapidly growingopportunity for a wide range of incumbent and emerging ecosystem across theInternet of Things (“IoT”) market in particular. IoT enables a wide range ofpreviously uncontrolled/isolated devices to connect to the Cloud, resulting in awide range of new applications while also presenting some technical challenges.
‒ As businesses seek to utilize the potentially large and diverse amounts of dataprovided by IoT devices at a high velocity, they will require robust solutions thatwill allow them to quickly process, analyze and manage real-time data to generateactionable intelligence
‒ Connectivity / integration of disparate IoT data sources in particular is also atremendous challenge for data analytics solutions to address
▪ Real-Time Analytics solutions make it possible for data to be categorized,processed and analyzed as it is being produced on edge devices, and can enablereal-time decision making for applications which are operating primarily in edgeenvironments.
‒ In a typical use case such as smart cities, security cameras, and autonomousvehicles, the real-time data is not stored at the edge, but quickly processed,analyzed and acted upon appropriately by the application on the device
‒ In some cases, certain pieces of data or the results of real-time analytics are sentback through the Cloud to a central repository or adjacent devices for furtherexploration, analysis or related actions
▪ In addition to IoT, key use cases for Stream Processing and Real-Time Analyticsinclude Autonomous Vehicles, Blockchain, Drones and broader Enterprise SaaSapplications where large volumes of real-time data are created and need to beefficiently processed.
1. Grand View Research, MarketsandMarkets
2. MongoDB, Techopedia, infoq, Streamanalytix/Impetus, Forbes, IDC, Accenture
3. Ververica
Stream Processing (3)
Real-Time Analytics / IoT Ecosystem (2)
Driven by your success.Page 16Sources: Crunchbase, S&P Capital IQ , Pitchbook
Stream Processing/Real-Time Analytics
Founded: 2014
HQ: Mountain View, CA
Employees: 1,000
Invested Capital: $206M
Description:
Confluent designs and
develops a real-time data
platform for organizations.
It offers Apache Kafka, an
open source technology
that operates as a scalable
messaging system and is
used for collecting user
activity data, logs,
application metrics, stock
ticker data, and device
instrumentation.
Founded: 2012
HQ: Palo Alto, CA
Employees: 90
Invested Capital: $72M
Description:
WebAction develops and
operates Striim, a platform
that combines streaming
data integration and
streaming operational
intelligence. Its platform
specializes in data
integration across a variety
of sources, including
change data capture, big
data, business
intelligence/machine data,
multi-log intelligence,
Internet of things, and
security and risk.
Founded: 2014
HQ: San Francisco, CA
Employees: 150
Invested Capital: $69M
Description:
StreamSets develops
and provides data ingest
technology for big data
applications. The
company’s tool is used
for retrieving and
transporting log
messages from files,
syslog, or gathering
collected metrics; to
ingest data into the
Hadoop and surrounding
ecosystem; and to
connect applications to
Kafka.
Founded: 2012
HQ: San Francisco, CA
Employees: 170
Invested Capital: $121M
Description:
InfluxDB is an open-source
time series database
developed by InfluxData. It
is written in Go and
optimized for fast, high-
availability storage and
retrieval of time series
data in fields such as
operations monitoring,
application metrics,
Internet of Things sensor
data, and real-time
analytics.
Founded: 2015
HQ: New York, NY
Employees: 50
Invested Capital: $31M
Description:
TimescaleDB is a time-
series SQL database
providing fast analytics
and scalability with
automated data
management on a proven
storage engine. It is built
on a time-series
database that natively
supports full-SQL.
Driven by your success.Page 17
▪ Per IDC, “Big Data describes a new generation of technologies and
architectures, designed to economically extract value from very large
volumes of a wide variety of data, by enabling high-velocity capture,
discovery, and/or analysis”.
▪ The early innovators in the big data sector were predominately hyperscale
platform companies who developed internal data management systems.
‒ Amazon (Dynamo), Facebook (Cassandra), Google (MapReduce), LinkedIn(Voldemort) and Yahoo (Hadoop) created and/or adopted their owndistributed data management systems rather than leveraging traditionaldatabases/data warehouses to provide the functionality required for theirlarge-scale, massively distributed architectures
‒ These are the core database technologies behind the Hadoop and “NoSQL”(not only SQL) movements, a collection of distributed data analytics / mgmttechnologies (many of which have been open sourced) which have formedthe basis for a broad category of rapidly emerging products and services
▪ Hadoop emerged as the early market leader as the platform of choice for
performing predictive analytics on unstructured data sets, while a wide range
of NoSQL databases have emerged for a broader range of use cases such as
real-time transaction processing.
▪ As the “platform distribution” game (e.g. Cloudera/Hortonworks for Hadoop,
MongoDB for NoSQL) has arguably been played out, the battleground at the
platform layer has shifted towards emerging analytics platforms such as Spark
(Databricks) and cloud-based data warehousing solutions such as Snowflake.
▪ Moving Big Data platforms / services into the Cloud is a top priority for many
organizations who seek to offload the compute, storage and management
infrastructure required to run their own data platforms internally.
▪ In addition, a new class of relational (“NewSQL”) databases have emerged
which provide more scalable forms of traditional databases – many of these
vendors are focused on providing both analytics and transactional capabilities
for real-time applications.
▪ Similarly, innovation continues to occur in the NoSQL sector as emerging
vendors seek to differentiate based on real-time performance, ability to
operate in the cloud and/or within mobile environments.
