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2017 Enterprise Almanac

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The Next Generational Shift In Enterprise Infrastructure Has Arrived. If SlideShare is broken, please download report here: https://www.scribd.com/document/352452857/2017-Enterprise-Almanac

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2017 Enterprise Almanac

  1. 1. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 2017 ENTERPRISE ALMANAC USHERING IN A NEW ERA OF ENTERPRISE TECH By Michael Yamnitsky #2017Almanac
  2. 2. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 2 For the past four 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 - so we’re 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. 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. PREAMBLE INTRODUCING THE WORK-BENCH ENTERPRISE ALMANAC MICHAEL YAMNITSKY Venture Partner, Work-Bench
  3. 3. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 3 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 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.
  4. 4. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 4 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
  5. 5. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 5 PREAMBLE SPECIAL THANKS Special thanks to… Team Work-Bench Jonathan Lehr, Jessica Lin, Vipin Chamakkala, Kelley Mak, Mickey Graham, and Dash Adam who added significant 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.
  6. 6. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 6 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 it’s worth. We’ve done our best to separate fact from opinion by highlighting opinionated perspectives in blue. You’ll notice many qualitative details, but a dearth of data in this report. The trends we discuss are indeed early – they can’t be rigorously quantified in customer surveys ran by Forrester and Gartner, nor can they be segmented out of spending figures by IDC. CBInsights and Pitchbook provide valuable fundraising data, but since history has shown there’s a disequilibrium between market potential and fundraising in early market crests, we’ve decided to keep funding figures 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.
  7. 7. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 7 2017 Macro Perspective The Next Generational Shift In Enterprise Technology Has Arrived
  8. 8. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 8 MACRO PERSPECTIVE SPEED, SCALE, CX DEFINES VALUE IN TODAY’S 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
  9. 9. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 9 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
  10. 10. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 10 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 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. 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 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. They’re apparently trying to change this though. BIG EXISTENTIAL QUESTION Can Diane Green, Sam Ramji, and the first-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 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 Source: Estimates from Bank of America Merrill Lynch’s “Server & Enterprise Software: Cloud Wars 9: AI : From faster to smarter powered by ABC.” May 8, 2017. Revenue includes PaaS & IaaS.
  11. 11. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 11 … The opportunity is massive, so megaclouds have gotten a little bit territorial… Darth vader death grip?
  12. 12. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 12 MACRO PERSPECTIVE TECHNOLOGY GIANTS ARE CRUMBLING AT THE HEELS OF MEGACLOUDS Software division spin out in 2016 20 straight quarters of YoY revenue decline
  13. 13. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 13 MACRO PERSPECTIVE ENTREPRENEURS IN SILICON VALLEY AREN’T IMMUNE EITHER Amazon’s 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 difficult for startups to scale Amazon mines the startup ecosystem and replicates their most popular software tools… …And makes it difficult to create a tech company around deep IP by hosting the latest open source software at a fraction of anyone else’s cost
  14. 14. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 14 … But the rules are about to change again with the resurgence of Artificial Intelligence
  15. 15. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 15 MACRO PERSPECTIVE AI STARTUP FUNDRAISING AT RECORD HIGHS Source: CBInsights “The 2016 AI Recap: Startups See Record High In Deals And Funding”
  16. 16. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 16 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 Artificial Intelligence Startups” Race for AI: top acquirers of AI startups
  17. 17. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 17 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. It’s 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/
  18. 18. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 18 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.”
  19. 19. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 19 MACRO PERSPECTIVE WE BELIEVE A “MINI” AI CRASH IS IMMINENT Large tech companies are ending their talent shopping spree, leaving many AI startups with inflated valuations and no real business in the dust. Salesforce is already showing signs it’s 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 can’t last long.
