2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 2017 ENTERPRISE ALMANAC USHERING IN A NEW ERA OF ENTERPRISE TECH By Michael Yamnitsky #2017Almanac
1. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 2017
ENTERPRISE ALMANAC USHERING IN A NEW ERA OF ENTERPRISE TECH By
Michael Yamnitsky #2017Almanac
2. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 2 For
the past four years at Work-Bench, weve been investing in a total
reimagining of the enterprise technology stack. Were in the midst
of a once in a decade tectonic shift of infrastructure that powers
the Fortune 1000 and is unlike anything weve 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 - so were
launching the Enterprise Almanac to share our thinking on the
trends reshaping enterprise technology. 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. Its 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 [email protected] or on Twitter at @ItsYamnitsky.
PREAMBLE INTRODUCING THE WORK-BENCH ENTERPRISE ALMANAC MICHAEL
YAMNITSKY Venture Partner, Work-Bench
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PREAMBLE ABOUT WORK-BENCH About Us Work-Bench is an enterprise
technology focused venture fund. 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 ows 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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PREAMBLE TABLE OF CONTENTS I. 2017 Macro Perspective The Next
Generational Shift In Enterprise Technology Has Arrived II. Four
Vertical Themes 1. Machine Intelligence 2. Cloud Native 3.
Cybersecurity 4. Internet of Things (IoT) III. Tips for
Entrepreneurs
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PREAMBLE SPECIAL THANKS Special thanks to Team Work-Bench Jonathan
Lehr, Jessica Lin, Vipin Chamakkala, Kelley Mak, Mickey Graham, and
Dash Adam who added signicant contributions and healthy debate for
the content of this presentation. Friends in the Enterprise Tech
ecosystem Scott Coleman (Ignition Partners), Lenny Pruss (Amplify
Partners), Lonne Jaffe (Insight Venture Partners), Frank Gillett
(Forrester Research), Aaref Hilaly (Sequoia), Bradford Cross
(Merlon Intelligence), Matt Turck (Firstmark Capital), Drew Conway
(Alluvium), Diego Oppenheimer (Algorithmia), Greg Smithies
(Versive), Tim Eades & Keith Stewart (vArmour), Divya
Venkatachari (Cisco), Ed Anuff (Apigee/Google), and Winter Mead
(Sapphire Ventures) for contributing their thoughts. Work-Bench
Founders and CEOs For keeping me honest and never failing to
surprise us with with where technology can take us on this pale
blue dot.
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PREAMBLE SMALL DISCLAIMER Our views are shaped by anecdotal
evidence based on our interactions with entrepreneurs, corporate
customers, and big tech leaders. Take that for what its worth. Weve
done our best to separate fact from opinion by highlighting
opinionated perspectives in blue. Youll notice many qualitative
details, but a dearth of data in this report. The trends we discuss
are indeed early they cant be rigorously quantied in customer
surveys ran by Forrester and Gartner, nor can they be segmented out
of spending gures by IDC. CBInsights and Pitchbook provide valuable
fundraising data, but since history has shown theres a
disequilibrium between market potential and fundraising in early
market crests, weve decided to keep funding gures to a minimum. Our
intent was to avoid cherry picking funding data to serve our
purpose and make unfair claims of causality. We have disclosed our
investments where appropriate.
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2017 Macro Perspective The Next Generational Shift In Enterprise
Technology Has Arrived
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MACRO PERSPECTIVE SPEED, SCALE, CX DEFINES VALUE IN TODAYS
POST-INTERNET ECONOMY The top 5 most valuable US public companies
in 2017 Market leadership requires a combination of customer
experience, scale, speed, standards, and insight
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MACRO PERSPECTIVE STARK EVIDENCE OF THIS AS 3 OF THEM TRANSFORM
ENTERPRISE TECH The core tenants of these powerful companies
(speed, scale, standards) led them to expose their internal
capabilities to global companies around the world and evolve into
megaclouds dominating growth in the enterprise IT market
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MACRO PERSPECTIVE MEGACLOUDS ARE FIGHTING TO BE #1 PLUMBING FOR
DIGITAL BUSINESS $17.1 Billion (2017 Revenue Est.) 40% YoY Growth
$6.1 Billion (2017 Revenue Est.) 81% YoY Growth $950 Million (2017
Revenue Est.) 75% YoY Growth PRODUCT STRATEGY The monocloud thats
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
cant allocate 30 top PhDs to solve a single problem. Who will hit
Amazon in the achilles heel? 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 Amazons scale and scope?
PRODUCT STRATEGY Google shines strength in machine learning,
developer tools, and container orchestration (Kubernetes). GTM
STRATEGY Historically Google hasnt catered to the enterprise with
sales & support. Theyre apparently trying to change this
though. BIG EXISTENTIAL QUESTION Can Diane Green, Sam Ramji, and
the rst-class GTM team from Apigee bring Google from enterprise 0
to hero? PLAYER #1 (CATEGORY LEADER): MOMENTUM AND BRAND NAME
PLAYER #2 (FOR NOW): ENTERPRISE HERITAGE PLAYER #3 (KILLER
PRODUCTS): BUT WHERES 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 Source: Estimates from Bank of America Merrill
Lynchs Server & Enterprise Software: Cloud Wars 9: AI : From
faster to smarter powered by ABC. May 8, 2017. Revenue includes
PaaS & IaaS.
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The opportunity is massive, so megaclouds have gotten a little bit
territorial Darth vader death grip?
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MACRO PERSPECTIVE TECHNOLOGY GIANTS ARE CRUMBLING AT THE HEELS OF
MEGACLOUDS Software division spin out in 2016 20 straight quarters
of YoY revenue decline
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MACRO PERSPECTIVE ENTREPRENEURS IN SILICON VALLEY ARENT IMMUNE
EITHER Amazons ReInvent is a startup bloodbath Amazon Lightsail =
DigitalOcean killer? AWS X-Ray = testing and debugging startup
killer? Amazon Pinpoint = mobile analytics startup killer? The list
goes on Megaclouds put tremendous pressure on startups within the
cloud ecosystem the Amazon clan will either build it on their own
or make it difcult for startups to scale Amazon mines the startup
ecosystem and replicates their most popular software tools And
makes it difcult to create a tech company around deep IP by hosting
the latest open source software at a fraction of anyone elses
cost
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But the rules are about to change again with the resurgence of
Articial Intelligence
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MACRO PERSPECTIVE AI STARTUP FUNDRAISING AT RECORD HIGHS Source:
CBInsights The 2016 AI Recap: Startups See Record High In Deals And
Funding
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MACRO PERSPECTIVE TECH POWERHOUSES ARE PLAYING DEFENSE WITH
ACQUI-HIRES Source: CBInsights The Race For AI: Google, Twitter,
Intel, Apple In A Rush To Grab Articial Intelligence Startups Race
for AI: top acquirers of AI startups
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MACRO PERSPECTIVE STRATEGY IS TO KEEP AI ON A LEASH BY
DEMOCRATIZING IT One of the most exciting things we all can do is
demystify machine learning and AI. Its important for this to be
accessible by all people Sundar Pichai, CEO of Google Democratizing
AI retains its disruptive power, allowing megaclouds to maintain
their power grip through sheer scale of delivering Source:
https://www.wired.com/2017/05/sundar-pichai-sees-googles-future-smartest-cloud/
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MACRO PERSPECTIVE JEFF BEZOS ADMITS AI VALUE IS IN REAL WORLD
APPLICATION IN OPERATIONS Source:
http://www.businessinsider.com/jeff-bezos-shareholder-letter-on-ai-and-machine-learning-2017-4
But much of what we do with machine learning happens beneath the
surface. Machine learning drives our algorithms for demand
forecasting, product search ranking, product and deals
recommendations, merchandising placements, fraud detection,
translations, and much more. Though less visible, much of the
impact of machine learning will be of this type quietly but
meaningfully improving core operations.
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MACRO PERSPECTIVE WE BELIEVE A MINI AI CRASH IS IMMINENT Large tech
companies are ending their talent shopping spree, leaving many AI
startups with inated valuations and no real business in the dust.