1. Cloudera Management Console
2. Wikibon; 2017 Big Date and Analytics Forecast
3. Wikibon; 2018 Big Data and Analytics Market Share Report
Software Big Data Market Segmentation and Share (2,3)
Other51%
Splunk11%
Oracle9%
IBM6%
SAP5%
Palantir4%
Cloudera3%
AWSSAS2%
Microsoft2%
Informatica2% Hortonworks
2%
Services27%
Software36%
Hardware37%
Cloudera‘s AI-powered Management Console (1)
Driven by your success.Page 18Sources: Crunchbase, S&P Capital IQ , Pitchbook
Founded: 2012
HQ: San Mateo, CA
Employees: 1,620
Invested Capital: $929M
Description:
Snowflake Computing
designs and develops a
cloud-based data
warehousing software
solution for customers
worldwide. The company
offers cloud-native elastic
data warehouse services,
multidimensional elasticity,
and combines structured
and semi-structured data,
such as JSON, Avro, or
XML in a single query.
Founded: 2008
HQ: Cambridge, MA
Employees: 75
Invested Capital: $95M
Description:
NuoDB develops a
relational database
solution for cloud-enabled
global applications. Its
database provides
memory-centric
architecture to make
optimizations around
storage, redundancy,
replication, and more
without worrying about
disk I/O.
Founded: 2010
HQ: San Francisco, CA
Employees: 180
Invested Capital: $110M
Description:
MemSQL provides real-
time databases for
transactions and analytics.
It offers a real-time data
warehouse that combines
real-time streaming,
database, and data
warehouse workloads for
sub-second processing
and reporting in a single
database.
Founded: 2008
HQ: Santa Clara, CA
Employees: 445
Invested Capital: $146M
Description:
Couchbase develops and
provides NoSQL
databases to enterprises
for web and mobile
applications. It offers a
platform that is used by
developers to build
enterprise web, mobile,
and Internet of Things
applications that support
massive data volumes in
real time.
Founded: 2009
HQ: Mountain View, CA
Employees: 95
Invested Capital: $77M
Description:
Aerospike provides a
NoSQL database. It offers
enterprise and open
source versions of NoSQL
database that include tools
and packages with
features such as key-value
store, flexible data model,
user defined functions,
geospatial, aggregations,
and geographic replication.
Next-Gen Data Warehousing / SQL NoSQL
Driven by your success.Page 19Sources: CIO Magazine, Digitalist/SAP, Microsoft 1. Microsoft Power BI integrated with Blendo2. Domo Marketing Dashboard
▪ Business Intelligence vendors are starting to integrate MachineLearning and AI as they seek to continue to differentiate theirofferings.
▪ Offering a full stack has proven difficult for certain emergingstandalone BI vendors, which has driven consolidation among vendorswho seek to attack the market with a combined solution and increasedscale.
▪ Example Transactions:
‒ SiSense / Periscope: Rapidly emerging data analytics platform playermerges with Periscope to offer advanced, ML-based predictiveanalytics on top of its core platform
‒ Arcadia Data joins forces with Cloudera to provide a full BI solution ontop of Cloudera’s data platform offerings
‒ Alteryx acquired ClearStory and Logi Analytics acquired Zoom Datato enhance their respective data processing and visualizationengines
▪ Market Dynamics Driving Emergence of Embedded Analytics:
‒ As the core BI sector becomes increasingly competitive, certainvendors have focused on embedding analytics into 3rd-party apps
‒ These solutions are often low-code oriented to allow developers tointegrate data analytics into application development practices(supportive of DataOps)
▪ Larger SaaS & Cloud platform players are actively investing in BI / dataanalytics offerings to enrich existing applications and provide Cloud-based data analytics services
‒ Salesforce acquired Tableau for $15.7B to gain a strong foothold inthe data visualization / next-gen BI layer
‒ Google Cloud acquired Looker for $2.6B to add significant BIcapabilities and a rapidly growing SaaS analytics offering
Microsoft’s Power BI Console (1)
Domo’s AI-powered Marketing Dashboard (2)
Driven by your success.Page 20Sources: Crunchbase, S&P Capital IQ , Pitchbook
Broader BI / Search / Data Analytics
Founded: 2005
HQ: New York, NY
Employees: 750
Invested Capital: $286M
Description:
Sisense develops In-Chip
and Single Stack business
intelligence and data
analytics software
solutions. The company
offers a business
intelligence tool that
enables users to manage,
analyze, and visualize
complex data for big and
disparate datasets.
Founded: 2003
HQ: Palo Alto, CA
Employees: 2,490
Invested Capital: $2,750M
Description:
Palantir Technologies
develops and builds data
fusion platforms for public
institutions, commercial
enterprises, and non-profit
organizations worldwide. The
company offers Palantir
Gotham, a platform that
integrates, manages,
secures, and analyzes
enterprise data; and Palantir
Metropolis, a platform that
integrates, enriches, models,
and analyzes quantitative
data.
Founded: 2008
HQ: San Francisco, CA
Employees: 290
Invested Capital: $218M
Description:
LucidWorks offers AI
powered search engine
software. It offers Fusion, a
platform that provides AI
powered search that
offers augmented
intelligence, machine
learning, clustering, query
analysis, signals, indexing,
and hyper personalization.
