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THE ENTERPRISE ALMANAC
2018 EDITION
#2018ALMANAC
By Michael Yamnitsky
2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC
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THE WORK-BENCH ENTERPRISE ALMANAC - 2018 EDITION
PREAMBLE
For the past five years at Work-Bench, we've been investing in a total reimagining of the enterprise technology
stack.
We're in the midst of a once in a decade tectonic shift of infrastructure that powers the Fortune 1000 and is
unlike anything we've seen before. Whereas consumer tech has the Mary Meeker Internet Trends report for an
aggregate view of industry trends, enterprise technology was missing a comprehensive overview of the key
trends. Last year we took action and launched the inaugural Enterprise Almanac to share our thinking on these
trends, and now we're pleased to release the updated 2018 Edition.
Our primary aim is to help founders see the forest from the trees. For Fortune 1000 executives and other
players in the ecosystem, it will help cut through the noise and marketing hype to see what really matters. It's
wishful thinking, but we also hope new talent gets excited about enterprise after reading this report. By no
means will most of the predictions be correct, but our purpose is to start the discussion by putting this stake in
the ground.
Please share any and all feedback via email at michael@work-bench.com or on Twitter at @ItsYamnitsky.
MICHAEL YAMNITSKY
Venture Partner, Work-Bench
2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC
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ABOUT WORK-BENCH
PREAMBLE
ABOUT US
Work-Bench is an enterprise technology focused venture fund based in NYC.
OUR THESIS
Customer-centricity. We make it our focus to deeply understand the business and IT needs of the Fortune 1000 in order to make more informed decisions
in our search for the next enterprise giants. This is highly informed by our backgrounds in corporate IT at leading Wall Street banks and as Industry
Analysts which is unique in the venture business.
OUR MODEL
Our model flows directly from our thesis. We leverage our deep corporate network in New York City and beyond as a way to identify trends, pick the
winners, and secure customers for our portfolio companies.
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TABLE OF CONTENTS
PREAMBLE
2018 Macro Perspective: The Empire Strikes Back
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Vertical Theme #1: Machine Learning
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Vertical Theme #2: Cloud Native
56
Vertical Theme #3: Cybersecurity
87
Vertical Theme #4: Decentralization of SaaS
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Team Work-Bench
Jonathan Lehr, Jessica Lin, Vipin Chamakkala, Kelley Mak, Kelley Henry, and Blake Jesse who added significant contributions and healthy debate for the
content of this presentation. Thanks to Tommy Truong for his design work.
Work-Bench Founders and CEOs
For keeping me honest and never failing to surprise us with where technology can take us on this pale blue dot.
SPECIAL THANKS
PREAMBLE
Our views are shaped by anecdotal evidence based on our interactions with entrepreneurs, Fortune 1000 executives, and big tech leaders. Take that for
what it's worth. We have disclosed our investments where appropriate.
Disclaimer
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2018 MACRO PERSPECTIVE
The Empire Strikes Back
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OLD GUARD
On-prem Empire
VS.
10 YEARS AGO IN A GALAXY FAR, FAR AWAY
MACRO PERSPECTIVE
NEW GUARD
Cloud Rebel Alliance
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MEGACLOUD
Bully Bazaars
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OLD GUARD
GROWTH GUARD
Multi-cloud momentum fighters
Early-stage
startups + open-
source
Growth guard and old guard haven't peaked yet, with fight in them to go head to head with new guard
TODAY, REBEL ALLIANCES FACE A MORE NIMBLE SET OF
CONTENDERS
MACRO PERSPECTIVE
VS.
NEW GUARD
VS.
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PUSHING SHARP INVESTORS TO RETHINK COMPETITIVE
ADVANTAGE
MACRO PERSPECTIVE
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MEGACLOUD AGGRESSION CONTINUES FULL FORCE
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Source: http://tomtunguz.com/mid-2018-ma/
M&A weighing in high on the scale
these days with two of the biggest
deals of the last 6 years closed in
the last 12 months
MACRO PERSPECTIVE
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REALITY: MEGACLOUDS = CHEAP WITH CASH; "BIG" M&A IS
RELATIVE
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MACRO PERSPECTIVE
Source: Goldman Sachs
Megaclouds spend a paltry
3% of cash on M&A; plenty
of room to step up spending
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Let's briefly speculate on megacloud M&A
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AMAZON OR GOOGLE TO BUY SALESFORCE AS A KILLER APP
IN THE CLOUD?
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MACRO PERSPECTIVE
Why not
Feeling the brunt of Microsoft
undercutting Dynamics to sell customers
on going all in with Azure
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MICROSOFT TO BUY OKTA TO REGAIN OWNERSHIP OF
"ENTERPRISE" IDENTITY?
MACRO PERSPECTIVE
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Continues acquiring companies to regain identity foothold it
lost in cloud
Professional identity
Personal identity
Developer identity
Enterprise identity
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What about AI and its promise to reshape
technology and transform business as we
know it?
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Systems of Intelligence are highly
focused analytical systems
intended to solve business
challenges and objectives (i.e.
increase revenue and customer
experience, improve operations,
reduce risk)
Value created by:
Integrating data from multiple sources include non-
tradition information rich channels
Novel new forms of data capture
Cleverly optimizing the data preparation and AI training
process
Value created by:
Embedding domain experts into the debugging and
hyper-parameter tuning process
Incorporating feedback from human experts into the
system of record (SOR)
Value created by:
Designing products from data capabilities up to user
experience and not the other way around
Software UI as invisible as possible > fancy GUIs. Name
of the game is making the workflow as seamless as
possible.