Salesforce is already showing signs its getting over acqui-hires as
it pivots to internally developing Einstein after gobbling up
startups in 2015 and 2016. Too many competitive Series A & B
deals in AI that have come across our desks at Work- Bench over the
past year have had valuations in the high double digits, with many
even $100M+. This cant last long.
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MACRO PERSPECTIVE LIKE THE INTERNET ECONOMY, AI VALUE WILL BE
CREATED AFTER THE CRASH After the crash The successful companies
will focus on using AI to enable business process
transformation
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MACRO PERSPECTIVE AI COMPANIES WILL CREATE VALUE THROUGH SYSTEMS OF
INTELLIGENCE 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 workow as
seamless as possible. Original Framework Source: Jerry Chens The
New Moats - Why Systems of Intelligence are the Next Defensible
BusinessModel
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MACRO PERSPECTIVE EXAMPLES OF SYSTEMS OF INTELLIGENCE
Manufacturing: predictive maintenance on factory equipment in a
manufacturing facility Insurance: Automation of consumer car
insurance ling claims Pharmaceuticals: Optimization of R&D
resource allocation for a portfolio of drug candidates in clinical
trial Financial services: Automated customer interactions with
retail banking chat bots in natural language
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Systems of Intelligence are like Ford Assembly Lines and Toyota
Production Systems powerful weapons for competitive process
advantage.
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MACRO PERSPECTIVE SYSTEMS OF INTELLIGENCE BARRIER TO ENTRY LIES IN
TIGHT INTEGRATION Domain expertise AI Data Data-driven product
design Cloud moat = unbundling capabilities into individually
deployed microservices for scale advantage Domain expertise baked
into product / operational lifecycle Turning the intelligence into
action anchors product design Processes to maintain and enhance
data Systems of intelligence moat = bundling capabilities into
processes advantage
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MACRO PERSPECTIVE BUILDING A SYSTEM OF INTELLIGENCE REQUIRES
GETTING INTO THE DETAILS Deep domain expertise Rich, highly
relevant data sets
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Achilles heel of megaclouds = lack of focus on the details of
real-world applications
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Next-generation of successful entrepreneurs will build systems of
intelligence
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Still scared of this guy?
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TL;DR MACHINE INTELLIGENCE PREDICTIONS 1 2 3 Vertical AI continues
to be a disruptive force in the enterprise, with niche markets
presenting massive opportunities. There wont be a Twilio, but there
will be a Github of AI. Technology advances enable AI applications
to expand beyond the limitations of large, well-labeled data sets.
One caveat: complex vertical AI operating models = protracted path
to product/market t. Invisible apps will ride consumerization wave
faster than Slack. 4 5
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MACHINE INTELLIGENCE BETWEEN TALENT AND OPPORTUNITY Most AI talent
works here to optimize the output of Meanwhile the world changing
opportunities are out here
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MACHINE INTELLIGENCE HYPE AND POTENTIAL Potential: 30 years ago
Michael Porter predicted an IT automated value chain were yet to
fully achieve Hype: Chatbots rose and fell in the course of 9
months no one wanted to talk to a real human, why would they want
to talk to a bot? Source: Michael Porter, How Information Gives You
Competitive Advantage HBR
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MACHINE INTELLIGENCE EXPECTATIONS AND REALITY What the press thinks
of AI entrepreneurs How entrepreneurs really feel right now
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Bridging the gap
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MACHINE INTELLIGENCE THE OPPORTUNITY IS GETTING MORE OBVIOUS
Fortune 1000 CEOs AI Entrepreneurs
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MACHINE INTELLIGENCE REFLECTING ON LESSONS LEARNED FROM THE RECENT
PAST AI Masquerade Ball Is it Ava? Or Jake from State Farm? Your
smartest people + our smartest people in a room? 14 months, 150
consultant ERP projects Action Jacks black box? Something in
between? ?
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MACHINE INTELLIGENCE AND DIGGING INTO THE ARCHIVES TO SEE WHAT
WORKED IN THE PAST 1960s 1970-1980s An algorithmic dream AI winter
Late 2000s Internet Big Data AI Masquerade Ball14 months, 150
consultant ERP projects 1990-2000s Mid 2010s 2016 Is it Ava? Or
Jake from State Farm? 1985 2017+ Sybase PowerBuilder AI-powered
business process revolution Articial intelligence meets business
process re-engineering
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MACHINE INTELLIGENCE TO WRITE THE FUTURE PLAYBOOK FOR BUSINESS
PROCESS AUTOMATION 2 winning personas for AI-powered enterprise
software Invisible apps Vertical AI Value prop Buyer persona Human
Strategy Data source AI HumanAI Biz app APIs (i.e. Gmail,
Salesforce) ID the killer app, ride on top of established data set,
create a data label moat to protect against new entrants Packaged
software to automate common business processes Hybrid app/services
to automate company-specic processes. Value prop Buyer persona
Strategy Data source Proprietary IT systems Implement with pilot
customer, facilitate niche search and user exploration in app to
train the AI, ID MVP that can scale with respect to customer
implementation and sell that before expanding scope Automate a
business task Eliminate headcount, make those remaining more
efcient BU leader/CxOEmployee Operating model Operating model
invisible apps move faster; vertical AI is more complex to
implement but stickier
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MACHINE INTELLIGENCE INVISIBLE APPS HAVE A MORE OBVIOUS TRAJECTORY
Prediction: By 2018 at least one breakthrough invisible app will
grow faster than the early days of Slack or Salesforce Invisible
apps Impact: Dominant force disrupting the workforce over the next
ve years because of deadly combination of task automation + wide
reach, ease of deployment of consumerized SaaS Key distinction:
end-to-end automation of a business task so the value proposition
is cost reduction. Otherwise merits of AI = more efcient UX and its
just a productivity play like any other SaaS app. GTM
differentiation: Shorter AI training periods leveraging structure
and rich semantics of biz app data. Busy execs, consultants and
sales peoplewill purchase and expense access to invisible apps in
true consumerized fashion. Examples of invisible apps: Buyer
persona Employee * Work-Bench portfolio company *
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MACHINE INTELLIGENCE VERTICAL AI IS TRICKIER GIVEN THE MORE COMPLEX
OPERATING MODEL Algorithm Annotation Action Feedbackloop Invisible
apps Algorithm Vendor Annotation Action Vertical AI Customer
Annotation Feedbackloop Defensibility driven by the data moat Deals
with sensitive information and drives functionality changes
requiring company-specic process expertise. Note: not all vertical
AI have customer annotation. Deals with routine discrepancies
Lock-in dynamic with integration services and customer side
annotation Note: there is some human-in-the-loop in that the users
interactivity with the software drives model renement, but the onus
is not on the customer to explicitly train the AI like in many
cases of vertical AI
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MACHINE INTELLIGENCE AND HARDER TO GET TO DATA SETS Invisible apps
are intelligence systems riding on top of SoRs Vertical AI require
customization to get to legacy systems and unstructured data
Structured data Unstructured data
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MACHINE INTELLIGENCE SO THEYRE SUBJECT TO TOUGHER REQTS FOR
PRODUCT/MARKET FIT Does it automate a task end-to-end with a high
degree of accuracy? Invisible apps Vertical AI Is it easy for
employees to use? Does it signicantly augment humans? Is the UX
intuitive and enjoyable to use? Does the AI require the customers
intervention? Who? Data scientists? Business analysts? This is
arguably the trickiest part to building vertical AI and thus where
startups should differentiate What level of abstraction from the
guts of the system will the UX need?