Founded: 2007
HQ: San Francisco, CA
Employees: 275
Invested Capital: $125M
Description:
GoodData operates a
SaaS-based business
intelligence (BI) and
analytics platform that
provides commercial big
data analysis services. Its
open analytics platform
supports information
technology needs for data
governance, security and
oversight, and business
users’ desires for self-
service data discovery.
Founded: 2000
HQ: McLean, VA
Employees: 190
Invested Capital: $48M
Description:
Logi Analytics provides
business intelligence
solutions to software, ISV,
and SaaS providers. It
offers Logi Info, an
information application
development platform
that features Web portals,
mobile applications,
embedded analytics, and
Web front-ends for
operational systems, as
well as dashboards,
reports, and analytics.
Driven by your success.Page 21
Full-service Bluemix offering that makes it easy for developers and data scientists to
work together to integrate predictive capabilities with their applications
▪ Built on IBM's proven SPSS analytics platform, IBM Watson’s Machine Learning allows
users to develop applications that make smarter decisions, solve tough problems, and
improve user outcomes.
‒ Watson Discovery: A cognitive search and content analytics engine that helps
developers extract value from unstructured data by converting, normalizing
and enriching the data to find hidden patterns and answers, enabling better
decisions across teams
‒ Watson Conversation: Leverages a visual dialog builder to create natural
conversations between apps and users, allowing users to quickly build, test and
deploy bots or virtual agents across mobile devices, messaging platforms or
even on a physical robot
‒ Watson Virtual Agent: Offers a cognitive, conversational self-service
experience that can provide answers and take action using pre-built content to
quickly configure virtual agents with company information engage with
customers in a conversational, personalized manner, on any channel
‒ Watson Knowledge Studio: Cloud-based application that enables developers
and domain experts to collaborate and create custom annotator components
for unique industries. These annotators can identify mentions and relationships
in unstructured data and be easily administered throughout their lifecycle using
one common tool
Source: IBM
Driven by your success.Page 22
Amazon provides Sagemaker, a fully managed service for building MLmodels and generating predictions, enabling the development of robust,scalable smart applications. Amazon Sagemaker provides users withaccess to powerful Machine Learning technology without requiring anextensive background in Machine Learning algorithms and techniques.
‒ The process of building ML models with Sagemaker consists of three
operations: building data analysis, training/tuning the model, and
deploying/managing the models
‒ Build: Easy-to-use ML model creator that includes the 10 most used
algorithms that are pre-installed and optimized. Computes and visualizes
data distribution, and suggests transformations that optimize the model
training process in a single, web-based interface
‒ Train/Tune: Managed infrastructure allowing the training and testing of
models up to the petabyte scale. Finds and stores the predictive patterns
within the transformed data
‒ Deploy/Manage: Assists with deployment on an auto-scaling cluster of EC2
instances with built-in testing capabilities
Amazon Sagemaker combines powerful Machine Learning algorithmswith interactive visual tools to guide users to easily create, evaluate, anddeploy Machine Learning models. Its built-in data transformationsensure that input datasets can be seamlessly transformed to maximizethe model's predictive quality. Once a model is built, the service'sintuitive model evaluation and fine-tuning console help usersunderstand its strengths and weaknesses, and adjust its performance tomeet business objectives.
Data Visualization And Exploration:
‒ High-quality data is critical to building accurate predictive models,
but real world datasets are frequently incomplete or inconsistent
‒ AML provides interactive charts to visualize, explore, and
understand data content and distribution and spot missing or
incorrect data attributes
Machine Learning Algorithms:
‒ Uses scalable and robust implementations of industry-standard ML
algorithms
‒ Developers can create models that predict values of binary
attributes (binary classification), categorical attributes (multi-class
classification), or numeric attributes (regression)
‒ For example, a binary classification model can be used to predict
whether a comment is spam
APIs for Batch and Real-time Predictions:
‒ Provides APIs to obtain predictions from ML algorithms to easily
build smart applications
‒ Batch prediction API retrieves a large number of data records and
generates predictions all at once
‒ Real-time prediction API generates predictions synchronously and
with low-latency
Modeling APIs:
‒ Provides APIs for modeling and management that allow users to
create, review, and delete data sources, models, and evaluations
‒ Allows users to automate the creation of new models when new
data becomes available
‒ APIs also inspect previous models, data sources, evaluations, and
batch predictions for tracking and repeatability
Source: Amazon
Driven by your success.Page 23Source: Google Cloud Platform
Google Cloud Machine Learning provides modern Machine Learning services, with pre-trained models and a service engine for users to easily build their own
customized models on any type and size data
Google Cloud Machine Learning Features Include:
• Predictive Analytics at Scale: Seamlessly transition from training to prediction, using online and batch prediction services. Integration to Google global load balancing enables
users to automatically scale their Machine Learning applications, and reach users world-wide
• Hypertune: Allows data scientists to build better performing models faster by automatically tuning hyperparameters, instead of manually discovering values that work for their
model to automatically improve predictive accuracy
• Scalable Service: Managed distributed training infrastructure that supports CPUs and GPUs to accelerate model development and build models of any data size or type by training
across many number of nodes, or running multiple experiments in parallel
• Integrated: Works with Cloud Dataflow for feature processing, Cloud Storage for data storage and Cloud Datalab for model creation
• Managed Service: Automates all resource provisioning and monitoring, allowing users to focus on model development and prediction without worrying about the infrastructure
• Portable Models: Through open source TensorFlow SDK, Google Cloud Machine Learning trains models locally on sample data sets and can be scaled through the Google Cloud
Platform as well as downloaded and shared for local execution or mobile integration using Cloud Machine Learning Engine
Driven by your success.Page 24
Facebook formed its Applied Machine Learning team in September 2015, the
group runs a company-wide internal platform for Machine Learning called
FBLearner Flow. FBLearner Flow combines several Machine Learning models to
process several billion data points, drawn from the activity of the site’s 1.5
billion users, and forms predictions about thousands of things: which user is in a
photograph, which message is likely to be spam, etc. The algorithms created
from FBLearner Flow’s models help define ranking and personalize News Feed
stories, filtering out offensive content, highlighting trending topics, ranking
search results, advertisements and more.