Original Framework Source: Jerry Chen's "The New Moats - Why Systems of Intelligence are the Next Defensible Business Model"
2017 = STARTUPS PROVED OUT SYSTEMS OF INTELLIGENCE
(SOI) MODEL FOR AI
MACRO PERSPECTIVE
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Data moat?
Algorithmic differentiation?
Data product differentiation?
Weaker: still a significant barrier, but
it's faster to develop and thus harder
to sustain a data moat.
Weaker: tough to sustain with open
source, but there is some value in
novel training, profiling, debugging,
and testing processes.
Competitive forces will be in flux as the AI landscape continues to develop at rapid speed.
Here is where things currently stand and directionally where they are going:
Stronger: The key value driver
moving forward is developing
products bottoms up, from data
and analytical capabilities to
features and user experience, and
creating a virtuous loop between
the two.
Direction = whether this factor will be more or less significant 12-24 months from now
Locus of focus shifting from the
quantity you own to the process
you use to sustain these assets*
*For more on this topic, see Matt Turck's "The Power of Data Network Effects"
AS MENTIONED LAST YEAR, COMPETITIVE FORCES CONTINUE
TO BE IN FLUX
MACRO PERSPECTIVE
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Arguably the king of SOI at the moment with very
impressive suite of AI-powered BI offerings under
'Einstein' umbrella.
Google leveraged its AI army to build Contact
Center AI, with a suite of capabilities to automate
customer support.
Startups proved SOI in the form of point solutions, but larger vendors are coming in to stitch together capabilities into more
holistic AI-powered software "suites"
+1 for big tech, -1 for startup competitors
CREATING THE PERFECT WINDOW FOR MEGACLOUDS TO
#WIN IN SOI RACE
MACRO PERSPECTIVE
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But wait isn't the Achilles Heel of
megaclouds a lack of focus on the details
of real-world applications?
well Google is proving us all wrong
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2018 = AI startups retreating to the
deep 'niches' to generate new
competitive powers
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COMING OUT THE WOODWORK: AI BIZ PROCESS AUTOMATION
AS AN API
MACRO PERSPECTIVE
Example:
Typical KYC/AML workflow:
Information
check
Merlon automates all of this like Stripe automates payments
Making financial AML and KYC compliance workflows as
seamless to set up as Stripe, and as easy for analysts to use as
TurboTax
Simple checklist for analysts to monitor progress
Sanction
list
screening
Negative
news
screening
Ongoing/
transaction
monitoring
*Merlon Intelligence is a Work-Bench portfolio company.
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Vertical Theme #1
MACHINE LEARNING
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TL;DR
MEGACLOUD AGGRESSION CONTINUES FULL FORCE
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To sell to BI? Data scientists? Or straight to the business? No matter who the buyer is, there
continues to be a disconnect between buyers and sellers of data science tools. Crack the code
and you have a billion dollar business in sight.
The Empire Strikes back. Large technology companies are #winning at AI.
Despite hopeful promise, startups racing to democratize AI are finding themselves stuck between
open source and a cloud place.
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3
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MACHINE LEARNING
FUTURE OF BUSINESS = DATA-DRIVEN (DUH)
"By 2021, insights-driven business will steal $1.8 trillion a year in
revenue from competitors that are not insights-driven"
Source: https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848
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MACHINE LEARNING
TO FUEL THE FIRE, DATA COLLECTION CONTINUES TO GROW
UNABATED
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90% of data in existence was created in
the last 24 months
Lying in these data sets are keys to
advances in medicine, energy, etc.
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Demand for "Machine Learning Engineers" and "Data Scientists" has grown 6-10x in last 5 years
Note: growth rates are calculated based on number of job postings on LinkedIn.
"Trust me, I can explain"
MACHINE LEARNING
BUT THERE'S A SHORTAGE OF DATA SCIENTISTS TO MAKE SENSE OF IT ALL
Source: LinkedIn's Emerging Jobs Report
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Sources: https://www.pwc.com/us/en/library/data-science-and-analytics.html, McKinsey Global Institute "The Age of Analytics: Competing In A Data-Driven World."
McKinsey Global Institute predicts the US
economy will be short 250k data scientists
by 2024.
MACHINE LEARNING
ENTERPRISES HAVE LOTS OF ANALYSTS, NOT ENOUGH DATA SCIENTISTS
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Most AI talent works here
to optimize the output of
MACHINE LEARNING
BECAUSE THE MEGACLOUDS SCOOP UP ALL THE BEST TALENT
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65% of execs are bullish
about the potential of AI in
their business
But only 39% believed
adoption will grow considerably
in 2018
Source: https://www.forbes.com/sites/adigaskell/2018/03/07/employees-optimistic-about-working-with-ai
MACHINE LEARNING
2018 ENTERPRISE AI SENTIMENT = PESSIMISTIC OPTIMISM
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"Suits and Hoodies" at Goldman Sachs
Ambitious attitudes: "AI is a competitive differentiator. We
want to own the model, we don't want Palantir to own it."
Realistic recruiting tactics: Avoiding talent wars with the
web-scales by expanding hiring in India, and hiring Masters-
levels rather than PhDs.
Healthy skepticism: "We have lots of existing regression
models that are finely tuned. Deep learning for many use
cases is just going to be incremental and more expensive
right?"
Source: Quotes from interviews with machine learning executives at top-tier Wall Street banks
MACHINE LEARNING
BUT THEY ARE GETTING INTO GEAR TO DO IT RIGHT
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#3: BUSINESS USER SELF-SERVICE
#2: 'CITIZEN' DATA SCIENTISTS
#1: DATA SCIENTISTS
Central data science group delivering
insights to the business.