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MACHINE INTELLIGENCE VERTICAL OPPS ARE MASSIVE: EX. FINANCIAL
SERVICES Theres a multi-billion dollar differential in investment
bank cost structure, and compliance is dominating expenditure post
2008 nancial regulation. Regulation is such a powerful force on
Wall Street that compliance ofcers seem to be running the business
and driving divisional efciency initiatives. Outcome: increase
speed, reduce human overhead Vertical AI can help rms reduce
compliance headcount by automating the mind numbingly repetitive
tasks within compliance: BU leader/CxO Business process: Compliance
workows (i.e. KYC, AML) Industry: Financial ServicesBuyer persona *
Work-Bench portfolio company *
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MACHINE INTELLIGENCE VERTICAL OPPS ARE MASSIVE: EX. PHARMA Business
process: Pre-clinical informatics research (i.e. drug predictions)
Industry: Pharmaceuticals Massive pre-clinical research cost
($1B+), vast timeline (3-4 years), high failure rates (99.5%).
Opportunity to simulate pre-clinical processes by identifying
molecule combinations most likely to succeed in clinical trial.
Outcome: increase speed, decrease R&D expenditure Vertical AI
can help rms speed the development pipeline and focus clinical
trial efforts on drugs with higher likelihood of success by
automating informatics research. Service-provider business model
Proprietary drug pipeline business model Source: Joseph A. DiMasi,
Henry G. Grabowski, Ronald W. Hansen Innovation In The
Pharmaceutical Industry: New Estimates of R&D Costs; Harvard
Business School The Medicines Case BU leader/CxO Buyer persona +
some startups innovating here
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MACHINE INTELLIGENCE VERTICAL OPPS ARE MASSIVE FOR A REASON Higher
margins = larger margin differential across rms = wider gap for AI
to be used as a competitive advantage Source: Factset
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New forces extend the possibilities in enterprise AI
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MACHINE INTELLIGENCE UNSTRUCTURED DATA PREP = MORE USE CASES,
FASTER TO MARKET Automated data prep has historically only worked
for the 10% of structured data Now automated data prep possible for
the 90% of unstructured data in large enterprises Result: expansion
of possible uses cases within enterprises as data prep is an
initial step in any AI process 70% of time in AI development spent
on data prep * Work-Bench portfolio company *
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MACHINE INTELLIGENCE INDUSTRIALIZED DATA ANNOTATION = MORE ACCURATE
AI Deep learning common among AI elite; special sauce turning to
data annotation in ebb/ow pattern between data and algos From a
single highly tuned data annotation console to many highly-tuned
data annotation consoles Modular enough to be outsourced 2014 2015
2016 2017 Machine learning Data annotation Industrialization of
data annotation Deep learning Levelof Yellow brick road towards
autonomous AI Industrialization of data annotation
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MACHINE INTELLIGENCE DEEP LEARNING SUBSTITUTES = MORE INFERENCE,
LESS DATA + EASIER TO USE Learning from fewer examples Transfer
knowledge between tasks Deep Learning and more exotic forms of AI
are great in theory, but difcult to implement in practice due to
the intensive parameter tuning and amount of data required to train
an algorithm New trend: make AI methods that require less data more
accessible by adding representation schemes from traditional ML.
Example: Hierarchical temporal memory Combining exotic bayesian
networks with common decision tree structure of neural nets to
bring deep-learning like algos to natural language understanding.
More elastic anomaly detection Put language into context Example:
Deep Forest Ensemble method in the woodworks that limits the number
of hyper-parameters relative to deep learning and adds common
(albeit vast, pun intended) decision tree structure. Ease the
training curve Lower the level of expertise required Expand the
possibilities:
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MACHINE INTELLIGENCE BAYESIAN LEARNING = PARTICULARLY PROMISING AS
COMPLEXITY RISES As the industry continues to explore more complex
machine learning challenges The need for easier to use substitutes
for deep learning like bayesian learning will rise
Experthumaneffort Problem difculty Deep learning Bayesian
learning
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MACHINE INTELLIGENCE COMPETITIVE FORCES IN AI-POWERED SOFTWARE ARE
CHANGING Data moat? Algorithmic differentiation? Data product
differentiation? Weaker: still a signicant barrier, but its 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, proling, debugging, and testing processes. Competitive
forces will be in ux 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 signicant 12-24 months from now Locus of focus
shifting from the quantity you own to the process you use to
sustain these assets* Example: Merlon intelligence designs its
automated compliance workow software to BOTH shorten insights to
action and gather feedback from users as new data that feeds into
the models.*For more on this topic, see Matt Turcks The Power of
Data Network Effects
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MACHINE INTELLIGENCE FORGET AI ELITISM, MAINSTREAM NOW BETTER
EQUIPPED/EAGER TO BUILD AI Suits and Hoodies at Goldman Sachs
Ambitious attitudes: AI is a competitive differentiator. We want to
own the model, we dont want Palantir to own it. Smart recruiting
tactics: Avoiding talent wars with the web- scales by sourcing data
scientists in India rather than US, and Masters-levels rather than
PhDs. But some skepticism, particularly around deep learning: We
have lots of existing regression models that are nely tuned. Deep
learning is just going to be incremental and more expensive right?
After missing out on the internet and struggling with mobile,
Corporate America wants in early on AI Source: Quotes from
interviews with machine learning executives at top-tier Wall Street
banks
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MACHINE INTELLIGENCE WITH A PLETHORA OPEN SOURCE IT WILL BE EASY,
RIGHT? Out-of-the-box deep learning with differentiated AI training
Widely available backend computing libraries Intuitive interfaces
Good for recursive neural nets Good for convolutional neural nets,
speedy/exible but no support for Keras Higher-level APIs
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MACHINE INTELLIGENCE WELL THERES A STEEP LEARNING CURVE Source: S.
Zayd Enam, Stanford AI Lab Root-cause analysis in AI is vastly more
complex than regular software Algorithmdesign Implementation Two
dimensions of investigation: Algorithm design Implementation Four
dimensions of investigation: Algorithm design Implementation Choice
of model Data It takes more experience to debug AI efciently than
to debug traditional software + longer time cycle testing the x
Software development = hours Machine learning = days Why?
Re-training algorithm on dataset is time consuming, pushing a code
change to production is not.
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MACHINE INTELLIGENCE AND BRICK WALLS SILOING INTERNAL EFFORTS =
LACK OF LEVERAGE Business Unit #1 Enterprise data science functions
are decentralizing to get more funding/buy-in from across the
enterprise. Most organizations lack culture of collaborative data
exchange, and data governance teams slow projects down. Data Algos
Common organizational processes mean algos can be used across
business units to solve different use cases Data governance
overlords Business Unit #2 Data Algos Business Unit #3 Data Algos +
Siloed data science initiatives = Enterprise are not getting
leverage with their data science efforts
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MACHINE INTELLIGENCE DISPARATE DATA SCIENCE OPERATIONS = NEED FOR
SEAMLESS PIPELINE Central intelligence Small group of high paid
technical experts responsible for initial model development Product
owners Backend AI trainers Less sophisticated/cheaper/ often
internationally based data science support function responsible for
preparing data and hyper-parameter tuning models developed by
central intelligence Domain experts responsible for application of
models in BUsDomain expertise Training data Production-ready models
IT ops This role is the newest addition to the enterprise data
science function highly underserved from a SW perspective
Infrastructure owners responsible for deploying models in a cost-
efcient manner How do you manage resources and costs running
complicated deep learning models? How do you keep everyone up to
speed on latest model commits and updates? App Dev APIs
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MACHINE INTELLIGENCE ML PIPELINES EVOLVING INTO A PLATFORM TO BUILD
& DEPLOY ML Historical lineage of ML models ready to be
leveraged across the org No-ops deployment Modeling tools and
platforms Git push like model employment with smart orchestration
and serverless computing Data Data lake Data store, version
control, parallel processing Algos Tools to build, optimize,
language- convert, containerize, and data connect ML models. Level
of abstraction varies depending on the role targeted (i.e. less
sophisticated data scientists vs. hardcore deep learning engineers)
Collaboration = area for differentiation for vendors ML platforms
help enterprises centralize, reuse, and deploy their models at
scale. Value will be in tight integration of ML workows spanning
the entire pipeline. * Work-Bench portfolio company * * *
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MACHINE INTELLIGENCE WHAT IT MEANS: FUTURE = GITHUB + HEROKU FOR
AI, FUTURE TWILIO FOR AI No-ops deployment Modeling tools and
platforms Data Data lake Algos Collaboration = area for
differentiation With deationary pressure from open source, we
expect MLaaS or Twilio for AI vendors with differentiated IP and
talented teams will try to pivot towards Github for AI, but will
most likely get acqui-hired or resort to selling their data sets to
sustain their business. YES NO acquired by Cisco * Work-Bench
portfolio company * * *
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TL;DR CLOUD NATIVE PREDICTIONS 1 2 2017 is shaping up to be a
pivotal year for Fortune 1000 deployments of cloud native
infrastructure. Container orchestration is the VMware anchoring the
cloud native ecosystem. Exactly who will play this critical role
will become clearer this year. 3 Cloud native is reshaping
databases, middleware, big data, developer tools, and business
models.
