1 THE ENTERPRISE ALMANAC 2018 EDITION #2018ALMANAC By Michael Yamnitsky 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 2 THE WORK-BENCH ENTERPRISE ALMANAC - 2018 EDITION PREAMBLE For the past five years at Work-Bench, we've been investing in a total reimagining of the enterprise technology stack. We're in the midst of a once in a decade tectonic shift of infrastructure that powers the Fortune 1000 and is unlike anything we've seen before. Whereas consumer tech has the Mary Meeker Internet Trends report for an aggregate view of industry trends, enterprise technology was missing a comprehensive overview of the key trends. Last year we took action and launched the inaugural Enterprise Almanac to share our thinking on these trends, and now we're pleased to release the updated 2018 Edition. Our primary aim is to help founders see the forest from the trees. For Fortune 1000 executives and other players in the ecosystem, it will help cut through the noise and marketing hype to see what really matters. It's wishful thinking, but we also hope new talent gets excited about enterprise after reading this report. By no means will most of the predictions be correct, but our purpose is to start the discussion by putting this stake in the ground. Please share any and all feedback via email at michael@work-bench.com or on Twitter at @ItsYamnitsky. MICHAEL YAMNITSKY Venture Partner, Work-Bench 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 3 ABOUT WORK-BENCH PREAMBLE ABOUT US Work-Bench is an enterprise technology focused venture fund based in NYC. OUR THESIS Customer-centricity. We make it our focus to deeply understand the business and IT needs of the Fortune 1000 in order to make more informed decisions in our search for the next enterprise giants. This is highly informed by our backgrounds in corporate IT at leading Wall Street banks and as Industry Analysts which is unique in the venture business. OUR MODEL Our model flows directly from our thesis. We leverage our deep corporate network in New York City and beyond as a way to identify trends, pick the winners, and secure customers for our portfolio companies. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 4 TABLE OF CONTENTS PREAMBLE 2018 Macro Perspective: The Empire Strikes Back 6 Vertical Theme #1: Machine Learning 22 Vertical Theme #2: Cloud Native 56 Vertical Theme #3: Cybersecurity 87 Vertical Theme #4: Decentralization of SaaS 103 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 5 Team Work-Bench Jonathan Lehr, Jessica Lin, Vipin Chamakkala, Kelley Mak, Kelley Henry, and Blake Jesse who added significant contributions and healthy debate for the content of this presentation. Thanks to Tommy Truong for his design work. Work-Bench Founders and CEOs For keeping me honest and never failing to surprise us with where technology can take us on this pale blue dot. SPECIAL THANKS PREAMBLE Our views are shaped by anecdotal evidence based on our interactions with entrepreneurs, Fortune 1000 executives, and big tech leaders. Take that for what it's worth. We have disclosed our investments where appropriate. Disclaimer 6 2018 MACRO PERSPECTIVE The Empire Strikes Back 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 7 OLD GUARD On-prem Empire VS. 10 YEARS AGO IN A GALAXY FAR, FAR AWAY MACRO PERSPECTIVE NEW GUARD Cloud Rebel Alliance 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC MEGACLOUD Bully Bazaars 8 OLD GUARD GROWTH GUARD Multi-cloud momentum fighters Early-stage startups + open- source Growth guard and old guard haven't peaked yet, with fight in them to go head to head with new guard TODAY, REBEL ALLIANCES FACE A MORE NIMBLE SET OF CONTENDERS MACRO PERSPECTIVE VS. NEW GUARD VS. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 9 PUSHING SHARP INVESTORS TO RETHINK COMPETITIVE ADVANTAGE MACRO PERSPECTIVE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC MEGACLOUD AGGRESSION CONTINUES FULL FORCE 10 Source: http://tomtunguz.com/mid-2018-ma/ M&A weighing in high on the scale these days with two of the biggest deals of the last 6 years closed in the last 12 months MACRO PERSPECTIVE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC REALITY: MEGACLOUDS = CHEAP WITH CASH; "BIG" M&A IS RELATIVE 11 MACRO PERSPECTIVE Source: Goldman Sachs Megaclouds spend a paltry 3% of cash on M&A; plenty of room to step up spending 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 12 Let's briefly speculate on megacloud M&A 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC AMAZON OR GOOGLE TO BUY SALESFORCE AS A KILLER APP IN THE CLOUD? 13 MACRO PERSPECTIVE Why not Feeling the brunt of Microsoft undercutting Dynamics to sell customers on going all in with Azure 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC MICROSOFT TO BUY OKTA TO REGAIN OWNERSHIP OF "ENTERPRISE" IDENTITY? MACRO PERSPECTIVE 14 Continues acquiring companies to regain identity foothold it lost in cloud Professional identity Personal identity Developer identity Enterprise identity 15 What about AI and its promise to reshape technology and transform business as we know it? 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 16 Systems of Intelligence are highly focused analytical systems intended to solve business challenges and objectives (i.e. increase revenue and customer experience, improve operations, reduce risk) Value created by: Integrating data from multiple sources include non- tradition information rich channels Novel new forms of data capture Cleverly optimizing the data preparation and AI training process Value created by: Embedding domain experts into the debugging and hyper-parameter tuning process Incorporating feedback from human experts into the system of record (SOR) Value created by: Designing products from data capabilities up to user experience and not the other way around Software UI as invisible as possible > fancy GUIs. Name of the game is making the workflow as seamless as possible. Original Framework Source: Jerry Chen's "The New Moats - Why Systems of Intelligence are the Next Defensible Business Model" 2017 = STARTUPS PROVED OUT