Business intelligence groups using
GUI-based AutoML tools to deliver
ML insights without the help of data
scientists.
Example Enterprise
Example Enterprise
Business people using software with
pre-packaged ML models and
reporting dashboards to generate ML
insights.
Source: Work-Bench estimates based on interviews with F1000 IT leaders.
Example Enterprise
Top Tier Bank
MACHINE LEARNING
3 MODELS FOR DATA-DRIVEN INSIGHTS
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Data scientists take an open source first approach to tooling and will
only pay for cheap point solutions that are easy to use and solve a very
particular need.
IT still has a bad taste in their mouth from failed big data projects. Black
boxes are a definite NO!
Business execs can barely use Tableau without an analyst's help,
nevertheless attempt a new tool.
85% of Big Data
projects fail* per Gartner
MACHINE LEARNING
NO FREE LUNCH FOR VENDORS, EACH BUYER HAS QUIRKS AND BAGGAGE
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I.E. they don't spend much on commercial software
The industry is leaving SAS and going OSS
MACHINE LEARNING
DATA SCIENTISTS: TINKERERS WHO PREFER FLEXIBILITY/CONTROL OF OSS
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Source: S. Zayd Enam, Stanford AI Lab
Root-cause analysis in AI is vastly more complex than regular software
Algorithm design Implementation
Two dimensions of investigation:
Algorithm design

Implementation
Four dimensions of investigation:
Algorithm design

Implementation
Choice of model
Data
It takes more experience to tune/debug machine-learning algorithms efficiently than to tune/debug regular code
and longer time cycle testing the fix
Software development = hours
Machine learning = days
Why?
Re-training algorithm on dataset is time consuming,
pushing a code change to production is not.
MACHINE LEARNING
BUT SINCE ML STILL AIN'T EASY
2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC
Model production,
deployment, and
monitoring
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Modeling = easier to automate
Data prep + model deployment = hard to automate
MACHINE LEARNING
STARTUPS ARE BUILDING AUTOMATION TOOLS SPANNING THE ENTIRE PIPELINE
Model and
hyper
parameter
configuration
selection
Model
training and
performance
review
Modifications to
learning rate,
regularization,
feature pre-
processing, etc.
Feature
engineering
Data prep/ETL
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MACHINE LEARNING
LANDSCAPE OF VENDORS (AND OPEN SOURCE PROJECTS) IN ML AUTOMATION
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How vendors market their AutoML products:
How AutoML is actually perceived:
Because most of a data scientist's time is spent
here
MACHINE LEARNING
VENDORS AUTOMATING MODEL DEVELOPMENT TALK A VERY BIG GAME BUT
Production
AutoML
Prep
How AutoML is actually perceived:
Production
AutoML
Prep
Data Scientists generate most of their value much later when they apply predictions to
figure out "what it means" for the business.
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"Our best bet are smart engineers, not AutoML tools. We need to scale our data
science organization significantly to see real lift from these tools. Although there are
areas where AutoML can help us, we cannot justify the investment."
- Chief Data Officer at F500 eCommerce company
"Hyper-parameter tuning [fancy term for time-intensive part of model generation]
will soon be a commodity."
- Head of Data Science at a bulge bracket bank
MACHINE LEARNING
PERCEIVED VALUE OF AUTOML = NOT HIGH ENOUGH TO JUSTIFY MAJOR SPEND
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Soon
Soon
Soon
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Cloud Composer
Data Factory
Batch AI
"We will wait for what Amazon releases in this space"
- Chief Data Officer at F500 eCommerce company
MACHINE LEARNING
MEANWHILE, MEGACLOUDS HAVEN'T BEEN SITTING IN THE PEANUT GALLERY
Data prep/ETL
Feature
engineering
Model training
and tuning
Model production,
deployment, and
monitoring
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AutoML startups are tweaking their products and messaging to win over the Tableau wielding BI analyst crowd
MACHINE LEARNING
HENCE AUTOML CO'S RE-ORIENTING PRODUCTS TOWARDS THE BI ANALYST
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MACHINE LEARNING
WHEN YOU TRY TO CREATE PRODUCTS FOR MULTIPLE STAKEHOLDERS WHEN
PLEASING ONE CAMP IS TOUGH ENOUGH
Data scientists/ML engineers
Business analysts
No code GUIs
Guardrails and 'training wheels'
for inexperienced users
Technical products
Detailed feature sets and full
extensibility
No code GUIs, but complex
for an unsophisticated user
You get stuck in the middle
*Algorithmia and Datalogue are Work-Bench portfolio companies.
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Expect all modern BI vendors to release an AutoML product or buy a startup by end of next year
Tableau bought Empirical Systems
for AutoML
Looker customers can now do AutoML
with new Google BigQueryML integration
Source: https://techcrunch.com/2018/06/13/tableau-gets-ai-shot-in-the-arm-with-empirical-systems-acquisition/
MACHINE LEARNING
BI VENDORS ARE COMING IN HOT
Alteryx is known for its GUI-based
ETL tool
In 2017 it acquired data science
startup Yhat
Our hunch: Alteryx releases a GUI-
based AutoML tool in early 2019
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These AutoML vendors just need a break
Anyway, moving on
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The 'big data architect' got fired and
was replaced with the data infra
engineer, who's ready to propose a
more nimble and distributed approach
to developing an enterprise data
platform for the business.
Source: https://blog.cerebrodata.com/modern-self-service-data-platforms-c7c90a789068
MACHINE LEARNING
MOVE OVER BIG DATA ARCHITECT, DATA INFRA ENGINEERS COMING IN
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A new category dubbed "customer data platform" is emerging as a purpose-built data platform to help marketers be more
data-driven without the help of special internal groups
What they do today
What they will do in the future
Example vendors:
1. Collect data from multiple sources and organize them into
constantly updating customer segments.