FBLearner Flow is capable of easily reusing algorithms in different products,
scaling to run thousands of simultaneous custom experiments, and managing
experiments with ease. This platform provides innovative functionality, like
automatic generation of UI experiences from pipeline definitions and
automatic parallelization of Python code. FBLearner Flow is used by more than
25% of Facebook's engineering team. Since its inception, over one million
models have been trained, and the prediction service has grown to make more
than 6 million predictions per second.
Source: Facebook
⚫ Where Facebook is Using Artificial Intelligence / Machine Learning:
• Textual Analysis: “DeepText” tool extracts meaning from words by learning toanalyze the context of user’s posts. Neural networks analyze the relationshipbetween words to understand how their meaning changes depending on otherwords around them. As a form of “semi-unsupervised learning”, the algorithms donot necessarily have reference data to understand the meaning of every word,instead, it learns for itself based on how words are used. This tool is used to directpeople towards products they may want to purchase based on conversations theyare having.
• Facial Recognition: “DeepFace” is a Deep Learning application to teach Facebookto recognize people in photos. Facebook’s most advanced image recognition tool ismore successful than humans in recognizing whether two different images are ofthe same person or not – with DeepFace scoring a 97% success rate compared tohumans with 96%.
• Targeted Advertising: Facebook uses deep neural networks – the foundationstones of Deep Learning – to decide which advertisements to show to which usersby tasking machines themselves to find out as much as they can about users, andcluster users together in the most insightful ways to deliver advertisements.
• Designing Applications: Facebook has even decided that the task of decidingwhich processes can be improved by AI and Deep Learning can be handled bymachines. A system called “Flow” has been implemented which uses Deep Learninganalysis to run simulations of 300,000 Machine Learning models every month, toallow engineers to test ideas and pinpoint opportunities for efficiency.
Driven by your success.Page 25
Microsoft has been investing in the promise of artificial intelligence for more than 25 years – and this vision has come to life with new chatbot Zo, Cortana Devices SDK and
Skills Kit, and expansion of intelligence tools. In 2016, Microsoft became the first in the industry to reach parity with humans in speech recognition. Microsoft has also built
perhaps the world’s biggest knowledge graph. Thanks to work in Bing and Office 365, it’s possible to understand billions of entities – people, places and things. Microsoft
now has the opportunity to connect this “world knowledge” with peoples’ “work knowledge”. Microsoft further expanded their access to diverse, coding entities thorough
its acquisition of GitHub, providing it access to the code repository and millions of additional users.
“In the last year, one of the things that started to happen is assembling multiple models to put speech together with computer vision, put it together with machine translation. And
once you do these multi-model trainings, you see some amazing, amazing things. We have a new addition to the PowerApp family, it’s the AI Builder. It takes some of those
magical AI Cognitive Services capabilities and brings it to any application that you may want to build.”
– Satya Nadella, Microsoft (August 2019)
Microsoft’s deep investments in AI are advancing the state of the art in machine intelligence and perception, enabling computers that understand what they see,
communicate in natural language, answer complex questions and interact with their environment. The research, tools and services that result from this investment are
woven into existing and new products and, as well as accessible to the broader community in a bid to accelerate innovation, democratize AI and solve the world’s most
pressing challenges.
Maluuba:• Maluuba is a Deep Learning start-up with one of the
world’s most impressive Deep Learning research labs for
natural language understanding for the advancement of
AI at Microsoft
• Maluuba’s expertise in Deep Learning and reinforcement
learning for question-answering and decision-making
systems helps Microsoft advance their strategy to
democratize AI and to make it accessible and valuable to
everyone — consumers, businesses and developers
Cortana:• Cortana is an AI-based personal assistant that integrates
with over 1,000 apps, and is available in 8 languages.
Cortana can set reminders, recognize natural voice
without the requirement for keyboard input, and answer
questions using information from Microsoft Bing. There
are over 145 million users across platforms
• Cortana Skills Kit allows developers to leverage bots to
create new skills and personalize their experiences by
leveraging Cortana's understanding of users'
preferences and context, based on user permissions
GitHub:• GitHub is an open source coding community that
enables people to share software development
controlled by Git, a distributed version control system
• It is the largest open source repository in the world and
expands Microsoft’s footprint in the Cloud / AI open
source vertical
• The acquisition allows Microsoft to access the code
repository and GitHub users, which it expects to use to
bolster its own offerings and cross-market products,
respectively
Sources: Microsoft and 451 Research
Driven by your success.Page 26
Sales & Marketing /
Customer Experience
Optimization
Industrials / IoT /
Autonomous VehiclesFinTech Cybersecurity Healthcare
• Analytics are enabling a
broad range of
applications and use
cases which customer-
facing e-commerce
businesses are using to
optimize their users’
digital experience
• Sales & Marketing
departments have been
leveraging ML-based
applications to better
understand their
customer bases and
optimize the delivery of
marketing initiatives
• ML/AI is an important component of Robotic Process Automation (RPA) solutions, which represent the automation and optimization of traditionally human-oriented tasks such as back office automation, supply chain mgmt, and call center response
• IoT leverages device monitoring for supply chain improvement and pre-emptive corrections to avoid downtime and critical machine failure –ingesting / analyzing data is a key component of successful IoT initiatives
• Health care organizations
maintain very large sets
of patient data, which can
be analyzed for a broad
range of potential use
cases including
case/practice
management, health
insurance optimization,
and patient record quality.