SYSTEMS OF INTELLIGENCE (SOI) MODEL FOR AI MACRO PERSPECTIVE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 17 Data moat? Algorithmic differentiation? Data product differentiation? Weaker: still a significant barrier, but it's faster to develop and thus harder to sustain a data moat. Weaker: tough to sustain with open source, but there is some value in novel training, profiling, debugging, and testing processes. Competitive forces will be in flux as the AI landscape continues to develop at rapid speed. Here is where things currently stand and directionally where they are going: Stronger: The key value driver moving forward is developing products bottoms up, from data and analytical capabilities to features and user experience, and creating a virtuous loop between the two. Direction = whether this factor will be more or less significant 12-24 months from now Locus of focus shifting from the quantity you own to the process you use to sustain these assets* *For more on this topic, see Matt Turck's "The Power of Data Network Effects" AS MENTIONED LAST YEAR, COMPETITIVE FORCES CONTINUE TO BE IN FLUX MACRO PERSPECTIVE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 18 Arguably the king of SOI at the moment with very impressive suite of AI-powered BI offerings under 'Einstein' umbrella. Google leveraged its AI army to build Contact Center AI, with a suite of capabilities to automate customer support. Startups proved SOI in the form of point solutions, but larger vendors are coming in to stitch together capabilities into more holistic AI-powered software "suites" +1 for big tech, -1 for startup competitors CREATING THE PERFECT WINDOW FOR MEGACLOUDS TO #WIN IN SOI RACE MACRO PERSPECTIVE 19 But wait isn't the Achilles Heel of megaclouds a lack of focus on the details of real-world applications? well Google is proving us all wrong 20 2018 = AI startups retreating to the deep 'niches' to generate new competitive powers 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 21 COMING OUT THE WOODWORK: AI BIZ PROCESS AUTOMATION AS AN API MACRO PERSPECTIVE Example: Typical KYC/AML workflow: Information check Merlon automates all of this like Stripe automates payments Making financial AML and KYC compliance workflows as seamless to set up as Stripe, and as easy for analysts to use as TurboTax Simple checklist for analysts to monitor progress Sanction list screening Negative news screening Ongoing/ transaction monitoring *Merlon Intelligence is a Work-Bench portfolio company. 22 Vertical Theme #1 MACHINE LEARNING 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 23 TL;DR MEGACLOUD AGGRESSION CONTINUES FULL FORCE 1 To sell to BI? Data scientists? Or straight to the business? No matter who the buyer is, there continues to be a disconnect between buyers and sellers of data science tools. Crack the code and you have a billion dollar business in sight. The Empire Strikes back. Large technology companies are #winning at AI. Despite hopeful promise, startups racing to democratize AI are finding themselves stuck between open source and a cloud place. 2 3 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 24 MACHINE LEARNING FUTURE OF BUSINESS = DATA-DRIVEN (DUH) "By 2021, insights-driven business will steal $1.8 trillion a year in revenue from competitors that are not insights-driven" Source: https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC MACHINE LEARNING TO FUEL THE FIRE, DATA COLLECTION CONTINUES TO GROW UNABATED 25 90% of data in existence was created in the last 24 months Lying in these data sets are keys to advances in medicine, energy, etc. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 26 Demand for "Machine Learning Engineers" and "Data Scientists" has grown 6-10x in last 5 years Note: growth rates are calculated based on number of job postings on LinkedIn. "Trust me, I can explain" MACHINE LEARNING BUT THERE'S A SHORTAGE OF DATA SCIENTISTS TO MAKE SENSE OF IT ALL Source: LinkedIn's Emerging Jobs Report 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 27 Sources: https://www.pwc.com/us/en/library/data-science-and-analytics.html, McKinsey Global Institute "The Age of Analytics: Competing In A Data-Driven World." McKinsey Global Institute predicts the US economy will be short 250k data scientists by 2024. MACHINE LEARNING ENTERPRISES HAVE LOTS OF ANALYSTS, NOT ENOUGH DATA SCIENTISTS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 28 Most AI talent works here to optimize the output of MACHINE LEARNING BECAUSE THE MEGACLOUDS SCOOP UP ALL THE BEST TALENT 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 29 65% of execs are bullish about the potential of AI in their business But only 39% believed adoption will grow considerably in 2018 Source: https://www.forbes.com/sites/adigaskell/2018/03/07/employees-optimistic-about-working-with-ai MACHINE LEARNING 2018 ENTERPRISE AI SENTIMENT = PESSIMISTIC OPTIMISM 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 30 "Suits and Hoodies" at Goldman Sachs Ambitious attitudes: "AI is a competitive differentiator. We want to own the model, we don't want Palantir to own it." Realistic recruiting tactics: Avoiding talent wars with the web-scales by expanding hiring in India, and hiring Masters- levels rather than PhDs. Healthy skepticism: "We have lots of existing regression models that are finely tuned. Deep learning for many use cases is just going to be incremental and more expensive right?" Source: Quotes from interviews with machine learning executives at top-tier Wall Street banks MACHINE LEARNING BUT THEY ARE GETTING INTO GEAR TO DO IT RIGHT 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 31 #3: BUSINESS USER SELF-SERVICE #2: 'CITIZEN' DATA SCIENTISTS #1: DATA SCIENTISTS Central data science group delivering insights to the business. Business intelligence groups using GUI-based AutoML tools to deliver ML insights without the help of data scientists. Example Enterprise Example Enterprise Business people using software with pre-packaged ML models and reporting dashboards to generate ML insights. Source: Work-Bench estimates