  20. 20. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 20 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
  21. 21. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 21 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 workflow as seamless as possible. Original Framework Source: Jerry Chen’s “The New Moats - Why Systems of Intelligence are the Next Defensible Business Model”
  22. 22. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 22 MACRO PERSPECTIVE EXAMPLES OF SYSTEMS OF INTELLIGENCE • Manufacturing: predictive maintenance on factory equipment in a manufacturing facility • Insurance: Automation of consumer car insurance filing 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
  23. 23. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 23 Systems of Intelligence are like Ford Assembly Lines and Toyota Production Systems – powerful weapons for competitive process advantage.
  24. 24. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 24 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
  25. 25. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 25 MACRO PERSPECTIVE BUILDING A SYSTEM OF INTELLIGENCE REQUIRES GETTING INTO THE DETAILS Deep domain expertise Rich, highly relevant data sets
  26. 26. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 26 …Achilles heel of megaclouds = lack of focus on the details of real-world applications…
  27. 27. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 27 Next-generation of successful entrepreneurs will build systems of intelligence
  28. 28. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 28 Still scared of this guy?
  29. 29. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 29 Vertical Theme #1 Machine Intelligence
  30. 30. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 30 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 won’t 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 fit. Invisible apps will ride consumerization wave faster than Slack. 4 5
  31. 31. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 31 Cloud Native disruption can’t be stopped Misalignments
  32. 32. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 32 MACHINE INTELLIGENCE …BETWEEN TALENT AND OPPORTUNITY… Most AI talent works here… … to optimize the output of… Meanwhile the world changing opportunities are out here…
  33. 33. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 33 MACHINE INTELLIGENCE …HYPE AND POTENTIAL… Potential: 30 years ago Michael Porter predicted an IT automated value chain we’re 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
  34. 34. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 34 MACHINE INTELLIGENCE … EXPECTATIONS AND REALITY… What the press thinks of AI entrepreneurs… How entrepreneurs really feel right now…
  35. 35. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 35 Bridging the gap…
  36. 36. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 36 MACHINE INTELLIGENCE THE OPPORTUNITY IS GETTING MORE OBVIOUS… Fortune 1000 CEOs… AI Entrepreneurs…
  37. 37. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 37 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 Jack’s black box? Something in between? ?
  38. 38. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 38 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 Artificial intelligence… … meets business process re-engineering
  39. 39. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 39 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-specific 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 efficient BU leader/CxOEmployee Operating model Operating model invisible apps move faster; vertical AI is more complex to implement but stickier
  40. 40. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 40 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 five 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 efficient UX and it’s 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 people will purchase and expense access to invisible apps in true consumerized fashion. Examples of invisible apps: Buyer persona Employee * Work-Bench portfolio company *
  41. 41. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 41 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-specific 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 user’s interactivity with the software drives model refinement, but the onus is not on the customer to explicitly “train” the AI like in many cases of vertical AI
  42. 42. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 42 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
  43. 43. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 43 MACHINE INTELLIGENCE …SO THEY’RE SUBJECT TO TOUGHER REQT’S 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 significantly augment humans? Is the UX intuitive and enjoyable to use? Does the AI require the customer’s 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?