• Healthcare companies,
especially Biotech and
Pharma, leverage
Machine Learning to
automate and expedite
the drug development
process as well.
• Analytics provide
organizations with a new
layer of context which
allows them to better
understand their risk
profiles in real-time and be
able to respond more
effectively to zero-day
attacks and prevent future
threats going forward
• ML/AI has become a key
enabling technology for a
variety of cybersecurity
use cases, as threat
prevention/detection
solutions companies
struggle to keep up with
new types of attacks and
areas of exposure.
• Analytics are becoming
increasingly critical for
large financial services
organizations, with a
broad range of use cases
including:
1. Automated trading
based on real-time
market data
2. Analysis of potential risk
exposure by insurance
companies
3. Understanding potential
credit risk for both
consumers and
businesses looking to
obtain credit from
banking institutions
Driven by your success.Page 27Disclaimer: Vertical Application landscape for corporate logos is not meant to be comprehensive, but is a representative view of companies leveraging ML/AI for vertical application use cases.
Sales & Marketing /
Customer Experience
Optimization
Industrials / IoT /
Autonomous VehiclesFinTech Cybersecurity Healthcare
Audio / Call Intelligence
Robotic Process
Automation
Driven by your success.Page 28Sources: Crunchbase, S&P CapitalIQ , Pitchbook
Founded: 2012
HQ: New York, NY
Employees: 105
Invested Capital: $109M
Description:
Augury develops a predictive
maintenance platform that
offers scalable predictive
maintenance strategies. It
enables facility owners and
service companies to deploy
quick, cost efficient strategies
that reduce environmental
impact, energy usage and
operational costs.
Predictive
Maintenance
Founded: 2009
HQ: New York, NY
Employees: 605
Invested Capital: $ 572M
Description:
Dataminr provides an AI
platform designed to discover
critical breaking information
from publicly available data sets
and deliver real-time alerts. The
company's platform detects,
classifies and determines the
significance of high-impact
events in real-time, providing
clients across industries with
the earliest updates on what
matters.
Event and Risk
Detection
Founded: 2010
HQ: Somerville, MA
Employees: 85
Invested Capital: $ 29M
Description:
Evergage is a cloud-based
software that allows users to
collect, analyze, and respond to
user behavior on their websites
and web applications in real-
time. It develops a cloud-based
platform for digital marketers
to increase engagement of
their visitors and users through
real time one-to-one
personalized experiences.
Employee
Engagement Platform
Founded: 2005
HQ: New York, NY
Employees: 2,823
Invested Capital: $1,016M
Description:
UIPath develops robotic
process automation software
designed to automate intricate
processes, enhance control,
enable cloud and on-premise
deployment, and provide
robust governance programs
on a single virtual machine.
Robotic Process
Automation
Driven by your success.Page 29Sources: Crunchbase, S&P CapitalIQ , Pitchbook*Prior to acquisition by Insight
Founded: 2015
HQ: San Francisco, CA
Employees: 91
Invested Capital: $238M
Description:
Freenome operates as a data-
driven health company that
brings accurate, accessible and
non-invasive disease
screenings to patients and
doctors. The Company’s
platform utilizes big data
analytics to detect oncoming
problems before they become
consequential.
Healthcare
Founded: 2013
HQ: San Francisco, CA
Employees: 1,145
Invested Capital: $238M
Description:
Darktrace develops a security
solution for organizations to
detect emerging cyber-threats
and defend them against
cyber-attacks. Its Enterprise
Immune System uses machine
learning and AI algorithms to
detect and respond to cyber-
threats across digital
environments.
Cybersecurity
Founded: 2013
HQ: Austin, TX
Employees: 235
Invested Capital: $ 187M
Description:
SparkCognition is a global
leader in cognitive computing
analytics. The Company's
technology is capable of
harnessing real time sensor
data and learning from it
continuously, allowing for more
accurate risk mitigation and
prevention policies to intervene
and avert disasters.
Cybersecurity
Founded: 2009
HQ: Somerville, MA
Employees: 500
Invested Capital: $69M*
Description:
Recorded Future provides real-
time threat intelligence
solutions to companies and
security professionals. It allows
Threat Intelligence Teams to
analyze emerging threats from
the entire Web and proactively
defend against cyber-attacks
and provides automated, real-
time threat intelligence.
Cybersecurity
Driven by your success.Page 30Sources: S&P CapitalIQ* Transaction not included in the Top 20 as it was a secondary round and investment amount was not publicly reported.Note: Top 20 private placements includes only horizontal ML platforms
Machine Learning-focused Private Placements
138 146
180
294
365
327
0
50
100
150
200
250
300
350
400
2014 2015 2016 2017 2018 2019
• Several horizontal machine learning platforms became newlyminted unicorns in 2019, including Databricks, Dataiku*, andDataRobot, with Dataiku reportedly climbing to a valuation of$1.4 billion following a secondary round where CapitalG acquiredshares from Serena Capital.