based on interviews with F1000 IT leaders. Example Enterprise Top Tier Bank MACHINE LEARNING 3 MODELS FOR DATA-DRIVEN INSIGHTS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 32 Data scientists take an open source first approach to tooling and will only pay for cheap point solutions that are easy to use and solve a very particular need. IT still has a bad taste in their mouth from failed big data projects. Black boxes are a definite NO! Business execs can barely use Tableau without an analyst's help, nevertheless attempt a new tool. 85% of Big Data projects fail* per Gartner MACHINE LEARNING NO FREE LUNCH FOR VENDORS, EACH BUYER HAS QUIRKS AND BAGGAGE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 33 I.E. they don't spend much on commercial software The industry is leaving SAS and going OSS MACHINE LEARNING DATA SCIENTISTS: TINKERERS WHO PREFER FLEXIBILITY/CONTROL OF OSS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 34 Source: S. Zayd Enam, Stanford AI Lab Root-cause analysis in AI is vastly more complex than regular software Algorithm design Implementation Two dimensions of investigation: Algorithm design Implementation Four dimensions of investigation: Algorithm design Implementation Choice of model Data It takes more experience to tune/debug machine-learning algorithms efficiently than to tune/debug regular code and longer time cycle testing the fix Software development = hours Machine learning = days Why? Re-training algorithm on dataset is time consuming, pushing a code change to production is not. MACHINE LEARNING BUT SINCE ML STILL AIN'T EASY 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC Model production, deployment, and monitoring 35 Modeling = easier to automate Data prep + model deployment = hard to automate MACHINE LEARNING STARTUPS ARE BUILDING AUTOMATION TOOLS SPANNING THE ENTIRE PIPELINE Model and hyper parameter configuration selection Model training and performance review Modifications to learning rate, regularization, feature pre- processing, etc. Feature engineering Data prep/ETL 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 36 MACHINE LEARNING LANDSCAPE OF VENDORS (AND OPEN SOURCE PROJECTS) IN ML AUTOMATION 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 37 How vendors market their AutoML products: How AutoML is actually perceived: Because most of a data scientist's time is spent here MACHINE LEARNING VENDORS AUTOMATING MODEL DEVELOPMENT TALK A VERY BIG GAME BUT Production AutoML Prep How AutoML is actually perceived: Production AutoML Prep Data Scientists generate most of their value much later when they apply predictions to figure out "what it means" for the business. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 38 "Our best bet are smart engineers, not AutoML tools. We need to scale our data science organization significantly to see real lift from these tools. Although there are areas where AutoML can help us, we cannot justify the investment." - Chief Data Officer at F500 eCommerce company "Hyper-parameter tuning [fancy term for time-intensive part of model generation] will soon be a commodity." - Head of Data Science at a bulge bracket bank MACHINE LEARNING PERCEIVED VALUE OF AUTOML = NOT HIGH ENOUGH TO JUSTIFY MAJOR SPEND 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC Soon Soon Soon 39 Cloud Composer Data Factory Batch AI "We will wait for what Amazon releases in this space" - Chief Data Officer at F500 eCommerce company MACHINE LEARNING MEANWHILE, MEGACLOUDS HAVEN'T BEEN SITTING IN THE PEANUT GALLERY Data prep/ETL Feature engineering Model training and tuning Model production, deployment, and monitoring 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 40 AutoML startups are tweaking their products and messaging to win over the Tableau wielding BI analyst crowd MACHINE LEARNING HENCE AUTOML CO'S RE-ORIENTING PRODUCTS TOWARDS THE BI ANALYST 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 41 MACHINE LEARNING WHEN YOU TRY TO CREATE PRODUCTS FOR MULTIPLE STAKEHOLDERS WHEN PLEASING ONE CAMP IS TOUGH ENOUGH Data scientists/ML engineers Business analysts No code GUIs Guardrails and 'training wheels' for inexperienced users Technical products Detailed feature sets and full extensibility No code GUIs, but complex for an unsophisticated user You get stuck in the middle *Algorithmia and Datalogue are Work-Bench portfolio companies. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 42 Expect all modern BI vendors to release an AutoML product or buy a startup by end of next year Tableau bought Empirical Systems for AutoML Looker customers can now do AutoML with new Google BigQueryML integration Source: https://techcrunch.com/2018/06/13/tableau-gets-ai-shot-in-the-arm-with-empirical-systems-acquisition/ MACHINE LEARNING BI VENDORS ARE COMING IN HOT Alteryx is known for its GUI-based ETL tool In 2017 it acquired data science startup Yhat Our hunch: Alteryx releases a GUI- based AutoML tool in early 2019 43 These AutoML vendors just need a break Anyway, moving on 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 44 The 'big data architect' got fired and was replaced with the data infra engineer, who's ready to propose a more nimble and distributed approach to developing an enterprise data platform for the business. Source: https://blog.cerebrodata.com/modern-self-service-data-platforms-c7c90a789068 MACHINE LEARNING MOVE OVER BIG DATA ARCHITECT, DATA INFRA ENGINEERS COMING IN 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 45 A new category dubbed "customer data platform" is emerging as a purpose-built data platform to help marketers be more data-driven without the help of special internal groups What they do today What they will do in the future Example vendors: 1. Collect data from multiple sources and organize them into constantly updating customer segments. 2. Allow marketers to program rules for how each segment received email automation programs. Automate more of what a data scientist can do for them: 1. Build AutoML features to allow marketers to make predictions like whether a customer will churn or migrate to another segment so they can take preventive measures. 