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CLOUD NATIVE APPLICATION INFRASTRUCTURE TRANSFORMATION = WELL UNDER
WAY Yesterday Apps on VMs Containers on VMs Containers orchestrated
on bare metal The container disruption = slowly shifting enterprise
infrastructure away from virtual machines (VMs) Today Tomorrow
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CLOUD NATIVE WHY CONTAINERS OVER VMS? Containers are lighter
10s-100s of MBs vs. multiple GBs, just the right size for component
based microservices Containers are faster they can be spun up and
down in seconds vs. minutes to realize the true agility,
resilience, and portability of cloud computing Containers are more
efcient you can t 4-8 times as many app components (or
microservices) on a bare metal container server than you can on a
VM because of the way containers share OS resources to free up
space Container are simply a better unit of deployment for the
cloud than VMs
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CLOUD NATIVE A NEW CLOUD NATIVE STACK IS BEING DEVELOPED AROUND
CONTAINERS Developer crave for speed and simplicity combined with
4-8X potential server efciency gains across $726B in global IT
infrastructure spend more than justies the new economy of
container-centric IT infrastructure dubbed cloud native Sources:
Forrester Research, Cloud Native Computing Foundation/Redpoint
Ventures
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CLOUD NATIVE WHY THIS MATTERS FOR THE FORTUNE 1000 Worlds largest
custodian of assets to be largest non web- scale to go cloud native
Use case: mission critical internal workloads and partner facing
developer platform called NEXEN Business case: transactional
velocity, cost cutting Tool & vendors: Apache Mesos,
Kubernetes, OpenStack, Docker Major media company goes no-ops with
self-service cloud native PaaS Use case: rapid-application
development platform to meet demands of its deadline-driven
business Business case: developer productivity Tool & vendors:
Kubernetes, Docker Major education company goes cloud native to
efciently scale its growing customer base Use case: core digital
learning platform Business case: rapid scalability Tool &
vendors: Kubernetes, Docker
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CLOUD NATIVE FORTUNE 1000S WANT DEVELOPMENT AGILITY LIKE THE
WEB-SCALES Microservices fullls on the promises of service-oriented
architecture by decoupling apps into single-purpose services that
communicate with other microservices via APIs or messages Sources:
PWC Agile coding in enterprise IT: Code small and local Speedier
app delivery Rapid cycle times and easier updates Higher
code-to-server density ratio
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CLOUD NATIVE AND TO SCALE AS EFFICIENTLY AS THE WEB-SCALES TOO
Average server utilization by type of environment 10-15% 50-70%
Cloud-native Virtualized Sources: Gartner, Codeship Non-virtualized
5-10% ? Hallmark vendor Infrastructure type Example users
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CLOUD NATIVE DESPITE ORGANIZATIONAL HURDLES ENTERPRISES NEED TO
OVERCOME Why the f&ck dont we use Kubernetes? Tech leadership
to ops Ops Were going to be placing you on a special projects team
to migrate all of our workloads over to Kubernetes After that, were
going to have to let you go. Devs The cloud native organizational
disconnect = ops getting the short end of the stick
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CLOUD NATIVE 2017 IS SHAPING UP TO BE A PIVOTAL YEAR FOR CLOUD
NATIVE ADOPTION Container adoption is crossing the chasm: 11% of
global developers reported using Docker containers for deployments
in late 2016. Megaclouds are increasing their integration and
support for container orchestration: Amazon natively integrates
with Mesos, Microsoft Azure container services supports Kubernetes,
Docker Swarm, and Mesos, Google naturally integrates with
Kubernetes, and even Oracle now supports Kubernetes! Developers are
embracing new programming models like functional pipelines (i.e.
serverless) and the agent model to ease their migration to
microservices. Sources: Forrester Research, Google Trends
Serverless on Google Trends
70. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 70 I
want in, where do I invest?
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CLOUD NATIVE ORCHESTRATION = CENTRAL NERVOUS SYSTEM OF CLOUD NATIVE
= $$$ Container orchestration tools are the data center operating
systems of the future. They automate container deployments by
spinning up and managing deployment of containers in production
applications to fully realize the agility, resiliency, and
portability benets of containers. Major OSS container orchestrators
Best bet for greeneld apps Largest open source initiative by Google
Fully featured orchestrator for enterprise apps Several commercial
vendors in ecosystem as with Hadoop Major Partners: Google,
Rackspace, RedHat, Intel, CoreOS, Oracle Most mature solution for
scale out apps More mature project than Kubernetes and Nomad
Integrates well with existing Hadoop stack Not so self-service:
bring your own service discovery, highly skilled operators, and
maintenance staff Major Partners: Microsoft, HP la carte option for
running micro services on existing infrastructure Individual open
source projects for service scheduling, discovery, and secrets
management that together are competitive to Kubernetes Existing
companies with legacy inertia use Nomad for service discover and
secrets management Managed by Hashicorp
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CLOUD NATIVE CONTAINER ORCHESTRATION VENDORS ARE THE NEXT BIG IT
VENDORS Commercial software vendors bring container orchestration
to life LEGACY FUTURETRANSITION VIRTUALIZED SERVERS EXISTING PAAS
PLATFORMS ADDING CONTAINER SUPPORT NEW PLATFORMS BUILT GROUND UP
FOR CONTAINERS * Work-Bench portfolio company *
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Kubernetes is a disruptive pirate ship built by Google, with sails
set straight for Amazon
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CLOUD NATIVE A FEW REASONS WHY KUBERNETES WILL WIN THE RACE FOR
DATA CENTER OS #1. Maximizes ease of use as the industrys most
fully-featured orchestrator #2. Vibrant open source community #3.
Like with Hadoop, lack of vendor dominance encourages the community
to freely innovate on OSS foundation #4. Technically sophisticated
stack built from the ground up with the right level of abstractions
for users to build and deploy applications using containers
Kubernetes-rst OSS projects emerging Istio currently only supports
the Kubernetes platform, although we plan support for additional
platforms such as Cloud Foundry, and Mesos in the near future.
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CLOUD NATIVE DATABASES FINALLY CATCHING UP WITH DEMANDS OF CLOUD
NATIVE Google Spanner Cloud native needs databases that can keep
up. Problem = databases are sluggish beasts that never quite
benetted from the pace of innovation the rest of the industry
enjoyed. Former Google VP of Infrastructure Eric Brewer summarized
the engineering challenges of developing database infrastructure
with the CAP Theorem: you can only achieve two of the following
guarantees for your database: 1) transactional integrity, 2)
availability, 3) and scalability. Until now. New solutions emerging
that dispel CAP Theorem: Transactional integrity Availability
Scalability Microsoft Cosmos CockroachDB * Work-Bench portfolio
company *
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CLOUD NATIVE APPLICATION MONITORING CATCHING UP AS WELL Buoyant New
problems + new enablers Network-riddled app logs Latency in event
time stamping Distributed tracing techniques and community support
Machine learning Powerful stream processing Interbred app-service
dependencies * Work-Bench portfolio company *
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CLOUD NATIVE POINT TO ANOTHER ROUND OF APM CONSOLIDATION? Will new
monitoring entrants evolve standalone or will APM leaders
AppDynamics/New Relic lead the charge? On-premise: fragmented APM
Cloud: consolidated APM Cloud Native: early monitoring
fragmentation Buoyant Cloud Native: consolidated APM? ? Either way,
both categories expand with shift to cloud native
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CLOUD NATIVE WITH CLOUD NATIVE, SOFTWARE EATS MIDDLEWARE Middleware
Then: App servers, complex event processing servers, and ESBs were
complex new technologies in the rst generation of cloud requiring
dedicated servers to manage them. Now: many of these functions are
now distributed directly across the application code, thinning the
traditional middleware layer in the stack.