• While horizontal machine learning companies continued toreceive significant private placement investment in 2019,several vertical software companies also raised in excess of$100 million as they leverage ML/AI to refine their offerings.
• The largest private placement was a $500M investment inMission Lane led by Invus, Oaktree, and Goldman Sachs. MissionLane is a credit card service to subprime consumers that usesML to determine creditworthiness of its borrowers.
• Similarly, Sidewalk Infrastructure Partners received $400M fromAlphabet to further develop its ML/AI capabilities that drive itsanalysis of where to invest in and urbanize infrastructure.Sidewalk is a spin-out of Alphabet.
Deal Value($MM)
Databricks $400 Andreessen Horowitz 10/22/19
Databricks 250 Andreessen Horowitz 01/11/19
DataRobot 206 Sapphire Ventures 09/17/19
Indecomm 200 Warburg Pincus 08/13/19
Element AI 151McKinsey, BDC Capital, Quebec Investment Fund
and others09/13/19
Vectra AI 100 Technology Crossover Ventures 06/10/19
SparkCognition 100 March Capital Partners 10/08/19
Scale AI 100 Founders Fund 08/05/19
BenevolentAI 90 Temasek 09/15/19
Appier 80Insignia Ventures Partners, UMC Capital, and
others11/25/19
Advance Technology 80 Pavilion Capital and Gaorong Capital 09/19/19
Synspective 78 aStart 04/30/19
PathAI 75 Laboratory Corp of America 04/09/19
H2O.ai 73 Goldman Sachs and Ping An Ventures 08/20/19
Workato 70 Redpoint Ventures 11/11/19
CloudFactory 65 FTV Capital 11/20/19
Harness 60Institutional Venture Partners, GV and
ServiceNow Ventures04/23/19
LucidWorks 55 Francisco Partners and TPG 06/21/19
Vianai Systems 50 ND 09/12/19
Stradigi AI 40 Quebec Investment Fund 11/12/19
Lead Investor AnnouncedTarget
Ma
chin
e L
ea
rnin
g P
riv
ate
Pla
cem
en
ts
Driven by your success.
30 42
73
105
159
277
$0.7B
$4.9B$5.0B
$18.9B
$13.3B
$13.2B
0
2
4
6
8
10
12
14
16
18
20
0
50
100
150
200
250
300
2014 2015 2016 2017 2018 2019
Page 31
• Machine Learning and AI is the top-ranked theme for driving acquisition activityin 2019 for the third consecutive year, with greater than 75% of respondents in451 Research's Tech Banking Outlook Survey predicting another uptick in M&Aactivity.
• The majority of ML/AI acquisition targets to date have been companies thatapply ML/AI technology to a specific vertical or to solve a particular problem,such as LinkedIn’s acquisition of Drawbridge to improve its mobile and web userprofile matching capabilities.
• Financial buyers have been playing an increasingly large role, with PE-backedacquisitions accounting for 31% of the overall technology acquisitions in 2018,up from 14% in 2015
• As more tech giants like FAANG are releasing innovative AI and ML technologies,other tech giants are using M&A to catch up and for hiring (acqui-hires).
• The three largest acquisitions of ML-focused companies were: Prudential’sacquisition of Assurance IQ, Shopify’s acquisition of 6 River Systems, andMotorola’s acquisition of VaaS International.
• Other key M&A deals for the latest twelve months include:
‒ Sisense / Periscope
‒ Logi Analytics / ZoomData (1)
‒ DataRobot / ParallelM
‒ Great Hill Partners / EnterpriseDB (1)
‒ HPE / MapR
‒ Mastercard / SessionM (1)
‒ DataRobot / Paxata
‒ Boomi (Dell) / Unifi
• Characteristics of Desired Targets:
‒ IP or differentiating features
‒ Innovative and disruptive solutions
‒ Proven in the market
‒ Strong customer traction
‒ Easily integrated into existing platforms
Sources: 451 Research, S&P CapitalIQ1. Canaccord-advised transactions, including deals lead by senior team members at prior firms.
Machine Learning-focused Acquisitions
Selected ML/AI Acquirors to Date
Driven by your success.