2. Incorporate more general purpose BI functionality to merge the now disparate data management, analytics, and marketing execution functions. MACHINE LEARNING + VERTICAL-SPECIFIC DATA PLATFORMS = BYPASSING DATA SCIENTISTS + BI 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 46 Source: https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#4294c0036f63 Data scientists live in data cleansing and hate it Proliferation of cross-data set analytical projects. BI and data science teams are becoming more advanced, forcing IT to rip the bandaid off their failed data management initiatives and prepare disparate data sets for analysis. They are starved for any solution that can help them do it in an automated way! Interest in unstructured data. Most enterprise data is unstructured, and the promises of AI have all sorts of stakeholders in the enterprise wondering how they might be able to now leverage it. MACHINE LEARNING RAMPANT ENTERPRISE INTEREST IN AUTOMATED DATA PREP THIS YEAR 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 47 Data scientists rarely have strong engineering backgrounds and need help with the manual coding required to make their models API accessible to the rest of the business. = "We made 20 models last year. Only two made it to production!" Chief Data Scientist at a major Wall Street Bank. New tools are automating the process of model deployment much like Heroku automates Ruby app deployments MACHINE LEARNING MODEL DEPLOYMENT = RACE TO BE THE HEROKU OF ML *Algorithmia is a Work-Bench portfolio company. 48 We couldn't talk enterprise AI this year without mentioning this guy 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 49 Source: Morgan Stanley "2018 Outlook: Riding the Software Wave," January 2018. MACHINE LEARNING FROM ZERO TO HERO: SALESFORCE HAS NO MINDSHARE IN AI 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 50 Source: Salesforce Dreamforce Keynote, 2017 MACHINE LEARNING BUT SALESFORCE HAS KEPT BUSY BUYING UP AI STARTUPS THE LAST 3 YEARS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 51 GUI-based ML model builder to predict any blank field in SFDC Salesforce has stitched its newly acquired assets into a 100% automated machine learning pipeline Source: Salesforce Marketing Materials Note: unlike DataRobot and Dataiku, Einstein has the proper guardrails to ensure business users do not make mistakes like introducing target data leakage MACHINE LEARNING AI ELITISTS WILL STICK UP THEIR NOSES, BUT WE THINK SALESFORCE AI IS IMPRESSIVE Out of the box apps like a sales lead scorer 52 Quick peek into the future 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 53 Today's BI tools claim to democratize BIbut they hinge on an ideal, unrealistic requirements. They require a user who is capable of asking the: Right question. Asking the right question requires analytical skills. This is a hard ask for the nondata pros that make up the majority of employees. Correctly. Most BI tools have "intuitive" GUIs, but they still require extensive training to use and thus users end up using them incorrectly. On the right data. The user must have access to the right data and prerequisite knowledge about the underlying data structure to ask the right question in the first place. TL;DR: skilled business analyst remain the conduit through which the rest of the business interfaces with analytics. And vendors are constantly overthrown whenever analytics projects go haywire. MACHINE LEARNING BI HAS HISTORICALLY BEEN A MESSY INDUSTRY 1 2 3 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 54 Pointing you to anomalies in your data and providing a diagnosis And suggestions for how to respond to data trends as well as guidance on how to run small experiments on your data so the system can tune itself and better respond when an anomaly occurs. with a natural language interface to ask clarifications and guide the analysis 'Actionable insights engines' will fully automate the data scientist by directly linking cause and causality in business outcomes MACHINE LEARNING 'ACTIONABLE INSIGHTS' IS THE FUTURE OF BI (AND ALL SW FOR THAT MATTER) 3 2 1 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 55 Make your product so easy a 3 year old can use it! Drop the feature bloat you'll die before you become everything for everyone. Incremental algorithmic advances are hitting a plateau. Most of the unique value in your product will come down to the user experience. Don't build for multiple buyer personas. Not only is it hard enough to build something in the data science space with basic product market fit, but buyers are skeptical of collaboration platforms; "[X vendor's] collaboration features were never interesting to us because we don't believe collaboration between data science and BI teams is possible." - Head of ML & Analytics at a bulge bracket bank. Play nice with everyone. The space is crowded and it may seem like a war zone out there with large tech companies in adjacent areas making a splash in the space. Remember that the TAM is massive and no one has truly cracked the code here. Many can win. The market dynamic will be similar to BI, where there are multiple BI platforms with 90% functionality overlap. Integrate with everyone relevant, starting with the major BI vendors. Try going vertical. The exit opportunity won't be as large but at least you won't be competing with the million other startups attempting to be the Salesforce of data science. Take a more calculated use case driven bet, and make the solution truly closed loop. MACHINE LEARNING KEY TAKEAWAYS FOR EARLY-STAGE ML STARTUPS 56 Vertical Theme #2 CLOUD NATIVE With contributions by: Vipin Chamakkala Principal at Work-Bench & Cloud Native Sector Lead @V1P1N 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 57 Microsoft is making inroads in the enterprise. Google is still behind (for now) Vendor lock-in fears steer enterprises towards multi-cloud Cloud native story continues to unfold with digital transformation initiatives driving the inexorable