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CLOUD NATIVE NEW PROGRAMMING MODELS ABSTRACT MIDDLEWARE FUNCTIONS
INTO CODE Pivotal Cloud Foundry Spring Cloud Services Microsoft
Azure Functions Microsoft Azure Service Fabric IBM Bluemix
OpenWhisk Amazon AWS Lambda Google Cloud Functions Functional
pipelines (aka serverless) = no more complex event processor
Ephemeral snippets of code govern app data trafc, rendering the
need for dedicated services for business rules obsolete. Actor
model = no more bloated app servers Software actors implement
concurrency via asynchronous messages and spread proxies across
physical servers to manage consistency. App servers are thus
relieved of resiliency duties. Middleware, once a core layer of the
IT stack, is shedding signicant weight as middleware functions now
reside in distributed code. Iron.io Serverless
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CLOUD NATIVE SERVICE MESHES ABSTRACT NETWORK FUNCTIONS VIA
LIGHTWEIGHT PROXIES Service discovery is the new IP address and DNS
New system of dynamically routing services to manage latency in
large scale distributed systems gRPC and REST are the new TCP/IP
Service meshes use new protocols developed for communications at
the service level rather than underlying network Service meshes are
lightweight network proxies governing service-to-service
communications for tasks such as service discovery, load balancing,
and monitoring in highly complex distributed systems LinkerD
Buoyant Istio
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CLOUD NATIVE MIDDLEWARE MARKET ISNT DEAD, ITS JUST EVOLVING
Replacing middleware pipes is a new software-human middleware layer
tackling the more specialized functions of complex modern apps:
Infrastructure Systems of engagement Next-generation middleware ML
pipeline Helping data scientists add intelligence and automation to
software Ingesting and interpreting real-time information from
around the world Streaming platform
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CLOUD NATIVE STREAMING IS THE NEW COMPLEX EVENT PROCESSING SERVER
& ESB Streaming platform Data lake/distributed database Code
Container OS Container engine Container orchestration External
integrations Operations and container image management
Businesslogicas distributedcode Data stack Cloud Native stack ML
pipeline Stream processing Messaging system Code Next-gen complex
event processing server Next-gen ESB Lightweight network proxies
for load balance and service discovery
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CLOUD NATIVE THE RACE IS ON FOR STREAM PROCESSING Its a story of
multi-purpose convenience vs. purpose-built performance, with
support for cloud-native schedulers becoming a must Already
considered legacy in Silicon Valley with Spark demonstrating
considerably more more horsepower Doesnt work out of the box at
scale and frustrating to set up and manage Streaming native systems
Multi-purpose batch systems with streaming bolt-on New streaming
library on popular distributed log with mid-2016 release Unproven
scalability/ stability, support for cloud-native schedulers New
streaming library developed at Twitter with promise of better
scalability and manageability than Storm Architecture supports
cloud- native schedulers and Storm migration One stop shop for
batch, streaming, and ML that plays well with Hadoop Near real-time
streaming is good enough but not great with respect to scale,
throughput, and latency Streaming and batch in one system incurs
latency Limited production use cases and unclear development path
Tied closely with YARN architecture Latency issues as a
multi-purpose system
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Cloud native stack Big data stack
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CLOUD NATIVE STREAMING INTEGRATIONS = DATA AND APPS WILL LIVE IN
ONE STACK Streaming platform Data lake/distributed database Code
Container OS Container engine Container orchestration External
integrations Operations and container image management Data stack
Cloud Native stack ML pipeline Stream processing Messaging system
Code Cloud native schedulers take on packaging and deployment of
big data workloads Specialized processing libraries instead of
all-in-one clusters tied to a specic scheduler Data and app stacks
have been separate until now Container orchestrators like
Kubernetes and Mesos distribute data workloads better than Hadoops
Yarn. Spark, Kafka, Herron and other new school stream processing
engines all integrate directly with container orchestrators.
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CLOUD NATIVE MORE BROADLY, HADOOP IS LOSING ITS DOMINANCE Markets
demands moving to machine learning, where Hadoop has no shine Spark
> ML for Hadoop, and with cloud-based object store, you dont
need Hadoop for Spark. ML is best run on highly specialized chips
like Googles TPUs and NVIDIAs DGX-1 rather than the commodity
hardware Hadoop was developed for. Megaclouds ate Hadoops lunch
Megaclouds use the Hadoop distribution in their cloud services, but
by unbundling the underlying le system (HDFS) from the cluster
manager (YARN) and making these components inter- changible with
alternatives, Hadoop is losing its position as a central nervous
system for the data stack.
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CLOUD NATIVE SERVERLESS COMPUTING MAKES FINANCIAL SENSE OF
MICROSERVICES 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 sever-based units of
measurement in pay-as-you-go business model Serverless lowers
operating costs for software vendors. Still TBD whether vendors
decide to pass these savings down to customers.
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CLOUD NATIVE WILL IT FORTIFY MEGACLOUD LOCK-IN OR DISSOLVE IT?
Amazon sees Lambda as another form of lock-in. It wouldnt be
trivial for Amazon to change their posture because architecturally,
functions are tied to AWS public cloud and it would take extensive
work with partner VMware to extend functions into private cloud.
Google wants to make functions more extensible to promote
multi-clouds and combat Amazons lock-in grip. They hope to
commoditize AWS by lowering switching costs with serverless. Google
Functions
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CLOUD NATIVE ML WILL SOON PLAY AN INTEGRAL ROLE FOR INFRA OPS AND
APPDEV ML-powered cluster management Improving resource efciency by
adding a Netix-like recommendation system to allocation of app
components across resources Hyperpilot ML-powered static code
analysis Analyzing code commits to gure out how to better allocate
developer resources across the organization ML-powered IT ops Bots
for new employee on-boarding tasks Electric.ai IT is by nature a
data-driven organization, making it the perfect function to infuse
with the power of AI Examples in the market: * Work-Bench portfolio
company *
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TL;DR SECURITY PREDICTIONS FOR 2017 1 SecDevOps blurs the lines
between networking and application security as the race for
cloud-native security products intensies. 2 3 Beyond the 1%: SOIs
as consumable microservices will bring advanced security technology
to the 99% of companies who previously couldnt afford. The security
ecosystem is re-organizing itself into Systems of Intelligence
(SOI). Systems of record (SORs) must become SOIs or risk being
relegated to plumbing. In the sweeping wave of industry
consolidation, legacy security companies will buy up security
analytics and Security Operations, Analytics, and Reporting (SOAR)
companies in yet another bout to stay relevant. 4
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ENDPOINT CYBERSECURITY LAST 5 YEARS = EVOLUTION FROM SECURITY
PRODUCTS TO SOR PLATFORMS ANTIVIRUS SIEM FIREWALL DLP IAM MALWARE
It used to be about product oligopolies NETWORK // HOST APPLICATION
// CODE Now the center of attention is around a new breed of
monopolistic Systems of Record platforms assembling themselves
around layers of the IT stack ? * Work-Bench portfolio company
*
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CYBERSECURITY SECURITY ECOSYSTEM EVOLVING INTO A SYSTEM OF
INTELLIGENCE How do you get the most comprehensive observability of
IT systems in the most seamless fashion? **Note: each value driver
is sized based on its ability to create sustainable competitive
advantage. Original framework source: Jerry Chen (Greylock
Partners) The New Moats and have a little bit of this SORs are
getting here How do you make sense of and take action based on the
wealth of new information generated by modern security systems?