  44. 44. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 44 MACHINE INTELLIGENCE VERTICAL OPPS ARE MASSIVE: EX. FINANCIAL SERVICES There’s a multi-billion dollar differential in investment bank cost structure, and compliance is dominating expenditure post 2008 financial regulation. Regulation is such a powerful force on Wall Street that compliance officers seem to be running the business and driving divisional efficiency initiatives. Outcome: increase speed, reduce human overhead Vertical AI can help firms reduce compliance headcount by automating the mind numbingly repetitive tasks within compliance: BU leader/CxO Business process: Compliance workflows (i.e. KYC, AML) Industry: Financial ServicesBuyer persona * Work-Bench portfolio company *
  45. 45. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 45 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 firms 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
  46. 46. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 46 MACHINE INTELLIGENCE VERTICAL OPPS ARE MASSIVE FOR A REASON… Higher margins = larger margin differential across firms = wider gap for AI to be used as a competitive advantage Source: Factset
  47. 47. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 47 New forces extend the possibilities in enterprise AI
  48. 48. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 48 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 *
  49. 49. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 49 MACHINE INTELLIGENCE INDUSTRIALIZED DATA ANNOTATION = MORE ACCURATE AI Deep learning common among AI elite; special sauce turning to data annotation in ebb/flow 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
  50. 50. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 50 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 difficult 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:
  51. 51. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 51 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 difficulty Deep learning Bayesian learning
  52. 52. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 52 MACHINE INTELLIGENCE COMPETITIVE FORCES IN AI-POWERED SOFTWARE ARE CHANGING 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* Example: Merlon intelligence designs its automated compliance workflow 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 Turck’s “The Power of Data Network Effects”
  53. 53. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 53 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 don’t 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 finely 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
  54. 54. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 54 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/flexible but no support for Keras Higher-level APIs
  55. 55. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 55 MACHINE INTELLIGENCE WELL THERE’S 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 efficiently than to debug ‘traditional’ software + 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.
  56. 56. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 56 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
  57. 57. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 57 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- efficient 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
  58. 58. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 58 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 workflows spanning the entire pipeline. * Work-Bench portfolio company * * *
  59. 59. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 59 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 deflationary 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 * * *
  60. 60. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 60 Vertical Theme #2 Cloud Native
  61. 61. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 61 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.
  62. 62. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 62 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
  63. 63. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 63 CLOUD NATIVE WHY CONTAINERS OVER VMS? Containers are lighter — 10’s-100’s 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 efficient — you can fit 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
  64. 64. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 64 CLOUD NATIVE A NEW CLOUD NATIVE STACK IS BEING DEVELOPED AROUND CONTAINERS Developer crave for speed and simplicity combined with 4-8X potential server efficiency gains across $726B in global IT infrastructure spend more than justifies the new economy of container-centric IT infrastructure dubbed “cloud native” Sources: Forrester Research, Cloud Native Computing Foundation/Redpoint Ventures
  65. 65. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 65 CLOUD NATIVE WHY THIS MATTERS FOR THE FORTUNE 1000 World’s 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 efficiently scale its growing customer base •Use case: core digital learning platform •Business case: rapid scalability •Tool & vendors: Kubernetes, Docker
  66. 66. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 66 CLOUD NATIVE FORTUNE 1000S WANT DEVELOPMENT AGILITY LIKE THE WEB-SCALES… Microservices fulfills 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
  67. 67. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 67 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
  68. 68. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 68 CLOUD NATIVE DESPITE ORGANIZATIONAL HURDLES ENTERPRISES NEED TO OVERCOME… “Why the f&ck don’t we use Kubernetes?” Tech leadership to ops Ops “…” “We’re going to be placing you on a special projects team to migrate all of our workloads over to Kubernetes… After that, we’re going to have to let you go.” Devs The cloud native organizational disconnect = ops getting the short end of the stick
  69. 69. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 69 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. 70. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 70 “I want in, where do I invest?”
  71. 71. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 71 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 benefits of containers. Major OSS container orchestrators Best bet for greenfield 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 À 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
  72. 72. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 72 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 *
  73. 73. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 73 Kubernetes is a disruptive pirate ship — built by Google, with sails set straight for Amazon
  74. 74. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 74 CLOUD NATIVE A FEW REASONS WHY KUBERNETES WILL WIN THE RACE FOR DATA CENTER OS #1. Maximizes ease of use as the industry’s 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-first 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.”
  75. 75. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 75 Cloud Native disruption can’t be stopped
  76. 76. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 76 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 benefitted 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 *
  77. 77. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 77 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 *
  78. 78. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 78 CLOUD NATIVE CLOUD NATIVE MONITORING VIA MULTI-VENDOR TOOL CHAIN… + + + + Common cloud native monitoring configurations…
  79. 79. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 79 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
  80. 80. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 80 CLOUD NATIVE WITH CLOUD NATIVE, SOFTWARE EATS MIDDLEWARE Middleware • Then: App servers, complex event processing servers, and ESBs were complex new technologies in the first 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.