2. Allow marketers to program rules for how each segment
received email automation programs.
Automate more of what a data scientist can do for them:
1. Build AutoML features to allow marketers to make predictions like
whether a customer will churn or migrate to another segment so they can
take preventive measures.
2.
Incorporate more general purpose BI functionality to merge the now
disparate data management, analytics, and marketing execution
functions.
MACHINE LEARNING
+ VERTICAL-SPECIFIC DATA PLATFORMS = BYPASSING DATA SCIENTISTS + BI
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Source: https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#4294c0036f63
Data scientists live in data cleansing and hate it
Proliferation of cross-data set analytical
projects. BI and data science teams are
becoming more advanced, forcing IT to rip the
bandaid off their failed data management
initiatives and prepare disparate data sets for
analysis. They are starved for any solution that
can help them do it in an automated way!
Interest in unstructured data. Most enterprise
data is unstructured, and the promises of AI
have all sorts of stakeholders in the enterprise
wondering how they might be able to now
leverage it.
MACHINE LEARNING
RAMPANT ENTERPRISE INTEREST IN AUTOMATED DATA PREP THIS YEAR
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Data scientists rarely have strong engineering backgrounds and need help with the manual coding required to make
their models API accessible to the rest of the business.
=
"We made 20 models last year. Only two made it to production!" Chief Data
Scientist at a major Wall Street Bank.
New tools are automating the process of model deployment much like Heroku automates Ruby app deployments
MACHINE LEARNING
MODEL DEPLOYMENT = RACE TO BE THE HEROKU OF ML
*Algorithmia is a Work-Bench portfolio company.
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We couldn't talk enterprise AI this year
without mentioning this guy
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Source: Morgan Stanley "2018 Outlook: Riding the Software Wave," January 2018.
MACHINE LEARNING
FROM ZERO TO HERO: SALESFORCE HAS NO MINDSHARE IN AI
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Source: Salesforce Dreamforce Keynote, 2017
MACHINE LEARNING
BUT SALESFORCE HAS KEPT BUSY BUYING UP AI STARTUPS THE LAST 3 YEARS
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GUI-based ML model builder to predict
any blank field in SFDC
Salesforce has stitched its newly acquired assets into a 100% automated machine learning pipeline
Source: Salesforce Marketing Materials
Note: unlike DataRobot and
Dataiku, Einstein has the proper
guardrails to ensure business
users do not make mistakes like
introducing target data leakage
MACHINE LEARNING
AI ELITISTS WILL STICK UP THEIR NOSES, BUT WE THINK SALESFORCE AI IS
IMPRESSIVE
Out of the box
apps like a sales
lead scorer
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Quick peek into the future
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Today's BI tools claim to democratize BIbut they hinge on an ideal, unrealistic requirements.
They require a user who is capable of asking the:
Right question. Asking the right question requires analytical skills. This is a hard ask for the nondata pros that make up the majority of employees.
Correctly. Most BI tools have "intuitive" GUIs, but they still require extensive training to use and thus users end up using them incorrectly.
On the right data. The user must have access to the right data and prerequisite knowledge about the underlying data structure to ask the right
question in the first place.
TL;DR: skilled business analyst remain the conduit through which the rest of the business
interfaces with analytics. And vendors are constantly overthrown whenever analytics
projects go haywire.
MACHINE LEARNING
BI HAS HISTORICALLY BEEN A MESSY INDUSTRY
1
2
3
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Pointing you to anomalies in your
data and providing a diagnosis
And suggestions for how to respond to data
trends as well as guidance on how to run small
experiments on your data so the system can tune
itself and better respond when an anomaly occurs.
with a natural language interface to ask
clarifications and guide the analysis
'Actionable insights engines' will fully automate the data scientist by directly linking cause and causality in business outcomes
MACHINE LEARNING
'ACTIONABLE INSIGHTS' IS THE FUTURE OF BI (AND ALL SW FOR THAT MATTER)
3
2
1
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Make your product so easy a 3 year old can use it! Drop the feature bloat you'll die before you become everything for everyone.
Incremental algorithmic advances are hitting a plateau. Most of the unique value in your product will come down to the user experience.
Don't build for multiple buyer personas. Not only is it hard enough to build something in the data science space with basic product market fit,
but buyers are skeptical of collaboration platforms; "[X vendor's] collaboration features were never interesting to us because we don't believe
collaboration between data science and BI teams is possible." - Head of ML & Analytics at a bulge bracket bank.
Play nice with everyone. The space is crowded and it may seem like a war zone out there with large tech companies in adjacent areas making a
splash in the space. Remember that the TAM is massive and no one has truly cracked the code here. Many can win. The market dynamic will be similar to
BI, where there are multiple BI platforms with 90% functionality overlap. Integrate with everyone relevant, starting with the major BI vendors.
Try going vertical. The exit opportunity won't be as large but at least you won't be competing with the million other startups attempting to be the
Salesforce of data science. Take a more calculated use case driven bet, and make the solution truly closed loop.