With new security tool overload and highly understaffed sec orgs,
how do you make workows more seamless? How do you foster
collaboration amongst security teams? Can you use automation? AI
Domain expertise Data-driven product design Data Primary value
driver** SORs have a natural rst mover advantage to put all the
pieces together * Work-Bench portfolio company *
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CYBERSECURITY SYSTEMS OF INTELLIGENCE PRINCIPLES BEHIND
BREAKTHROUGH SORS Ease of implementation and management Scalability
in complex IT environments Short time-to-value ENDPOINT NETWORK
APPLICATION How do you seamlessly instrument into IT systems? How
do you create a System of Record platform? How do you build great
data-driven products with attributes CISOs really care about?
Agents on web servers or application run-times In-line
forward/reverse proxy or agent Trafc access point off rewall or web
proxy App APIs Distributed sensors that act like agents but are
decoupled from underlying hardware Traditional software agents
Traditional software agents Get better data to build a better
product: First you land customers with a single product, the thin
edge of the wedge. Designing a product starts with what type of
data you can peek into: Data Data-driven product design
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CYBERSECURITY AS THE BASIS FOR BEATING LEGACY CO'S AT PRODUCT
OLIGOPOLY GAME You expand beyond the thin edge of the wedge by
leveraging data/instrumentation advantages to extend product scope
and displace product-centric companies in adjacent categories.
Instrumentation: lightweight endpoint agents collaborating in a
distributed system POLICY MGMT INCIDENCE RESPONSE CONFIGURATION
MGMT OS PATCH MANAGEMENT Land Expand Expand VULNERABILITY MGMT
POLICY MGMT DECEPTIONFIREWALLMICRO- SEGMENTATION Land Expand Expand
SECURITY ANALYTICS Instrumentation: distributed network sensor
system Distributed security architecture is a common thread here
because it brings outsized speed & scale to the process of
obtaining data to build a host of security products. * Work-Bench
portfolio company *
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CYBERSECURITY REASONS WHY A PLATFORM STRATEGY IS SO CRITICAL TO
SUCCESS Long term sustainable competitive advantage. With superior
performance characteristics and a growing treasure trove of data,
you can evolve with the rapidly changing industry pace, much like
the ancient force trumps the latest imperial weapon. Its difcult to
stay competitive with a single-product strategy because hackers
will eventually gure out workarounds, rendering your product
obsolete. FireEyes uncertain future is a case in point.
Cost-reduction value proposition. Platforms are stickier than
products. The comprehensiveness of your platform allow customers to
rip-and-replace their old tools in a cost reduction play.
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CYBERSECURITY SOR LANDSCAPE = MORE COMPLICATED AS NETWORK/APP LINES
BLUR NETWORK // HOST TL;DR: Network and app layer are looking to
achieve the same goal of bringing X-Ray vision of apps to security.
Culture + technology factor into this shift. In startup race,
network/host layer leaders have rst mover advantage over new
entrants to gain X-ray vision up the stack. APPLICATION //
CODE
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CYBERSECURITY DEVSECOPS: THE CULTURAL ASPECT OF NETWORK/APP BLUR
App Dev VP Infrastructure Owns the Systems of Record (including
legacy security tools) Assembling the Systems of Intelligence to
bridge the divide (creating a pane of glass for security) CISO
Making pane of glass more complicated by using new tools with rich
insight into app activity without VP Infrastructure buy-in App Dev
is bringing new infrastructure and tools to the table, so security
teams must keep up with the rich insights into applications these
tools generate
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CYBERSECURITY ABSTRACTED INFRA: THE TECHNICAL ASPECT OF NETWORK/APP
BLUR NETWORK ENDPOINT Old-world app X-ray vision = server = mostly
network data in rewall + some data from endpoint New-world app
X-ray vision = LAYER 7? OS HOST? RUNTIME? CONTAINER? Dispersed
across more abstracted infrastructure Hidden in data-rich DevOps
tools Container orchestration CI/CD toolchain X-ray vision
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CYBERSECURITY BLURRING NETWORK/APP LINES CREATE NEW BATTLE FOR
CLOUD NATIVE SEC NETWORKING OS HOST CONTAINER CODE IMAGE New app
dev rst entrants = thin edge of the wedge point products with hopes
to become new Systems of Record in cloud native security world.
Host layer Systems of Record = extending product capabilities to
ensure compatibility with containers and cloud native architecture
Thin edge = vulnerability managementThin edge = WAF bandaid
CONTAINER ORCHESTRATION With application logic distributed across
individual microservices callable via APIs on the network,
east-west trafc visibility via deep packet inspection is critical
Security tools must limit network activity between containers
running on distributed hosts and observe communication
interdependencies between containers on the same host OS *
Work-Bench portfolio company * * * * *
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CYBERSECURITY SOR PLATFORMS WILL HAVE TO EVOLVE INTO SOIS TO REMAIN
COMPETITIVE **SOAR: new term dubbed by Gartner for Security
Operations, Analysis and Reporting technologies that support workow
management. Note: these categories are not mutually exclusive in
that several Systems of Record vendors have Systems of Intelligence
capabilities and vice versa. Original framework source: Jerry Chen
(Greylock Partners) The New Moats Or these guys? Chicken and egg
problem: how do we partner to get the SOR data? Do we acquire one
of these guys? Security analytics SOAR** * Work-Bench portfolio
company * *
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CYBERSECURITY SOI ENTRANTS = SECURITY ANALYTICS, ON THE HUNT FOR
DATA Security analytics work across Systems of Record (SORs) to
make sense of all the data. With SORs developing security analytics
capabilities themselves, they must prove out the value of
generating insights across SORs if they are to endure as
independent vendors. Acquired by Oracle Acquired by HP Example:
Versive partners across the SORs to build a deep and wide data moat
in security analytics Deeply instrumented in the data center Best
visibility into mission critical workloads Flexibility to pull data
from end user devices selectively * Work-Bench portfolio company *
* *
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CYBERSECURITY SOE ENTRANTS = SOAR, SECURITY WORKFLOW EXPERTS
Security Operations, Analytics, and Reporting (SOAR) tools
automatically run playbooks for common security workows, freeing up
limited analyst bandwidth to handle the more niche cases. Its still
to be decided whether they meaningfully penetrate the enterprise
market directly or power the next generation of managed security
service providers as CISOs increasingly outsource analyst work.
Example: Demisto is developing a workow tool that integrates
existing security tools Sources: Demisto
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So how will this all play out?
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CYBERSECURITY THE SORS MUST EMBRACE CREATIVE DISRUPTION AND SHIFT
INTO SOI Step 2: de-couple analytics and policy control
capabilities into a marketplace of callable security function
modules that leverage a broad swathe of SOR data. Step 1:
commoditize SORs into backend data feeds Step 3: develop an
application platform for SOEs to build on top of the SOIs. Splunks
Splunkbase is an early illustration of this concept. SOR landscape
getting complicated and competitive. New SOIs are coming in. SORs
must move up the stack and embrace new operating models that
commoditize their very crown jewels.
109. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC $0
$20,000 $40,000 $60,000 $80,000 $100,000 $120,000 $140,000 0 50000
100000 150000 200000 250000 300000 350000 400000 Averageordersize
Customer count CHKP FEYE IMPV PANW SYMCFTNT PFPT Blue Coat MIME
CYBR 109 CYBERSECURITY SOI WILL EXPAND MARKET OF LOW COST SECURITY
SERVICES But what if we could deliver SOI functions as callable
microservices on serverless backends to lower the cost of
delivering security services? Cutting edge security technology has
historically been prohibitively expensive More Expensive / Narrow
Customer Set Less Expensive / Broader Customer Set Source: Company
data, Goldman Sachs Global Investment Research
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CYBERSECURITY WHAT WILL HAPPEN TO LEGACY SECURITY COMPANIES? Legacy
security companies need to shift from peripheral to disruptive
acquisitions. Ex. CASB acquisitions from 2016 do not place
incumbents directly into the heart of the cloud, but buying a
System of Record platform startup will. Most likely outcome this
year and and next is legacy security companies buy Security
Operations, Analytics, and Reporting (SOAR) startups to put
themselves closer, but not fully embedded in the cloud IT stack.