shift Site reliability engineering (SRE) model is all the rage for operating the cloud. The battle has begun to be the Salesforce of SRE in cloud era Serverless continues to be a disruptive force, but more so for vendors than end customers today 1 TL;DR CLOUD PREDICTIONS 2 3 4 5 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 58 PRODUCT STRATEGY The monocloud that's good enough for most things, not amazing for anything. Heading down proprietary path as most services are integrally tied to their public cloud architecture. GTM STRATEGY Aggressive enterprise sales: lock-in, land-and-expand. BIG EXISTENTIAL QUESTION Amazon can't allocate 30 top PhDs to solve a single problem. Google can. PRODUCT STRATEGY Play to internal strengths: Underserved enterprise workloads like legacy Microsoft products, platform and application services for modern enterprise apps. GTM STRATEGY Strong enterprise support model. BIG EXISTENTIAL QUESTION Will enterprise chops trump Amazon's scale and scope? PRODUCT STRATEGY Google shines strength in machine learning, developer tools, and container orchestration (Kubernetes). GTM STRATEGY Historically Google hasn't catered to the enterprise with sales & support. Google Kubernetes Engine on-prem signaling a change? BIG EXISTENTIAL QUESTION Despite the incredibly nice people we meet at Google, enterprises feel Google is still arrogant towards them. How does Google change their rep? PLAYER #1 (CATEGORY LEADER): THE ENTERPRISE COSTCO PLAYER #2 (FOR NOW): ENTERPRISE HERITAGE PLAYER #3 (KILLER PRODUCTS): BUT WHERE'S THE ENTERPRISE LOVE? Besides a few serious regional players like Alibaba, global enterprises have 3 main marketplace bazaars to choose from to power their digital transformation: TL;DR MEGACLOUDS = CONTINUE TO DUKE IT OUT TO LOCK IN ENTERPRISES 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 59 Microsoft is at least as prevalent as Amazon in on- boarding late movers to the cloud And significant deployments have increased the most in Azure over the last 12 months Source: Morgan Stanley "2018 Outlook: Riding the Software Wave," January 2018. CLOUD NATIVE MICROSOFT = BIGGEST LEAP IN POSITION THIS YEAR 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 60 CLOUD NATIVE WITH MAJOR EXPANSION PLANS TO BOOT 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 61 CLOUD NATIVE GOOGLE = SIGNIFICANTLY BEHIND 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 62 "In 2018, the level of anxiety for decision makers at big organizations around vendor lock-in will continue to rise - replacing security as the #1 cloud concern." - Spencer Kimball, CEO of Cockroach Labs "Over 80% of enterprises show moderate to high levels of concerns about public cloud lock-in." - Stratascale Hybrid Cloud Survey CLOUD NATIVE REGARDLESS OF YOUR CHOICE OF POISON, VENDOR LOCK-IN IS REAL *Cockroach Labs is a Work-Bench portfolio company. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 63 CLOUD NATIVE MULTI-CLOUD = EVERYONE REPORTS HAVING A STRATEGY 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 64 Majority of enterprises are still struggling with containerizing applications, yet they have big lofty strategies that largely fall in 3 buckets: Choose a single cloud provider to go all in with Amazon wins over the majority of this segment, with Microsoft catching up Run workloads wherever they will be cheapest to deploy Kubernetes is a key tool enabling this segment to deploy workloads in the most capitally efficient manner Enterprise Example Enterprise Example Pick and choose clouds for different capabilities (i.e. AWS for cost, GCP for AI and data pipelines, Azure for legacy app migration) Individual teams must manage their own instances and budget, with IT implementing light-weight governance *Source: Work-Bench estimates based on interviews with F1000 IT executives. #3: THE FUNCTION BROKER MODEL #2: THE PRICE BROKER MODEL #1: THE MONOCLOUD MODEL CLOUD NATIVE THAT FEW HAVE EXECUTED ON Enterprise Example Still in Flight 65 The cloud native migration story start with customers and their harsh demands 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 66 Customer expectations = rising. Enterprise need to build software faster Only way to move fast enough is to split applications into easier to manage microservices Microservices are best housed in containers Containers are best governed by orchestrators Orchestrating containers at scale requires a service mesh network Result: move faster than competitors, save cost, and combat cloud vendor lock-in CLOUD NATIVE GOING CLOUD NATIVE IS THE ONLY RESOLUTION 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 67 Best bet for greenfield apps Most mature solution for scale out apps la carte option for running micro services on existing infrastructure 2017 CLOUD NATIVE ORCHESTRATION = KEY ENABLER OF MULTI-CLOUD; KUBERNETES = KING 2018 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 68 CLOUD NATIVE THE DATA SPEAKS FOR ITSELF 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 69 Source: https://resources.coreos.com/analyst-reports/hybrid-cloud-drives-growing-container-production-use-and-disruption-451-research-report CLOUD NATIVE HALF OF ENTERPRISES NOW USE CONTAINERS IN SOME CAPACITY 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 70 2X CLOUD NATIVE KUBERNETES ADOPTION = DOUBLED OVER LAST 12 MONTHS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 71 CLOUD NATIVE SOON IF YOU USE DOCKER, YOU WILL DE FACTO ALSO USE KUBERNETES 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 72 Services expertise Distributed systems expertise Joined the mix bringing direct expertise as the original creators of Kubernetes at Google CLOUD NATIVE RED HAT/COREOS + HEPTIO = RACING TO BRING F1000 TO KUBERNETES Bringing Kubernetes to the Enterprise *CoreOS was a Work-Bench portfolio company, acquired by Red Hat. 1 2 + 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 73 LinkerD Bouyant Istio CLOUD NATIVE SERVICE MESH = REMINISCENT OF DOCKER HYPE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 74 Layer 7 SDN-like overlay network infrastructure