private & confidential · the global market for machine learning as a service (“mlaas”)is...

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)


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


▪ 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.


Human Only

Human + Data

Informed

Human + Machine

Assisted

Machine Only

Inte

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lgo

rith

mic

Re

aso

nin

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


• 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


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?


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


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


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


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


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.


Founded: 2008

HQ: Menlo Park, CA

Employees: 60


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


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


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


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


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


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


Employees: 1,100


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


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


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


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


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


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


Employees: 205


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


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


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


Employees: 48


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


▪ 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)


Stream Processing/Real-Time Analytics

Founded: 2014


Employees: 1,000


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


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


Employees: 150


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


Employees: 170


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


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.


▪ 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)


Founded: 2012

HQ: San Mateo, CA

Employees: 1,620


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


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


Employees: 180


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


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


Employees: 95


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)


Broader BI / Search / Data Analytics

Founded: 2005

HQ: New York, NY

Employees: 750


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


Employees: 290


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


Employees: 275


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


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.


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


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


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.


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


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


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


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


Employees: 91


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


Employees: 1,145


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


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


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


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


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


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



League Table Selected Recent Transactions

Financial advisor on acquisition of

Financial advisor on recapitalization by


Financial advisor on investment from








Financial advisor on acquisition of





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 sale to

$104,400,000

ManageIQ provides

cloud and virtual

infrastructure

management

software


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


Parature provides

cloud-based

customer service

solutions


Convey Computer

develops hybrid-

core computing

solutions


$620,800,000

Anite provides products

and network testing

systems to the wireless

market


Enterprise DB

provides enterprise-

class open source

products


$340,900,000

Com Dev designs,

manufactures &

distributes wireless

space technologies


$122,000,000

Openwave

Messaging provides

secure messaging

solutions for telco

customers

Stradigi develops

Kepler, an ML-based

analytics subscription

software


Cloud Cruiser provides

software solutions to

manage hybrid cloud

environments

Note: Includes certain transactions completed by Petsky Prunier and Scott Card while at AGC.


Cask specializes in

building solutions to

run on big data

analytics platforms


mTAB provides

database services,

data analytics and

data visualization

solutions

Financial Advisor on investment from

$61,600,000

Ecobee develops

intelligent energy

management

solutions


Codeship provides

hosted continuous

integration solutions


$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


$105,000,000

Connance provides

predictive analytics

technology

solutions


kSaria produces and

supplies fiber optic

interconnect

products


I.D. Systems provides

Wireless M2M solutions;

CarrierWeb provides in-

cab mobile

communications

technology


Leverton provides an AI-

powered data extraction

solution for real estate,

legal, and corporate

documents


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


Reis provides

commercial real

estate information

and analytic

solutions


Tendril provides AI-

based energy

services

management

solutions


TIS develops and

markets automated

data capture

solutions


Digi provides Internet of

Things connectivity

products, services, and

solutions

$140,000,000

Financial Advisor on minority recapitalization

by

CAD 53,000,000


● 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


● 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


● 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


$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


$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


● 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


$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)


$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)


● 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


Page 37



Note: Includes transactions led by senior bankers with prior firms.


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


Top ranked underwriter

each of the last 5+ yearsSame CG team 10+ years Global distribution

100+ completed

transactions

Highly respected

research


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