MACHINE LEARNING
KEY TAKEAWAYS FOR EARLY-STAGE ML STARTUPS
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Vertical Theme #2
CLOUD NATIVE
With contributions by:
Vipin Chamakkala
Principal at Work-Bench &
Cloud Native Sector Lead
@V1P1N
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Microsoft is making inroads in the enterprise. Google is still behind (for now)
Vendor lock-in fears steer enterprises towards multi-cloud
Cloud native story continues to unfold with digital transformation initiatives driving the inexorable
shift
Site reliability engineering (SRE) model is all the rage for operating the cloud. The battle has
begun to be the Salesforce of SRE in cloud era
Serverless continues to be a disruptive force, but more so for vendors than end customers today
1
TL;DR
CLOUD PREDICTIONS
2
3
4
5
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PRODUCT STRATEGY
The monocloud that's good enough for most things, not
amazing for anything. Heading down proprietary path as
most services are integrally tied to their public cloud
architecture.
GTM STRATEGY
Aggressive enterprise sales: lock-in, land-and-expand.
BIG EXISTENTIAL QUESTION
Amazon can't allocate 30 top PhDs to solve a single problem.
Google can.
PRODUCT STRATEGY
Play to internal strengths: Underserved enterprise workloads
like legacy Microsoft products, platform and application
services for modern enterprise apps.
GTM STRATEGY
Strong enterprise support model.
BIG EXISTENTIAL QUESTION
Will enterprise chops trump Amazon's scale and scope?
PRODUCT STRATEGY
Google shines strength in machine learning, developer
tools, and container orchestration (Kubernetes).
GTM STRATEGY
Historically Google hasn't catered to the enterprise with
sales & support. Google Kubernetes Engine on-prem
signaling a change?
BIG EXISTENTIAL QUESTION
Despite the incredibly nice people we meet at Google,
enterprises feel Google is still arrogant towards them. How
does Google change their rep?
PLAYER #1 (CATEGORY LEADER):
THE ENTERPRISE COSTCO
PLAYER #2 (FOR NOW):
ENTERPRISE HERITAGE
PLAYER #3 (KILLER PRODUCTS):
BUT WHERE'S THE ENTERPRISE LOVE?
Besides a few serious regional players like Alibaba, global enterprises have 3 main marketplace bazaars to choose from to power their digital transformation:
TL;DR
MEGACLOUDS = CONTINUE TO DUKE IT OUT TO LOCK IN ENTERPRISES
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Microsoft is at least as prevalent as Amazon in on-
boarding late movers to the cloud
And significant deployments have increased the
most in Azure over the last 12 months
Source: Morgan Stanley "2018 Outlook: Riding the Software Wave," January 2018.
CLOUD NATIVE
MICROSOFT = BIGGEST LEAP IN POSITION THIS YEAR
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CLOUD NATIVE
WITH MAJOR EXPANSION PLANS TO BOOT
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CLOUD NATIVE
GOOGLE = SIGNIFICANTLY BEHIND
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"In 2018, the level of anxiety for decision makers at big organizations around
vendor lock-in will continue to rise - replacing security as the #1 cloud concern."
- Spencer Kimball, CEO of Cockroach Labs
"Over 80% of enterprises show moderate to high levels of concerns
about public cloud lock-in."
- Stratascale Hybrid Cloud Survey
CLOUD NATIVE
REGARDLESS OF YOUR CHOICE OF POISON, VENDOR LOCK-IN IS REAL
*Cockroach Labs is a Work-Bench portfolio company.
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CLOUD NATIVE
MULTI-CLOUD = EVERYONE REPORTS HAVING A STRATEGY
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Majority of enterprises are still struggling with containerizing applications, yet they have big lofty strategies that largely fall
in 3 buckets:
Choose a single cloud provider to go all in
with
Amazon wins over the majority of this
segment, with Microsoft catching up
Run workloads wherever they will be
cheapest to deploy
Kubernetes is a key tool enabling this
segment to deploy workloads in the most
capitally efficient manner
Enterprise Example
Enterprise Example
Pick and choose clouds for different
capabilities (i.e. AWS for cost, GCP for AI and
data pipelines, Azure for legacy app migration)
Individual teams must manage their own
instances and budget, with IT implementing
light-weight governance
*Source: Work-Bench estimates based on interviews with F1000 IT executives.
#3: THE FUNCTION BROKER MODEL
#2: THE PRICE BROKER MODEL
#1: THE MONOCLOUD MODEL
CLOUD NATIVE
THAT FEW HAVE EXECUTED ON
Enterprise Example
Still in Flight
65
The cloud native migration story start with
customers and their harsh demands
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Customer expectations = rising. Enterprise
need to build software faster
Only way to move fast enough is to split
applications into easier to manage microservices
Microservices are best housed in containers
Containers are best governed by orchestrators
Orchestrating containers at scale requires a
service mesh network
Result: move faster than competitors, save
cost, and combat cloud vendor lock-in
CLOUD NATIVE
GOING CLOUD NATIVE IS THE ONLY RESOLUTION
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Best bet for
greenfield apps
Most mature solution
for scale out apps
la carte option for running
micro services on existing
infrastructure
2017
CLOUD NATIVE
ORCHESTRATION = KEY ENABLER OF MULTI-CLOUD; KUBERNETES = KING
2018
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CLOUD NATIVE
THE DATA SPEAKS FOR ITSELF
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Source: https://resources.coreos.com/analyst-reports/hybrid-cloud-drives-growing-container-production-use-and-disruption-451-research-report
CLOUD NATIVE
HALF OF ENTERPRISES NOW USE CONTAINERS IN SOME CAPACITY
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2X
CLOUD NATIVE
KUBERNETES ADOPTION = DOUBLED OVER LAST 12 MONTHS
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CLOUD NATIVE
SOON IF YOU USE DOCKER, YOU WILL DE FACTO ALSO USE KUBERNETES
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Services expertise
Distributed systems expertise
Joined the mix bringing direct
expertise as the original creators of
Kubernetes at Google
CLOUD NATIVE
RED HAT/COREOS + HEPTIO = RACING TO BRING F1000 TO KUBERNETES
Bringing
Kubernetes
to the Enterprise
*CoreOS was a Work-Bench portfolio company, acquired by Red Hat.