for controlling service to service traffic in microservices applications What is Service Mesh? Knowing where your app is CLOUD NATIVE SERVICE MESH = IMPORTANT MICROSERVICES GLUE Benefits Developer independence App-aware load balancing Microsegmentation Identity assignment 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 75 Service mesh adoption still early in 2018. Service meshes are like broccoli you know you need them but only adopt when you feel the pain of not having them. With enterprise container and microservices deployments in infancy, we may be waiting some time for service mesh adoption to kick into gear. Continued 'school clique' dynamics within the community. While most in the real world doesn't care, small highly opinionated camps are forming between Istio and Linkerd. Building an enterprise product in a competitive market governed by fickle open- source "communities" will be a challenge commercial vendors in this space will grapple with. Security will be the killer use case for service meshes. Scenario: 'digital transformation' AppDev teams at enterprises writing microservices on AWS/Kubernetes that need to communicate with on-premise services in a secure manner. Services meshes are the perfect answer. CLOUD NATIVE SERVICE MESH PREDICTIONS 76 How will enterprises deal with all this? 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 77 In other words, SREs are IT ops pros in a software-defined infrastructure world Source: https://landing.google.com/sre/interview/ben-treynor.html CLOUD NATIVE ENTER THE SITE-RELIABILITY ENGINEER (SRE) 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 78 Monitoring CLOUD NATIVE THE SRE TOOLS LANDSCAPE SPANS 5 MAJOR CATEGORIES Alerting Ticketing APM Logging 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 79 In today's "bigger is better" environment, vendors will jockey into leadership position to own the mind and wallet share of SREs by unifying tools & workflows: Monitoring/Tracing Alerting Ticketing Logging Future New startups will have to bring a fresh angle to the table to compete with the growth players and their comprehensive "suite" approaches. Some will enter the market by positioning themselves as built for "scale" or fill a niche with a creative approach like crowdsourcing post- mortem analysis insights to help build the SRE playbooks of tomorrow. CLOUD NATIVE TO KEEP UP WITH THE INCREASING PACE OF OPS, TOOLS MUST INTEGRATE of SRE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 80 Chaos engineering is the discipline of stress testing a distributed system to build confidence in its ability to withstand turbulent production conditions (like the traffic load of an eCommerce website on Black Friday). New tools are emerging to facilitate the process: This stuff is real. Top enterprises (like below) are hiring chaos engineers: What is it: CLOUD NATIVE CHAOS ENG. EMERGING TO STRESS TEST INFRASTRUCTURE HOLISTICALLY 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 81 Bouyant * Holistic "observability" across infra, traces, logs and all forms of environments (containers/serverless) is all the rage because today's complex/heterogenous environments are spewing out rich data that cannot be viewed in isolation. Legacy vendors and their point solutions won't last in this next evolution of monitoring as IT will refuse to manage haphazard integration projects. CLOUD NATIVE FULL STACK MONITORING (I.E. OBSERVABILITY) IS ALL THE RAGE *Backtrace is a Work-Bench portfolio company. 82 Sailing into the serverless era 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 83 App components Resource utilization"Pay-as-you-go": theoretical maximum utilization of infrastructure Spend per server: "pay-as-you-go" vs. serverless "Serverless": actual instance run rate idle time run time Microservices = more shallow utilization across a wider footprint = uneconomical with server-based units of measurement in the "pay-as- you-go" business model Serverless lowers operating costs for software vendors. CLOUD NATIVE SERVERLESS COMPUTING MAKES FINANCIAL SENSE OF MICROSERVICES 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 84 With serverless, it's more economical for an early-stage company to deliver complex middleware functionality via elastically scalable compute and memory Without serverless, these companies wouldn't be as economically viable Automating ML model deployment 'Headless' CMS Clicks-no-code SaaS integrations CLOUD NATIVE SERVERLESS = ENABLING A NEW GENERATION OF STARTUPS *Algorithmia is a Work-Bench portfolio company. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 85 "Today, developers have begun to publish and share individual functions. Imagine pushing a button on Github to instantiate a function or an application. Elegant on-ramp for a developer to become a paying Azure customer." - Tomasz Tunguz, Redpoint Source: http://tomtunguz.com/microsoft-github/ + = lock-in? Serverless is the ultimate vendor lock-in as the vendor manages the full stack from server to the runtime layer CLOUD NATIVE SERVERLESS = WEAPON FOR MEGACLOUD LOCK-IN 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 86 Google Spanner Transactional integrity Availability Scalability Microsoft Cosmos CockroachDB Consistent performance remains a hurdle for serverless Without a distributed database you have a distributed monolith CLOUD NATIVE DISTRIBUTED DATABASES = KEY INFRASTRUCTURE FOR SERVERLESS 87 Vertical Theme #3 CYBERSECURITY With contributions by: Kelley Mak Principal at Work-Bench & Security Lead @kelleymak 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 88 Security engineering teams will lead shift from bolt-on to built-in security deployed as code GTM motions for enterprise security startups will take a cue from the open source world 1 2 3 CISO = no longer the only security buyer; VP Infrastructure/Security Engineer is the new buyer/ champion combo to master TL;DR SECURITY PREDICTIONS FOR 2018 89 Year in Review 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 90 CYBERSECURITY SECURITY FUNDINGS = RECORD HIGH; NEWBIES ENTERING THE MIX 