Healthcare consumes more than 17% of US GDP, and our government is on course to go bankrupt based on promises it’s made in the space. This is nearly twice what other major countries spend, and those countries aren’t exactly efficient either — but we in the US are worse. Whether or not you blame companies, our regulatory framework, or both, this is clearly a huge problem.
Health insurance is the main system we use to manage healthcare, and realistic solutions from both political parties acknowledge that the nearly 1 trillion USD in market cap of these companies is not going away. Insurance companies will continue to play a central role in our health system for years to come. Healthcare is a giant ecosystem with a lot of players and interests, and in the US, insurance companies influence which new practices are applied and when. A lot of us in the technology world are immensely frustrated because conservative and tech-backwards insurance giants are often slow to implement advances that could save people money and improve patient experiences and outcomes, such as data-driven preventative medicine apps or emerging IT-enabled genomic tests.
On the bright side for entrepreneurs, like many other big industries at the moment, there is a giant gap between how insurance companies work today and how they could work if they were more technology-savvy and efficient. Frankly, I’m surprised at how wasteful and archaic a lot of the industry is — for example, many of these companies still hire thousands of fax operators and data transcribers. It’s as if nobody told them the ’80s are over. Don’t get me wrong, there are a lot of smart and highly motivated doctors in the system, and great hospitals pushing key innovations — but they aren’t able to drive the kind of change needed without the insurance industry playing its part.
Oscar, the first new for-profit insurance company in 15 years, is working hard to do just that. By creating a technology-driven culture and pairing it with some of the most experienced forward-thinking experts from the industry, Oscar is just starting to scratch the surface of what’s possible. From utilizing telemedicine to make doctors more accessible, to showing customers the data they need to make more informed care decisions, Oscar is already using new techniques to allow patients to get great care more efficiently and enable doctors to deliver a better experience at the same time.
This role is especially important as new advances emerge over the coming years that will change the healthcare landscape: synthetic biology, genomic IT, big data-enabled diagnostics and new forms of preventative medicine. As these new technologies are proven out, it will be critical for the healthcare system to adopt them correctly to cut waste and ensure better outcomes.
Insurance sits at base of our whole system and decides on the incentives for every player. Simply put, more efficient, more accessible and smarter insurance companies will help make that system better. Oscar is the first health insurance company to combine great technology (a truly elite group of hackers and designers) with forward thinking industry veterans. The company is well positioned to play an important role in the evolution of our health system, and 8VC is proud to be part of the mission.
The Conventionalization of Big Data
Joe|November 1, 2013
“But far more numerous was the herd of such, who think too little, and who talk too much”
– John Dryden
The term ‘big data’ is a pervasive Silicon Valley colloquialism. The expression now describes a range of technologies, from back-end infrastructure to front-end consumer software applications. This development corresponds with a trend: the allocation of billions of dollars by investors into ‘big data’ companies which, simply put, stand little chance of becoming transformative companies.
This statement may be unexpected coming from a group which is frequently identified with ‘big data’ companies. To be certain, we focus on a subset of ‘big data’: Smart Enterprise Data Platforms, which we described in Platform Plays and elsewhere. The term, however, has grown to encompass a much larger (and far less valuable) group of companies.
The chart above illustrates the rapid conventionalization of ‘big data’ over the past two years. This should scare investors. We remember there was much truth behind the “new economy paradigms” of the late 90′s, but most of the resulting companies were nonsense. As investors we know that investing along popular sentiment leads to crowded trades and lost money, and this time it won’t be any different.
What are the most common mistakes being made in the area? We offer five archetypes of investments to avoid as well as positive attributes of the Smart Enterprise companies we focus on. We hope these will help guide entrepreneurs to properly allocate their time and investors to better allocate their capital.
Common ‘Big Data’ Archetypes to Avoid:
Dashboards, Visualization Tools and Presentation Layer.
Making data consumable is vital, and a fundamental feature of a Smart Enterprise company. Visualization, however, is just that — a feature. Standalone companies create the bulk of their value by using proprietary data to continuously improve work flows and build network effects, not simply displaying it. Dashboards are one feature of a Smart Enterprise company, but rarely represent a successful company’s core value proposition. We see Tableau (NYSE:DATA) as an exception which proves the rule — a well-executed first mover which leaves little room (or need) for competition.
Horizontal Business Intelligence and Analytics.