Driven by your success.Page 33
Sales & Trading
Research
Asset Management
Investment Banking
200+ professionals
~2,250 institutions covered
globally
Sector focused franchises
Advisory & financing leadership
Recent acquisition of tech
advisory boutique Petsky Prunier
130+ professionals
~170 tech companies
covered
$60+ billion in AUM
Strong, recurring, growing
San Francisco
LondonMontreal
BostonNew York
TorontoCalgary
Vancouver
Houston
Beijing
Sydney
Hong Kong
Melbourne
DublinParis
Nashville
Dubai
Washington, DC
Perth
• International distribution footprint; US
research analysts market across all
geographies
• Publicly held
• $1.2 billion revenue
• Profitable, growing
• ~2,000 employees
• 80 tech banking professionals
• 20 Managing Directors globally
• Recent acquisition of Petsky Prunier
reinforces commitment to growing tech
• 75+ transactions completed in the last twelve
months
Senior attention
Deep, tenured team
Sector expertise
Client references validate
Scaled, Independent
Full Service
Global Reach
Technology is our Largest Banking Practice
Driven by your success.Page 34
Director, Software ResearchBoston
US Director of
Research, Internet
& Fintech
New York
Managing Director,
Head of MENASA
Dubai
CEO & Managing
Director
Melbourne
Managing Director,
Software Research
Boston
Global
Executive Leadership & Support
Managing Director,
President
Boston
Managing Director,
Co-Head of Technology
Boston
CEO, Canaccord
Genuity
Toronto
Managing Director,
Co-Head of US IB
Boston
Managing Director,
Co-Head of US IB,
Co-Head of
Technology
New York
US
Managing Director,
Technology M&A
Boston
Managing Director,
Technology
Boston
Managing Director,
Technology
New York
Managing Director,
Technology
New York
Managing Director,
Technology
New York
Managing Director,
Technology
New York
Managing Director,
Technology
New York
Managing Director,
Technology
Boston
Managing Director,
Technology
Boston
Managing Director,
Technology
New York
Managing Director,
Technology
New York
Managing Director,
Technology
New York
Managing Director,
Technology
New York
Managing Director,
Equity Capital Markets
Boston
Managing Director,Head of European IB& Head of European TechnologyLondon
Vice Chairman,TechnologyTel Aviv
Managing Director,
Head of Canadian
Technology IB
Toronto
Head, US FinancialSponsors GroupNashville
Managing DirectorHead, US M&ASan Francisco
Managing Director,
Equity Capital Markets
Boston
Research
Thought Leadership Product PartnersECM
Managing Director,
Technology
Chicago
80 person team
Global presence
Software, Digital Media, Marketing, Information Services, IOT
Top ranked advisory practice
Association with high profile IPOs
CG’s Technology Banking Team
Managing Director,
Technology
New York
Director,
Technology
San Francisco
Managing Director,
Technology
Toronto
Driven by your success.
Rank Firm Name # of Transactions
2019
1 Canaccord Genuity 42
1 Raymond James 42
3 William Blair 41
4 Goldman Sachs 36
5 Jefferies 31
5 Piper Jaffray 31
7 Bank of America 26
8 Robert W. Baird 25
9 Morgan Stanley 23
9 Needham 23
11 JP Morgan 21
12 Houlihan Lokey 20
12 Stifel Financial 20
Page 35
Financial advisor on sale to
Financial advisor on sale to
Financial advisor on sale to
League Table Selected Recent Transactions
Financial advisor on acquisition of
Financial advisor on recapitalization by
Financial advisor on sale to
Financial advisor on investment from
Financial advisor on sale to
Financial advisor on sale to
Financial advisor on sale to
Financial advisor on sale to
Financial advisor on recapitalization by
Financial advisor on sale to
Financial advisor on sale to
Financial advisor on acquisition of
Financial advisor on sale to
Financial advisor on sale to
Financial advisor on sale to
Financial advisor on recapitalization by
1. Mid-market defined as announced deals below US$500 million. Source: Freeman Consulting Services based on data from Refinitiv. Includes deals lead by Petsky Prunier prior to acquisition.
U.S. Mid-Market(1) TMT Advisory
and subsequent acquisition of
Financial advisor on recapitalization by
Driven by your success.Page 36
Financial Advisor on sale to
$104,400,000
ManageIQ provides
cloud and virtual
infrastructure
management
software
Financial Advisor on sale to
Arkeia Software
provides data
backup and disaster
recovery software
Financial Advisor on acquisition of
$31,400,000
Datawatch
develops, markets
and distributes data
management
software
Financial Advisor on sale to
Parature provides
cloud-based
customer service
solutions
Financial Advisor on sale to
Convey Computer
develops hybrid-
core computing
solutions
Financial Advisor on sale to
$620,800,000
Anite provides products
and network testing
systems to the wireless
market
Financial Advisor on sale to
Enterprise DB
provides enterprise-
class open source
products
Financial Advisor on sale to
$340,900,000
Com Dev designs,
manufactures &
distributes wireless
space technologies
Financial Advisor on sale to
$122,000,000
Openwave
Messaging provides
secure messaging
solutions for telco
customers
Stradigi develops
Kepler, an ML-based
analytics subscription
software
Financial Advisor on sale to
Cloud Cruiser provides
software solutions to
manage hybrid cloud
environments
Note: Includes certain transactions completed by Petsky Prunier and Scott Card while at AGC.
Financial Advisor on sale to
Cask specializes in
building solutions to
run on big data
analytics platforms
Financial Advisor on sale to
mTAB provides
database services,
data analytics and
data visualization
solutions
Financial Advisor on investment from
$61,600,000
Ecobee develops
intelligent energy
management
solutions
Financial Advisor on sale to
Codeship provides
hosted continuous
integration solutions
Financial Advisor on sale to
$441,100,000
Sandvine provides
network intelligence
products for
network operators
Financial Advisor on recapitalization and
subsequent acquisition of
Motus provides
mobile and web-
based workforce
management SaaS
Financial Advisor on sale to
$105,000,000
Connance provides
predictive analytics
technology
solutions
Financial Advisor on sale to
kSaria produces and
supplies fiber optic
interconnect
products
Financial Advisor on acquisition of
I.D. Systems provides
Wireless M2M solutions;
CarrierWeb provides in-
cab mobile
communications
technology
Financial Advisor on sale to
Leverton provides an AI-
powered data extraction
solution for real estate,
legal, and corporate
documents
Financial Advisor on sale to
Zoomdata develops and
deploys data visualization
and analytics systems for
big data
Co-Financial Advisor on sale to
Enterprise DB develops
and provides enterprise-
class products and
services based on
PostgreSQL
Financial Advisor on sale to
Reis provides
commercial real
estate information
and analytic
solutions
Financial Advisor on sale to
Tendril provides AI-
based energy
services
management
solutions
Financial Advisor on sale to
TIS develops and
markets automated
data capture
solutions
Financial Advisor on acquisition of
Digi provides Internet of
Things connectivity
products, services, and
solutions
$140,000,000
Financial Advisor on minority recapitalization
by
CAD 53,000,000
Driven by your success.