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 91 Cyclical trend as fewer enterprises refreshed their firewall last year? Or more secular shift as security moves to the cloud where it is embedded in the infrastructure? Source: Morgan Stanley "2018 Outlook: Riding the Software Wave," January 2018. CYBERSECURITY YET REVENUE GROWTH = SLOWING DOWN FOR PUBLIC SECURITY CO'S 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 92 You know a tech category is inching past early adoption when There's a Gartner MQ and a Forrester Wave $250M+ acquisitions by the incumbents + startups raising $100M+ growth rounds to go big Prediction: Netskope goes public or gets acquired for 8-figures by 2020 CYBERSECURITY CASB = WELL WITHIN HYPER-GROWTH PHASE 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 93 Sizable M&A for SOAR (Security Operations, Analytics, and Reporting) vendors focused on automation. While automation is hot, there is still a gap in triage analytics and response workflow tools $350M $100M CYBERSECURITY SOAR = CONTINUES TO BE DARLING ACQUISITION TARGET FOR LEGACY CO'S *Uplevel Security is a Work-Bench portfolio company. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 94 Data privacy tools Helpful tools for IT to comply with GDPR regulation in accordance with a legal team's mandate Software 'legalese' vendors Closed loop solutions that replace the legal decision making regarding GDPR with software Market shakeout: Data discovery capabilities will trump legal expertise > CYBERSECURITY GDPR = TWO NEW CAMPS OF VENDORS TO HELP 95 Ok, now on to the exciting stuff ahead 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 96 Source: https://www.forrester.com/report/CISO+Career+Paths+Plot+Your+Course+For+Advancement/-/E-RES141371 Who they were Leader of the Infosec team/tech/process Hands on owner of the firewall, endpoint protection, and security operations Who they're becoming Manager ensuring IT stays in compliance C-level and board liaison communicating the cyber risk profile of the firm More likely a suit than a geek. 45% have MBAs; 18% have CS degrees.* What it means: Traditional 'infosec' becomes back office as CISOs increase reliance on outsoucers/MSSPs to clear bandwidth for executive relations. CYBERSECURITY CISOS EVOLVING TO LOOK MORE LIKE CIOS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 97 Source: https://www.aporeto.com/blogs/hybrid-and-multi-cloud-security/enterprises-becoming-cloud-native-journey-thoughts-kubecon/ Example: Goal: fix this paradigm CYBERSECURITY SECURITY ENGINEERING ORG = EMERGING AS CHAMPION FOR NEW TECH Shared service group of engineers that sit between infra and appdev organizations. Knowledgeable about both appdev and security. Mandate to embed security into appdev process rather than bolt it on as a last step before prod. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 98 22% of enterprises use microservices in some capacity today Cloud-native security trends favor security to be built-in as opposed to bolted onto infrastructure Security engineers have the hybrid DevSecOps skills to lead the implementation of cloud-native security Source: https://www.forrester.com/report/Microservices+And+External+APIs+Underpin+Digital+Business/ CYBERSECURITY SECURITY ENGINEERING EMERGING BECAUSE MICROSERVICES HEATING UP 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 99 Whereas CISO and Infra teams think bolt-on, the security engineering orgs think built-in CYBERSECURITY SECURITY ENGINEERING ORG = NEW CHAMPION FOR BUILT-IN SECURITY TECH Built-in Bolt-on *vArmour & Scytale are Work-Bench portfolio companies. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 100 TL;DR: Identity is an essential first building block of microservices security, yet it has played second fiddle to network monitoring CYBERSECURITY MICROSERVICES SECURITY MATURITY FRAMEWORK STEP #3: LOG ANALYTICS STEP #2: NETWORK/RUNTIME MONITORING/HARDENING STEP #1: IDENTITY *Scytale is a Work-Bench portfolio company. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 101 CYBERSECURITY PROPRIETARY CORES ARE A THING OF THE PAST, NEW CO'S BUILT ON OSS *Scytale is a Work-Bench portfolio company. 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 102 The depth of new built-in security products are beyond the scope of CISO org. Target security engineers as champions and infrastructure leaders as buyers. Open source is the new marketing tactic for enterprise security. Build a community of web-scale security engineers and the F1000s will follow. InfoSec startups: target the progressive newly minted CISOs looking to implement the basics. CYBERSECURITY KEY TAKEAWAYS FOR EARLY-STAGE SECURITY STARTUPS 103 Vertical Theme #4 The de(Centralization) of SaaS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 104 Open source business software will experience a rebirth Business software goes VPC There will be a day when developers choose their company's business software 1 2 3 4 Ebbs and flows: SaaS ate infrastructure. In due time, infrastructure will eat SaaS TL;DR SAAS PREDICTIONS FOR 2018 105 Megaclouds aren't the only bullies 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 106 SaaS-holes are the new status quo 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC SaaS vendors are becoming mighty and taking advantage of it using aggressive tactics to expand dollar share within existing accounts, often by shoving excessive features and extensive contract terms down customers' throats. Customers have no choice but to succumb to these closed-ecosystem tactics. 107 Rulers of the front office Kings of the back office SAAS SAAS MONARCHIES RULE BUSINESS SOFTWARE 108 SaaS-hole death grip? 