1
2
+
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LinkerD
Bouyant
Istio
CLOUD NATIVE
SERVICE MESH = REMINISCENT OF DOCKER HYPE
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Layer 7 SDN-like overlay network infrastructure for controlling
service to service traffic in microservices applications
What is Service Mesh?
Knowing where your app is
CLOUD NATIVE
SERVICE MESH = IMPORTANT MICROSERVICES GLUE
Benefits
Developer independence
App-aware load balancing
Microsegmentation
Identity assignment
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Service mesh adoption still early in 2018. Service meshes are like broccoli you know you need them but only adopt when
you feel the pain of not having them. With enterprise container and microservices deployments in infancy, we may be waiting
some time for service mesh adoption to kick into gear.
Continued 'school clique' dynamics within the community. While most in the real world doesn't care, small highly opinionated
camps are forming between Istio and Linkerd. Building an enterprise product in a competitive market governed by fickle open-
source "communities" will be a challenge commercial vendors in this space will grapple with.
Security will be the killer use case for service meshes. Scenario: 'digital transformation' AppDev teams at enterprises writing
microservices on AWS/Kubernetes that need to communicate with on-premise services in a secure manner. Services meshes are
the perfect answer.
CLOUD NATIVE
SERVICE MESH PREDICTIONS
76
How will enterprises deal with all this?
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In other words, SREs are IT ops pros in a software-defined infrastructure world
Source: https://landing.google.com/sre/interview/ben-treynor.html
CLOUD NATIVE
ENTER THE SITE-RELIABILITY ENGINEER (SRE)
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Monitoring
CLOUD NATIVE
THE SRE TOOLS LANDSCAPE SPANS 5 MAJOR CATEGORIES
Alerting
Ticketing
APM
Logging
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In today's "bigger is better" environment, vendors will jockey into
leadership position to own the mind and wallet share of SREs by
unifying tools & workflows:
Monitoring/Tracing
Alerting
Ticketing
Logging
Future
New startups will have to bring a fresh angle to
the table to compete with the growth players
and their comprehensive "suite" approaches.
Some will enter the market by positioning
themselves as built for "scale" or fill a niche with a
creative approach like crowdsourcing post-
mortem analysis insights to help build the SRE
playbooks of tomorrow.
CLOUD NATIVE
TO KEEP UP WITH THE INCREASING PACE OF OPS, TOOLS MUST INTEGRATE
of SRE
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Chaos engineering is the discipline of stress testing a distributed system to build confidence in its ability to withstand turbulent
production conditions (like the traffic load of an eCommerce website on Black Friday).
New tools are emerging to facilitate the process:
This stuff is real. Top enterprises (like below) are hiring
chaos engineers:
What is it:
CLOUD NATIVE
CHAOS ENG. EMERGING TO STRESS TEST INFRASTRUCTURE HOLISTICALLY
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Bouyant
*
Holistic "observability" across infra, traces, logs and all forms of environments (containers/serverless) is all the rage because
today's complex/heterogenous environments are spewing out rich data that cannot be viewed in isolation.
Legacy vendors and their point solutions won't last in this next evolution of monitoring as IT will refuse to manage haphazard
integration projects.
CLOUD NATIVE
FULL STACK MONITORING (I.E. OBSERVABILITY) IS ALL THE RAGE
*Backtrace is a Work-Bench portfolio company.
82
Sailing into the serverless era
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App components
Resource utilization"Pay-as-you-go": theoretical maximum
utilization of infrastructure
Spend per server: "pay-as-you-go" vs. serverless
"Serverless": actual instance run rate
idle time
run time
Microservices = more shallow
utilization across a wider footprint =
uneconomical with server-based
units of measurement in the "pay-as-
you-go" business model
Serverless lowers operating costs for software vendors.
CLOUD NATIVE
SERVERLESS COMPUTING MAKES FINANCIAL SENSE OF MICROSERVICES
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With serverless, it's more economical for an early-stage company to deliver complex middleware functionality
via elastically scalable compute and memory
Without serverless, these companies wouldn't be as economically viable
Automating ML model deployment
'Headless' CMS
Clicks-no-code SaaS integrations
CLOUD NATIVE
SERVERLESS = ENABLING A NEW GENERATION OF STARTUPS
*Algorithmia is a Work-Bench portfolio company.
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"Today, developers have begun to publish and share individual functions. Imagine pushing a button on Github
to instantiate a function or an application. Elegant on-ramp for a developer to become a paying Azure
customer." - Tomasz Tunguz, Redpoint
Source: http://tomtunguz.com/microsoft-github/
+
= lock-in?
Serverless is the ultimate vendor lock-in as the vendor manages the full
stack from server to the runtime layer
CLOUD NATIVE
SERVERLESS = WEAPON FOR MEGACLOUD LOCK-IN
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Google Spanner
Transactional integrity
Availability
Scalability
Microsoft Cosmos
CockroachDB
Consistent performance remains a hurdle for serverless
Without a distributed database you have a distributed monolith
CLOUD NATIVE
DISTRIBUTED DATABASES = KEY INFRASTRUCTURE FOR SERVERLESS
87
Vertical Theme #3
CYBERSECURITY
With contributions by:
Kelley Mak
Principal at Work-Bench &
Security Lead
@kelleymak
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Security engineering teams will lead shift from bolt-on to built-in security deployed as code
GTM motions for enterprise security startups will take a cue from the open source world
1
2
3
CISO = no longer the only security buyer; VP Infrastructure/Security Engineer is the new buyer/
champion combo to master
TL;DR
SECURITY PREDICTIONS FOR 2018
89
Year in Review
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CYBERSECURITY
SECURITY FUNDINGS = RECORD HIGH; NEWBIES ENTERING THE MIX
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Cyclical trend as fewer enterprises
refreshed their firewall last year? Or
more secular shift as security moves to
the cloud where it is embedded in the
infrastructure?