Paradox of legacy companies and PE rms buying and integrating
security products is that it only brightens the spotlight on
independent SOAR and security analytics vendors who differentiate
by casting a wider net than any multi-product suite.
Democratization of machine learning may swing the pendulum in favor
of SIEM vendors who can build an intelligence moat around their
legacy SORs. Security incumbents have been busy buying
cybersecurity startups is M&A really a silver bullet?
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Vertical Theme #4 Internet of Things (IoT)
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TL;DR INDUSTRIAL IOT PREDICTIONS 1 Distributed analytics will be
critical for remote/low-bandwidth industrial IoT operations. 2 3
IoT is potent for competitive advantage amongst industrials like
gunpowder was for kingdoms of the 1200s. Industrial IoT = earlier
than most of us think because distributed infrastructure remains in
its infancy. Security for IoT will spawn directly from distributed
analytics architectures.4 The next frontier is systems management
software bridging disparate IoT software systems.5
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So, have you ever IOTd before?
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INTERNET OF THINGS WELL, EVERYONE TALKS ABOUT IT Predictive
maintenance of equipment can save massive amount of time and cost
63% reduction in maintenance time on site $340K-$1.7M loss per day
due to shutdown $11M loss per day of unplanned downtime Oil &
gas renery Natural gas drillerBuilding security systems
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INTERNET OF THINGS NOBODY REALLY KNOWS HOW TO DO IT IoT product
platforms have been in market for years with an established
approach the industry just assumed Industrial IoT would work
similarly. Closed system software stack with broad protocol support
and prepackaged apps for asset management, alert management,
product relationship management, and workow management
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Ample in home environment Low-bandwidth in remote environment
Homogenous time-series data streams from a couple sensors
Heterogeneous data from the drones, drills, and operational
databases Business rules, ML-based anomoly detection Complex,
event-driven analytics INTERNET OF THINGS PRODUCT VS. INDUSTRIAL
IOT The connected washing machine The autonomous drone in a
sensor-laden oil eld Connectivity Data Analytics Product Industrial
IoT
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INTERNET OF THINGS EVERYONE THINKS EVERYONE ELSE IS DOING IT John
Deere has mastered connected farming operations Sure dude What does
the connected cow have to say?
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INTERNET OF THINGS SO EVERYONE CLAIMS THEY ARE DOING IT Forrester:
25% of enterprise IT claims to be using IoT software platforms, 33%
of global enterprise developers reported building IoT applications
in 2017. BCG/IDC: Insights from clients points to 250B in annual
spend by 2020. Sources: Forrester Research, Winning in IoT: Its All
About the Business Processes by BCG
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Putting hype aside IoT = new era of industrial competitive
advantage
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MACRO PERSPECTIVE GE IS ALREADY LEADING PACK OF INDUSTRIALS
BECOMING TECH COMPANIES In 2012, GE and followers set out to create
software systems to improve internal operational efciency
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INTERNET OF THINGS THEY SOON REALIZED WHAT THEY WERE BUILDING WAS
UNIQUE Megaclouds will scale your application up, down, and
side-ways but have no idea how to bring software over here
Industrials are building IoT platforms highly specialized PaaS with
modules for industrial processes such as asset productivity,
operations scheduling, maintenance, and product delivery for their
clients
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INTERNET OF THINGS INDUSTRIAL CHARTER = DEVELOP SYSTEMS OF
INTELLIGENCE Domain expertise Data AI Data-driven product design
Industrials have the opportunity to evolve their software platforms
into powerfully defensible systems of intelligence Industrial
systems of intelligence ?
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INTERNET OF THINGS MORE IMMEDIATELY, RACE IS ON FOR SOFTWARE
PLATFORM DOMINANCE The Palantir of Industrial IoT Services company
helping industrials like John Deere develop their own systems of
intelligence for IoT Developing portfolio of IoT software
Differentiating with distributed analytics capabilities and
blockchain for P2P transactions. Bought Tririga for facilities
management applications Parlaying Azure portfolio towards IoT
Microsoft combined an IoT device management platform with its
robust portfolio of streaming analytics, and easy-to-use machine
learning services to develop IoT software for predictive
maintenance and remote monitoring Recently announced edge analytics
for distributing analytics processing across devices, gateways, and
the cloud Strongest in distributed computing with Greengrass and
Lambda Greengrass adds a smart app server to IoT gateways to enable
distributed computing Amazons Lambda functions govern business
logic and manage device state across distributed systems Analytics
chops and industrial customer base Streaming analytics capabilities
via SAP HANA Developing modules for predictive maintenance and
asset management It has one of the strongest industrial customer
bases in the tech sphere with its ERP heritage, but with a very
different type of buyer it is not clear this will give them an
advantage in IoT Strong player for remote, low-bandwidth scenarios
relying on cellular connections With Jasper Technologies
acquisition, strongest network of telcos to better manage cellular
data fees in remote locations Acquisition of ParStream is a
catalyst for Cisco to develop edge analytics capabilities needed
for remote area IoT Most mature industrial IoT software platform
Built on Cloud Foundry with easy migration between on-premise and
cloud Developing analytics capabilities with acquisitions of
wise.io for machine learning and Bit Stew for data ingestion and
transfer Planning to better automate services with Servicemax
acquisition Strong network of SIs and partners Needs to developed
more pre-packaged software modules
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INTERNET OF THINGS SKILL SETS ARE GOING TO COME TOGETHER Can these
guys learn to run technology businesses? i.e. developer evangelism,
partnerships and integrations Can these guys learn industrial
processes? Seems doubtful So were starting to see healthy signs of
cooperation GE for application PaaS Microsoft for IaaS
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Endpoints as massive P2P data centersData centers as central
control Edge servers as bridges Mini data centers in cell towers
IoT gateways INTERNET OF THINGS TIDES ARE TURNING AWAY FROM CLOUD
AND BACK TO THE EDGE Compute resources will slowly shift from the
cloud to devices
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INTERNET OF THINGS CONSUMER TECH TRENDS + NEW EDGE PROCESSES =
READY FOR EDGE ML New NVIDIA Jetson TK1 is built for computer
vision, ML, NLP on a variety of small devices Nest proved out local
ML data processing as did Google Now +
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INTERNET OF THINGS GATEWAYS ARE LOCAL FUELING STATIONS BRIDGING
DEVICES AND CLOUD Bridges gaps in: Networking throughput that
render real-time data processing too slow for the cloud Compute for
local data preprocessing that may be too resource intensive for
endpoints Intermediary data store for efcient, spoke-hub
distribution of sensor data 1010101010101010101010101010 1010101010
1010101010101010101010101010 1010101010
1010101010101010101010101010 1010101010
1010101010101010101010101010 1010101010 Gateway 50B devices in 2020
Source: Cisco IoT Report Spewing data streams >> Centralized
cloud
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INTERNET OF THINGS GATEWAYS ARE BEING INFUSED WITH SOFTWARE TO
ENABLE EDGE COMPUTING Ciscos Parstream allows for efcient,
spoke-hub distribution of sensor data at IoT gateways Amazons
Greengrass sends functions with complex event processing rules for
data ltration and synchronization of digital shadows for managing
asset state across low network environments Vapor.io retrots cell
towers with mini data centers for local data preprocessing that may
be too resource intensive for endpoints and too time sensitive or
prohibitively expensive to send to the cloud
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Analytics at the edge to make instantaneous decisions. Speed is
mission critical in the case of brake failure detection on a
speeding train, where symptoms show up in data just minutes before
a disaster. Large wireless data fees to send to cloud. Worth the
cost? Only if useful for historical analysis. Utilize gateways when
you can to save on device battery power drain. Amazon AWS
Greengrass Putting it all together highly distributed IoT
operations Scenario: Brake failure preventative maintenance on
remote supply transport train
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Governing data ow will be a new analytics architecture Scenario:
Brake failure preventative maintenance on remote supply transport
train
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INTERNET OF THINGS ANALYTICAL TOOLS BUILT FOR THE INTERNET DONT
WORK FOR IOT Resource intensive Try putting heterogeneous
industrial data streams into traditional big data pipeline ETL
Learn Build Data lake Extract value High latency Loss of critical
real-time insights XXX
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INTERNET OF THINGS ANATOMY OF A DISTRIBUTED IOT ANALYTICS
ARCHITECTURE Industrial sensors Machine processes Distributed
instrumentation Data labelling Industrial scale ETL Stream
processing Messaging system Predictive maintenance Inventory mgmt
Asset productivity Gateway Cloud Machine learning Instant response
Additional context, data ltering Deep insights, model updates
Device Expert operator feedback Data historians System architecture
Software modules
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INTERNET OF THINGS CONTENDERS FOR DISTRIBUTED IOT ANALYTICS IN
THREE CAMPS IoT application PaaSPure play startupsMegaclouds
(recently acquired by Greenwave Systems) * Work-Bench portfolio
company *
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INTERNET OF THINGS SECURITY FLOWS FROM DISTRIBUTED ANALYTICS Armis
Distributed analytics architectures instrument deeply into
endpoints in the gateway, and thus will be the providing data to
security solutions focused on device anomaly detection and
distributed policy-based prevention. Traditional security vendors
talk a big game about IoT but they are going to struggle to get
into the industrial space because operators arent going to want to
instrument connected assets 10 ways like IT does in the data
center. Because of this dynamic, distributed analytics vendors have
an opportunity to become security vendors themselves. Outside of
endpoint and network security, the radio frequency spectrum is a
new topology that the most discerning government agencies and
nancial institutions will protect against external IoT intruders.