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 109 SaaS providers manage your business logic and the latest copy of your data Meaning they can up-charge you 30% or more every year Up-charging: You can either spend millions with the vendor's preferred service provider Or spend 12-months recruiting a team with the right skills to manage customization Complexifying their technology to foist services contracts: SAAS SAAS-HOLE DEATH GRIP TACTICS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 110 Short/medium term: macros favor large SaaS vendors SaaS ate infrastructure: SaaS integrates all layers of the IT stack, allowing vendors to extract more rents than with on-premise software. Suites > best of breed: It's the Costco mindset: Enterprises choose the platform with the most features to prepare for tomorrow, even if they don't need it today. Systems of intelligence: Value increases with scale: more customers = more partners = more functionality = more data = improved algorithms = better functionality and user experience = hard to migrate. SAAS SAAS-HOLES = ONLY GETTING RUDER 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 111 Salesforce commands greater share than Siebel ever did SAAS SALESFORCE = VERTICAL DISRUPTOR 1 In part because SaaS eats infrastructure 2 Forcing analysts to constantly adjust their market forecasts 3 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 112 Cloud SaaS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 113 Running your own software is cheap: SaaS provided favorable economics in a time when running your infrastructure was expensive. Cloud compute is now cheap and readily available. Installing software is easy: SaaS made installing software easy at a time when configuration was difficult. Docker/Kubernetes changes all this: software can be installed anywhere and live in minutes. Data security is imperative: SaaS was never viewed as more secure than on- premise software but the cost savings and flexibility outweighed the risks. CASBs emerged to provide a salve for the CISO but with data breaches at an all time high and GDPR mandates looming, we'll see the SaaS/on-premise equation re-examined. "It's time to reconsider the SaaS model in a modern context, integrating developments of the last nearly two decades so that enterprise software can reach its full potential." Grant Miller, CEO of Replicated Source: https://techcrunch.com/2018/06/17/after-twenty-years-of-salesforce-what-marc-benioff-got-right-and-wrong-about-the-cloud/ SAAS IF SAAS ATE INFRA, CLOUD WILL EVENTUALLY EAT SAAS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 114 Abstraction is shifting further from infrastructure to the app layer to speed application development and customization, diluting the need for 'SaaS as an app platform' Server, storage, network VM OS Database Middleware App SAAS RAPID DEVELOPMENT IN THE CLOUD EATS AT SAAS MOAT 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 115 SAAS ISN'T IT IRONIC, DON'T YOU THINK? Oracle/Siebel Pricing Multi-year contracts Sales Aggressive enterprise sales focused on cross-sell and account penetration Marketing Annual conference with disgruntled customers Ecosystem Proprietary Technology Oracle hardware Salesforce 1999 Pricing Monthly subscription Sales Free trials, friendly telesales Marketing Infamous "no software" drum beat Ecosystem Open Technology Modern architecture Salesforce 2018 Pricing Multi-year contracts Sales Aggressive enterprise sales focused on cross-sell and account penetration Marketing Annual conference with disgruntled customers Ecosystem Proprietary Technology Oracle hardware Note: we're only picking on Salesforce because it is the most successful SaaS vendor. The Pendulum Has Swung Back 116 Is it possible to break the great SaaS moat? 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 117 SaaS customization = $$$ Containers and microservices = freedom Containers and microservices are at odds with SaaS customization In a world where services written in different languages can easily communicate, proprietary languages and processes that require hiring "experts" will be obsolete. = ? Prediction: In the future, customization will be simple and led by internal developers SAAS SAAS DISRUPTION: BREAK THE CUSTOMIZATION MOAT? 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 118 The rise of AWS and quick-spinning database services means software no longer must be beholden to the underlying infrastructure. Enterprises already prefer VPC to SaaS for infrastructure tools, and we expect business software to follow suit. Example: Mattermost offers an enterprise-grade VPC alternative to Slack Easy, one-click to deploy Mattermost to any cloud platform: Source: mattermost.com SAAS SAAS DISRUPTION: BREAK THE EASE OF DEPLOYMENT MOAT? 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 119 #1. Salesforce is the new IBM. Many major enterprises just migrated to Salesforce, and no one will fire you for buying Saleforce. #2. It's easier to budget for SaaS than headcount to self-manage software. Most enterprises are strapped for developers; they need them for customer-facing software first and foremost. #3. SaaS out-of-the-box functionality is hard to match. Major SaaS vendors integrate with everything. Disruptors face an uphill battle figuring out what "good enough" would mean. #4. If it ain't broke don't fix it mentality is still a thing. Organizations with a major reliance on SaaS platforms are stuck in the same sunk cost fallacy as with the on-premise software of yesteryear. TL;DR: The disruption of SaaS will take many years to play out. Either the numerous pain points will become acute enough to take action or we'll have to wait for today's CIOs to overturn and for a new generation of buyers to come in. SAAS BUT DON'T GET TOO EXCITED KIDS 2018 ENTERPRISE ALMANAC // @WORK_BENCH // #2018ALMANAC 120 After all, they feel the biggest SaaS tax dealing with the cleanup when integration and customization work hits the fan Contentful breaks down a CMS into a barebones set of individual microservices developers love to use. Happy developers = happy customers. Source: contentful.com = decoupled and developer friendly CMS Example: SAAS PERHAPS DEVELOPERS WILL CATALYZE THE SHIFT? 121 Join our Work-Bench extended community Sign up for our Enterprise Weekly newsletter, a weekly digest of all things enterprise with 15K+ subscribers WWW.WORK-BENCH.COM @WORK_BENCH HELLO@WORK-BENCH.COM THANK YOU! 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