Source: Morgan Stanley "2018 Outlook: Riding the Software Wave," January 2018.
CYBERSECURITY
YET REVENUE GROWTH = SLOWING DOWN FOR PUBLIC SECURITY CO'S
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You know a tech category is inching past early adoption when
There's a Gartner MQ and a
Forrester Wave
$250M+ acquisitions by the
incumbents
+ startups raising $100M+
growth rounds to go big
Prediction: Netskope goes public or gets acquired for 8-figures by 2020
CYBERSECURITY
CASB = WELL WITHIN HYPER-GROWTH PHASE
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Sizable M&A for SOAR (Security Operations, Analytics, and Reporting) vendors focused on automation.
While automation is hot, there is still a gap in triage analytics and response workflow tools
$350M
$100M
CYBERSECURITY
SOAR = CONTINUES TO BE DARLING ACQUISITION TARGET FOR LEGACY CO'S
*Uplevel Security is a Work-Bench portfolio company.
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Data privacy tools
Helpful tools for IT to comply with GDPR
regulation in accordance with a legal team's
mandate
Software 'legalese' vendors
Closed loop solutions that replace the legal
decision making regarding GDPR with software
Market shakeout: Data discovery capabilities will trump legal expertise
>
CYBERSECURITY
GDPR = TWO NEW CAMPS OF VENDORS TO HELP
95
Ok, now on to the exciting stuff ahead
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Source: https://www.forrester.com/report/CISO+Career+Paths+Plot+Your+Course+For+Advancement/-/E-RES141371
Who they were
Leader of the Infosec team/tech/process
Hands on owner of the firewall,
endpoint protection, and security
operations
Who they're becoming
Manager ensuring IT stays in compliance
C-level and board liaison communicating the cyber risk
profile of the firm
More likely a suit than a geek. 45% have MBAs; 18% have
CS degrees.*
What it means: Traditional 'infosec' becomes back office as CISOs increase reliance on outsoucers/MSSPs to
clear bandwidth for executive relations.
CYBERSECURITY
CISOS EVOLVING TO LOOK MORE LIKE CIOS
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Source: https://www.aporeto.com/blogs/hybrid-and-multi-cloud-security/enterprises-becoming-cloud-native-journey-thoughts-kubecon/
Example:
Goal: fix this paradigm
CYBERSECURITY
SECURITY ENGINEERING ORG = EMERGING AS CHAMPION FOR NEW TECH
Shared service group of engineers that sit
between infra and appdev organizations.
Knowledgeable about both appdev and
security.
Mandate to embed security into appdev
process rather than bolt it on as a last step
before prod.
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22% of enterprises use microservices in some capacity today
Cloud-native security trends favor security to be built-in as opposed to bolted onto infrastructure
Security engineers have the hybrid DevSecOps skills to lead the implementation of cloud-native security
Source: https://www.forrester.com/report/Microservices+And+External+APIs+Underpin+Digital+Business/
CYBERSECURITY
SECURITY ENGINEERING EMERGING BECAUSE MICROSERVICES HEATING UP
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Whereas CISO and Infra teams think bolt-on, the security engineering orgs think built-in
CYBERSECURITY
SECURITY ENGINEERING ORG = NEW CHAMPION FOR BUILT-IN SECURITY TECH
Built-in
Bolt-on
*vArmour & Scytale are Work-Bench portfolio companies.
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TL;DR: Identity is an essential first building block of microservices security, yet it has played second
fiddle to network monitoring
CYBERSECURITY
MICROSERVICES SECURITY MATURITY FRAMEWORK
STEP #3: LOG ANALYTICS
STEP #2: NETWORK/RUNTIME
MONITORING/HARDENING
STEP #1: IDENTITY
*Scytale is a Work-Bench portfolio company.
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CYBERSECURITY
PROPRIETARY CORES ARE A THING OF THE PAST, NEW CO'S BUILT ON OSS
*Scytale is a Work-Bench portfolio company.
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The depth of new built-in security products are beyond the scope of CISO org. Target security engineers
as champions and infrastructure leaders as buyers.
Open source is the new marketing tactic for enterprise security. Build a community of web-scale security
engineers and the F1000s will follow.

InfoSec startups: target the progressive newly minted CISOs looking to implement the basics.
CYBERSECURITY
KEY TAKEAWAYS FOR EARLY-STAGE SECURITY STARTUPS
103
Vertical Theme #4
The de(Centralization) of SaaS
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Open source business software will experience a rebirth
Business software goes VPC
There will be a day when developers choose their company's business software
1
2
3
4
Ebbs and flows: SaaS ate infrastructure. In due time, infrastructure will eat SaaS
TL;DR
SAAS PREDICTIONS FOR 2018
105
Megaclouds aren't the only bullies
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SaaS-holes are the new status quo
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SaaS vendors are becoming mighty and taking advantage of it using aggressive tactics to expand dollar share within
existing accounts, often by shoving excessive features and extensive contract terms down customers' throats. Customers have
no choice but to succumb to these closed-ecosystem tactics.