● Open source (Postgres) database, analytics software
● Strong industry tailwinds – Postgres named “Database of the Year” for the second consecutive year by leading independent research firm
● Growing 25%; EBITDA positive● Participation by both strategic and
financial buyers● 6x+ revenue multiple● Two-time advisory client
Financial advisor on sale to
● Fully automated customer relationship management software for local SMB and Franchise firms
● 5,700+ SMB and Franchise customers, $20 MM+ in recurring revenue
● Drove expediated due diligence process, navigating crucial closing conditions while maintaining all key terms agreed to at exclusivity
● Investment will let Signpost scale its footprint & extend leadership in technology for local businesses
Financial Advisor on investment from
● Commercial real estate (CRE) data and analytics for real estate professionals
● Involved over 100 strategic and financial parties, including a number of inbound inquiries – led to 7 indications of interest
● Significant tactical advisory navigating the public process and final negotiations
● $278MM deal represents a 31% share premium to the day prior to announcement
Financial Advisor on sale to
$278,000,000
● Vertical predictive analytics platform for health systems and BPOs
● Provides solutions for revenue cycle management, patient pay optimization, vendor management and reimbursement management
● Growing 17%, EBITDA positive● Catalyzed by an inbound offer,
process included market check with 12 qualified strategic buyers
● $105m+ enterprise value (exceeded top end of inbound offer range) equal to 5.8x LTM revenue
Financial Advisor on sale to
$105,000,000
● Cloud-based event management platform for venues and patrons
● Helps venues to maximize revenue, better engage with fans and patrons and run efficient and profitable operations
● Project catalyzed by inbound interest from strategic and financial parties
● Robust transaction process drove highly efficient timeline (<5 months launch to close), well vetted group of formal bidders and premium value outcome
Financial Advisor on sale to
● RFP and sales proposal software platform
● Growing 10%, EBITDA positive● Received 11 proposals● 2 step process, in phase 2 winning
bidder moved up 15%+ on price● Due to highly competitive bidding
dynamic, able to get buyer to fully fund Rep & Warranty insurance policy – no escrow, seller friendly terms
Financial Advisor on sale to
$50,000,000
● Vehicle mileage measurement and reimbursement software platform
● Canaccord proprietary idea to TB –recapitalize Motus and simultaneously purchase all of Runzheimer
● Process message - premium value for Motus warranted (~$150m), platform that optimizes synergies
● Both Motus and Runzheimer were founder controlled
● $400M+ combined EV and 7.8x revenue multiple for Motus
● Founder controlled; education, non-profit vertical software
● Helping institutions optimize, grow fundraising utilizing benchmarking and analytics
● Initial inbound interest from Blackbaud and RNL, leveraged to create a more thorough market for the Reeher
● Strong interest from both financial and strategic buyers – 13 proposals
● $43MM purchase price (23% increase from initial offer)
Financial Advisor on sale to
$43,000,000
● Marketing trade spend management software for CPG industry
● Focus was on finding a financial partner to recap the business with founder continuing with NewCo
● To satisfy long-term shareholders, conducted market check with a highly targeted group of strategic buyers
● Received 10 proposals● Winning bid was approximately 50%
increase from IOI (fully buyer funded R&W insurance policy)
Financial Advisor on sale to
● Online and mobile group organizational software tool broadly used within the education and non-profit verticals
● Over 60m registered users; mixed business model – software, advertising, payments
● Growing 80%+, EBITDA margin 15%+● Significant inbound interest from a
variety of buyer types● Winning bid was 43% increase from
initial bid, 6.6x revenue multiple
Financial Advisor on investment from
Page 37
Financial Advisor on acquisition of
Financial advisor on recapitalization by
Note: Includes transactions led by senior bankers with prior firms.
Driven by your success.Page 38
Highlights
• 25+ years experience, amongst the longest tenured on
Wall Street
• In-depth research, not reporting, adds real value,
unique perspective
• Engaging personality and writing style
• Broad following and deep software relationships
• Ability to leverage deep sector and company specific
knowledge
Focused on next generation disruptive cloud platforms
Enterprise ProductivityMarketing Tech
Human Capital Mgmt.Infrastructure & Security
Analytics & Big DataCategory Leaders
Verticals
Senior Analyst
Boston, MA
Senior Analyst
Boston, MA
Software Research Coverage:
Note: Research coverage decisions are made exclusively by research management and the individual analyst.
Same senior team, same firm, 10+ years
Driven by your success.Page 39
Top ranked underwriter
each of the last 5+ yearsSame CG team 10+ years Global distribution
100+ completed
transactions
Highly respected
research
Driven by your success.Page 40
Canaccord Genuity is the business name used by certain subsidiaries of Canaccord Financial Inc., including Canaccord Genuity LLC, Canaccord Genuity Limited, and Canaccord Genuity, a division of Canaccord Financial Ltd. Canaccord Financial LLC is listed on the TSX and LSE.
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