  81. 81. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 81 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 traffic, 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 significant weight as middleware functions now reside in distributed code. Iron.io Serverless
  82. 82. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 82 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
  83. 83. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 83 CLOUD NATIVE MIDDLEWARE MARKET ISN’T DEAD, IT’S 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
  84. 84. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 84 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
  85. 85. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 85 CLOUD NATIVE THE RACE IS ON FOR STREAM PROCESSING It’s 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 • Doesn’t 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
  86. 86. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 86 Cloud native stack Big data stack
  87. 87. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 87 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 specific scheduler Data and app stacks have been separate until now… Container orchestrators like Kubernetes and Mesos distribute data workloads better than Hadoop’s Yarn. Spark, Kafka, Herron and other new school stream processing engines all integrate directly with container orchestrators.
  88. 88. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 88 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 don’t need Hadoop for Spark. •ML is best run on highly specialized chips like Google’s TPUs and NVIDIA’s DGX-1 rather than the commodity hardware Hadoop was developed for. Megaclouds ate Hadoop’s lunch •Megaclouds use the Hadoop distribution in their cloud services, but by unbundling the underlying file system (HDFS) from the cluster manager (YARN) and making these components inter- changible with alternatives, Hadoop is losing it’s position as a central nervous system for the data stack.
  89. 89. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 89 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.
  90. 90. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 90 CLOUD NATIVE …WILL IT FORTIFY MEGACLOUD LOCK-IN OR DISSOLVE IT? Amazon sees Lambda as another form of lock-in. It wouldn’t 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 Amazon’s lock-in grip. They hope to commoditize AWS by lowering switching costs with serverless. Google Functions
  91. 91. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 91 CLOUD NATIVE ML WILL SOON PLAY AN INTEGRAL ROLE FOR INFRA OPS AND APPDEV ML-powered cluster management Improving resource efficiency by adding a Netflix-like recommendation system to allocation of app components across resources Hyperpilot ML-powered static code analysis Analyzing code commits to figure 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 *
  92. 92. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 92 Vertical Theme #3 Cybersecurity
  93. 93. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 93 TL;DR SECURITY PREDICTIONS FOR 2017 1 SecDevOps blurs the lines between networking and application security as the race for cloud-native security products intensifies. 2 3 Beyond the 1%: SOIs as consumable microservices will bring advanced security technology to the 99% of companies who previously couldn’t 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
  94. 94. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 94 A bird’s eye view
  95. 95. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 95 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 *
  96. 96. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 96 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 workflows 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 first mover advantage to put all the pieces together * Work-Bench portfolio company *
  97. 97. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 97 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 • Traffic access point off firewall 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
  98. 98. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 98 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 *
  99. 99. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 99 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. It’s difficult to stay competitive with a single-product strategy because hackers will eventually figure out workarounds, rendering your product obsolete. FireEye’s 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.
  100. 100. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 100 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 first mover advantage over new entrants to gain X-ray vision up the stack. APPLICATION // CODE
  101. 101. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 101 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
  102. 102. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 102 CYBERSECURITY ABSTRACTED INFRA: THE TECHNICAL ASPECT OF NETWORK/APP BLUR NETWORK ENDPOINT Old-world app X-ray vision = server = mostly network data in firewall + 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
  103. 103. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 103 CYBERSECURITY BLURRING NETWORK/APP LINES CREATE NEW BATTLE FOR CLOUD NATIVE SEC NETWORKING OS HOST CONTAINER CODE IMAGE New “app dev first” 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 traffic 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 * * * * *
  104. 104. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 104 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 workflow 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 * *
  105. 105. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 105 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 * * *
  106. 106. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 106 CYBERSECURITY SOE ENTRANTS = SOAR, SECURITY WORKFLOW EXPERTS Security Operations, Analytics, and Reporting (SOAR) tools automatically run playbooks for common security workflows, freeing up limited analyst bandwidth to handle the more niche cases. It’s 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 workflow tool that integrates existing security tools Sources: Demisto
  107. 107. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 107 So how will this all play out?