107
Rulers of the front office
Kings of the back office
SAAS
SAAS MONARCHIES RULE BUSINESS SOFTWARE
108
SaaS-hole death grip?
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SaaS providers manage your business logic
and the latest copy of your data
Meaning they can up-charge you
30% or more every year
Up-charging:
You can either spend millions with the
vendor's preferred service provider
Or spend 12-months recruiting a team
with the right skills to manage customization
Complexifying their technology to foist services contracts:
SAAS
SAAS-HOLE DEATH GRIP TACTICS
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Short/medium term: macros favor large SaaS vendors
SaaS ate infrastructure: SaaS integrates all layers of the IT stack, allowing vendors to extract more rents than
with on-premise software.
Suites > best of breed: It's the Costco mindset: Enterprises choose the platform with the most features to
prepare for tomorrow, even if they don't need it today.
Systems of intelligence: Value increases with scale: more customers = more partners = more functionality =
more data = improved algorithms = better functionality and user experience = hard to migrate.
SAAS
SAAS-HOLES = ONLY GETTING RUDER
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Salesforce commands greater
share than Siebel ever did
SAAS
SALESFORCE = VERTICAL DISRUPTOR
1
In part because SaaS eats
infrastructure
2
Forcing analysts to constantly
adjust their market forecasts
3
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Cloud
SaaS
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Running your own software is cheap: SaaS provided favorable economics in
a time when running your infrastructure was expensive. Cloud compute is now
cheap and readily available.
Installing software is easy: SaaS made installing software easy at a time when
configuration was difficult. Docker/Kubernetes changes all this: software can be
installed anywhere and live in minutes.
Data security is imperative: SaaS was never viewed as more secure than on-
premise software but the cost savings and flexibility outweighed the risks.
CASBs emerged to provide a salve for the CISO but with data breaches at an all
time high and GDPR mandates looming, we'll see the SaaS/on-premise
equation re-examined.
"It's time to reconsider the SaaS model in a modern
context, integrating developments of the last nearly
two decades so that enterprise software can reach its
full potential."
Grant Miller, CEO of Replicated
Source: https://techcrunch.com/2018/06/17/after-twenty-years-of-salesforce-what-marc-benioff-got-right-and-wrong-about-the-cloud/
SAAS
IF SAAS ATE INFRA, CLOUD WILL EVENTUALLY EAT SAAS
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Abstraction is shifting further from infrastructure to the app layer to speed application development and customization, diluting
the need for 'SaaS as an app platform'
Server, storage, network
VM
OS
Database
Middleware
App
SAAS
RAPID DEVELOPMENT IN THE CLOUD EATS AT SAAS MOAT
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SAAS
ISN'T IT IRONIC, DON'T YOU THINK?
Oracle/Siebel
Pricing
Multi-year contracts
Sales
Aggressive enterprise sales
focused on cross-sell and
account penetration
Marketing
Annual conference with
disgruntled customers
Ecosystem
Proprietary
Technology Oracle hardware
Salesforce 1999
Pricing
Monthly subscription
Sales
Free trials, friendly telesales
Marketing
Infamous "no software"
drum beat
Ecosystem
Open
Technology Modern architecture
Salesforce 2018
Pricing
Multi-year contracts
Sales
Aggressive enterprise sales
focused on cross-sell and
account penetration
Marketing
Annual conference with
disgruntled customers
Ecosystem
Proprietary
Technology Oracle hardware
Note: we're only picking on Salesforce because it is the most successful SaaS vendor.
The Pendulum Has Swung Back
116
Is it possible to break the great
SaaS moat?
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SaaS customization = $$$
Containers and microservices = freedom
Containers and microservices are at odds with SaaS customization
In a world where services written in different
languages can easily communicate, proprietary
languages and processes that require hiring
"experts" will be obsolete.
= ?
Prediction: In the future, customization will be simple
and led by internal developers
SAAS
SAAS DISRUPTION: BREAK THE CUSTOMIZATION MOAT?
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The rise of AWS and quick-spinning database services means software no longer must be beholden to the underlying
infrastructure. Enterprises already prefer VPC to SaaS for infrastructure tools, and we expect business software to follow suit.
Example: Mattermost offers an enterprise-grade VPC alternative to Slack
Easy, one-click to deploy Mattermost to
any cloud platform:
Source: mattermost.com
SAAS
SAAS DISRUPTION: BREAK THE EASE OF DEPLOYMENT MOAT?
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#1. Salesforce is the new IBM. Many major enterprises just migrated to Salesforce, and no one will fire you for buying Saleforce.
#2. It's easier to budget for SaaS than headcount to self-manage software. Most enterprises are strapped for developers; they need them for
customer-facing software first and foremost.
#3. SaaS out-of-the-box functionality is hard to match. Major SaaS vendors integrate with everything. Disruptors face an uphill battle figuring out
what "good enough" would mean.
#4. If it ain't broke don't fix it mentality is still a thing. Organizations with a major reliance on SaaS platforms are stuck in the same sunk cost fallacy
as with the on-premise software of yesteryear.
TL;DR: The disruption of SaaS will take many years to play out. Either the numerous pain points will become acute enough to
take action or we'll have to wait for today's CIOs to overturn and for a new generation of buyers to come in.
SAAS
BUT DON'T GET TOO EXCITED KIDS
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After all, they feel the biggest SaaS tax dealing with the cleanup when integration and customization work hits the fan
Contentful breaks down a CMS into a barebones set of
individual microservices developers love to use.
Happy developers = happy customers.
Source: contentful.com
= decoupled and developer friendly CMS
Example:
SAAS
PERHAPS DEVELOPERS WILL CATALYZE THE SHIFT?
121
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