Example startups
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INTERNET OF THINGS NEXT UP: OPPORTUNITY TO BUILD SYSTEMS MGMT LAYER
OF INDUSTRIAL IOT Industrials connecting asset in their supply
chain must do the same for software shipped with these assets. Much
like with the rise of systems management software (Tivoli, BMC) in
the 90s to help IT more efciently manage and get value out of
disparate appliances in the data center, a management layer to
integrate disparate IoT software stacks will likely emerge.
Connected asset #1 Connected asset #2 Connected asset #3 Connected
asset #4 Connected asset #5 OEM/IoT platform vendor Management
layer
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INTERNET OF THINGS LASTLY, IOT SOFTWARE STARTUPS SHOULD AVOID
DEVELOPING HARDWARE TL;DR: IoT software startups should focus on
use cases in which the underlying physical assets are already
IoT-enabled. Vertical AI software is highly specialized, and
creating a full stack solution tuned to a particular use case often
means developing proprietary hardware to obtain data from older,
non-IoT enabled physical assets. Besides the operational challenge
for a startup to set up hardware manufacturing, many startups we
meet are incurring heavier losses than typical vertical SaaS
companies at the same stage because they absorb the hardware cost
and just sell the software. These startups intend to convince OEMs
to manufacture the devices on their behalf. We believe this wishful
thinking because OEMs will not be able to extract enough value from
hardware purpose-built to serve even the largest of vertical
application markets.
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Lastly, A Few Tips for Entrepreneurs Going Forward
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TL;DR TIPS FOR ENTREPRENEURS GOING FORWARD 1 2 Systems of
Intelligence are the long run combative play against the
megaclouds, but there are still ways to build value in cloud
ecosystem. Enterprises still want licenses. Thwart their demand by
undercutting SaaS pricing sooner rather than later. Systems of
Intelligence companies will need a thin-edge of the wedge market
entry strategy, for which there are several models emerging. 3
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Megaclouds will be busy duking it out this year
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TIPS FOR ENTREPRENEURS WHEN MEGACLOUDS HOST OSS PROJECTS, FOCUS ON
NICHE FEATURES Google releases Kubernetes to the OSS community
Amazon reacts by integrating with Mesos Offensive play to render
Amazon lock-in obsolete. Defensive play aligning with more mature
(although less progressive) alternative Neither of them are
focusing on enterprise features and support, leaving room for these
pure plays to shine: * Work-Bench portfolio company *
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TIPS FOR ENTREPRENEURS WHEN MEGACLOUDS GO COMMERCIAL SERVICE, PLAY
AGAINST LOCK-IN Google tries the Amazon lock-in approach thinking
it can lure new GCP customers with its innovative new database
product. Google releases Spanner as a managed service for GCP
Microsoft releases me-too competitor called Cosmos Private/virtual
private cloud Pure play Cockroach Labs is well positioned to power
Amazons combative strategy and address the massive landscape of
enterprise managed databases. When megaclouds go play the lock-in
game Go multi-cloud, multi-region as a differentiator * Work-Bench
portfolio company *
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TIPS FOR ENTREPRENEURS MEGACLOUD AGNOSTIC PLAY = PERTINENT WHEN
SENSITIVE DATA INVOLVED Google, Microsoft, and Amazon want to
enforce vendor lock-in by developing one-click ML deployment
services on their functional backends But enterprises need
exibility to move ML workloads to where the data is and not vice
versa as megaclouds hope. Why? Because machine learning and data
need to sit together, often on the same GPU server, and sensitive
customer records cant just instantaneously be moved to the cloud
for a data science project. Hence a big opportunity for pure play
ML platform startups: * Work-Bench portfolio company *
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TIPS FOR ENTREPRENEURS THIN-EDGE OF WEDGE STRATEGIES CRITICAL FOR
SYSTEMS OF INTELLIGENCE Systems of Intelligence have a chicken and
egg problem: Customers want proof the power of automation can help
their business and startups need the data to train the system so it
can actually deliver on that promise. The DVR player: Lightweight
version of the product that takes historical data from a customer
and delivers insights in retrospect. This approach provides the
necessary training data and proof points to convince the customer
to deploy the solution for real-time analysis. Vertical AI
masquerading as invisible software: Although example in the market
are less obvious today, some enterprise chatbot startups take this
approach where they sell automation bots bottoms up to employees
with the intention of using the data the bots integrate with to
gathering insights into how businesses operate. This can be used to
build a system of intelligence for optimizing business functions
and operations to be sold more formally to senior management as a
next evolution of the company. Single pane of glass: A prevalent
approach is to integrate disparate data and provide unied
visibility across databases. In this respect, the thin edge
strategy is data middleware, with applications that enable business
process transformation upsold on top of this core
functionality.
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Megaclouds arent the only bullies, dont forget the SaaS-holes
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TIPS FOR ENTREPRENEURS SAAS-HOLES ARE TO BLAME FOR LICENSES IN THE
CLOUD PHENOMENON SaaS vendors are making enterprises run back to
licenses. SaaS SOR 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 Enterprises are
push back by opting for licenses to run in their own virtual
private cloud which may ruin things for the rest of the industry
should the trend continue to persist. Why this matters: More
license revenue = less deferred revenue = higher capital
requirements
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TIPS FOR ENTREPRENEURS UNDERCUT SAAS PRICING TO AVOID LICENSES IN
THE CLOUD RUT Most enterprise infrastructure startups make the
mistake of not undercutting SaaS prices right away, and only do so
after customers start opting for licenses. 0% 20% 40% 60% 80% 100%
Year1 Year2 Year3 Year4 Year5 Year6 Year7 Year8 Year9 Year10 Year11
First-timecustomersbydeployment model(%) Subscription-based
License-based 0 50 100 150 200 250 300 350 400 450 Year1 Year2
Year3 Year4 Year5 Year6 Year7 Year8 Year9 Year10 Year11
3-yearTCObyyearofsalescontract($USD,000s) Annualsubscription
3-yearlicense This leads to licenses in the cloud. Long protracted
path to get out of licenses in the cloud rut, discounting SaaS
prices helps. Theoretical growth metrics for high growth enterprise
infrastructure startup
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