  108. 108. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 108 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. Splunk’s 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. 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
  110. 110. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 110 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 firms 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?
  111. 111. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 111 Vertical Theme #4 Internet of Things (IoT)
  112. 112. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 112 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
  113. 113. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 113 So, have you ever IOT’d before?
  114. 114. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 114 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 refinery Natural gas drillerBuilding security systems
  115. 115. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 115 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 workflow management
  116. 116. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 116 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 field Connectivity Data Analytics Product Industrial IoT
  117. 117. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 117 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?
  118. 118. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 118 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: It’s All About the Business Processes” by BCG
  119. 119. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 119 Putting hype aside… IoT = new era of industrial competitive advantage
  120. 120. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 120 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 efficiency
  121. 121. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 121 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
  122. 122. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 122 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 ?
  123. 123. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 123 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 • Amazon’s 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
  124. 124. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 124 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 we’re starting to see healthy signs of cooperation GE for application PaaS Microsoft for IaaS
  125. 125. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 125 IoT requires highly distributed infrastructure
  126. 126. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 126 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
  127. 127. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 127 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 +
  128. 128. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 128 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 efficient, 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
  129. 129. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 129 INTERNET OF THINGS GATEWAYS ARE BEING INFUSED WITH SOFTWARE TO ENABLE EDGE COMPUTING Cisco’s Parstream allows for efficient, spoke-hub distribution of sensor data at IoT gateways Amazon’s Greengrass sends functions with complex event processing rules for data filtration and synchronization of digital shadows for managing asset state across low network environments Vapor.io retrofits 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
  130. 130. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 130 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
  131. 131. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 131 … Governing data flow will be a new analytics architecture Scenario: Brake failure preventative maintenance on remote supply transport train
  132. 132. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 132 INTERNET OF THINGS ANALYTICAL TOOLS BUILT FOR THE INTERNET DON’T 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
  133. 133. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 133 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 filtering Deep insights, model updates Device Expert operator feedback Data historians System architecture Software modules
  134. 134. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 134 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 *
  135. 135. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 135 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 aren’t 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 financial institutions will protect against external IoT “intruders.” Example startups
  136. 136. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 136 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 efficiently 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
  137. 137. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 137 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.
  138. 138. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 138 Lastly, A Few Tips for Entrepreneurs Going Forward
  139. 139. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 139 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
  140. 140. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 140 Megaclouds will be busy duking it out this year…
  141. 141. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 141 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 *
  142. 142. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 142 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 Amazon’s 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 *
  143. 143. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 143 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 flexibility 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 can’t 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 *
  144. 144. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 144 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 unified 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.
  145. 145. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 145 Megaclouds aren’t the only bullies, don’t forget the SaaS-holes
  146. 146. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 146 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
  147. 147. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 147 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% Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Year 11 First-time customers by deployment model (%) Subscription-based License-based 0 50 100 150 200 250 300 350 400 450 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Year 11 3-year TCO by year of sales contract ($USD, 000s) Annual subscription 3-year license 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
  148. 148. 2017 ENTERPRISE ALMANAC // @WORK_BENCH // #2017ALMANAC 148 Join our Work-Bench extended community Sign up for our Enterprise Weekly newsletter, a weekly digest of all things enterprise with 10K+ subscribers. WWW.WORK-BENCH.COM @WORK_BENCH HELLO@WORK-BENCH.COM thank youTHANK YOU MICHAEL YAMNITSKY Venture Partner, Work-Bench @ITSYAMNITSKY MICHAEL@WORK-BENCH.COM Please reach out to say hello!

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