Structuring data and drawing insights is vital for companies, especially given the exponential growth in data created by modern enterprises. This data is often disparate and idiosyncratic. Companies that are industry agnostic ignore the importance of owning and understanding work flows in structuring data. We see these companies facing three common pitfalls: 1) They fail to produce a product which is differentiated; 2) They over-build the technology and never produce a product; or 3) They are forced to customize to win customers, and become more a consultancy than a platform.
Artificial Intelligence and Predictions.
Advances in data science and software engineering have paved the way for computers to contribute in decision-making, but automation of strategic thinking is still science fiction. Companies that base their value proposition off of making predictions or replacing high-skill knowledge workers ignore the limitations of computers and the realities of selling a product. AI plays a vital role in many Smart Enterprise companies. But we do not invest in “black-box” algorithms; we invest in companies which use the power of computers to structure data and expose results for people to use. “Black boxes” are rarely valuable in their own right, and when they are, they are not billion dollar companies. As stated previously in The Smart Enterprise Wave, our interest is in technology which augments and extends the human mind, not that which attempts to replace it. One day, there may be an exception; but AI and predictive technology is not a coherent business strategy.
Reliance on Partnerships for Data / No Ownership of Infrastructure.
The key to generating proprietary data is owning infrastructure, and in many industries the infrastructure seems locked away by incumbents. Examples include EMRs in healthcare, inter-bank networks in financial services and hardware in agriculture. Many ambitious young companies partner with incumbents to access data. This approach is a sound one; in many cases infrastructure is so entrenched in core work flows that partnership is the logical first step. However, this dependence means companies are constantly at risk of being held hostage. Smart Enterprise companies mitigate this risk by aligning incentives and creating inter-dependency with necessary partners, and then quickly work to build work flow tools which turn user engagement into proprietary data.
Company solves a Technology Challenge, not a Business Need.
The increase in data generated by enterprises has presented a variety of difficult technical problems. Many of the top data scientists and software engineers are excited by these problems, and are building companies to bring their solutions to market. While intellectually interesting, not all of the technical challenges presented by increasing data scale are valuable to businesses. We find a great number of clever technologists (especially in more academic geographies such as Boston) who start with an intriguing technology solution and then search for a business application. We believe the most valuable companies solving real problems will be vertically focused and have direct influence on crucial business processes. These companies empower knowledge workers to directly create ROI — they are not clever technology which sits as middleware or back-end infrastructure.
Key Aspects of a Smart Enterprise Company
Vertically Focused Work Flow Software.
Certain business functions are similar across industry verticals and therefore are appropriately served by horizontal platforms (e.g. Workday, RelateIQ). However, ‘front-office’ roles (which tend to be the main driver of business success) demand software which is specialized. Building software which standardizes the best practices from the industry improves these vital workflows, and in doing so previously disparate meta data produced by engagement is now structured and ‘purpose built’ to be re-introduced in a meaningful way.
Network and Platform Effects.
Advances in computer science, data storage, and infrastructure have made software more cost efficient to create. While this phenomenon has driven innovation, it has also reduced technical barriers to entry in software. Entrepreneurs must thoughtfully architect defensibility into their business models; it is not enough to deliver a better and cheaper solution. Companies should structure solutions where each additional client brings more value to the platform. For example, networks are strengthened when additional clients contribute relevant data that can be used (anonymously if needed) by the broader customer base, or when an ecosystem of applications develops on top of the platform.
Solves a Real Business Problem (Corollary: Replace Existing Spend).
Smart Enterprise companies are first and foremost technology driven, but to succeed they must also solve current problems for business. The most critical business needs are almost always manifested on the income statement; identifying the line item being targeted is crucial. That isn’t to say that the analogy is obvious to find — a quill and a printing press look quite different — but a successful company will be laser focused on the cost center it seeks to usurp. It is rare that an immediate business challenge isn’t being tackled in some way, so be skeptical of companies looking to solve a problem that is currently completely ignored. Many times this is evidence of a non-problem, a sign of a fundamental lack of domain expertise, or the premonition of an impossibly long sale cycle.
There has been a shift in market focus from consumer to enterprise technology over the past two years, resulting in a proliferation of companies looking to leverage data. We are excited as well, but we caution over-optimism. We urge investors and entrepreneurs alike to think beyond ‘big data’ and study the workflows and challenges facing our economy’s key industries.
The promise of the Smart Enterprise wave is radical improvement to the core workflows of industry. These new companies will improve outcomes in healthcare, drive transparency in finance and transform energy infrastructure, among other improvements. Building these companies is not easy. Billions of dollars and man hours will be wasted by ‘big data’ companies which fail to target the right areas. This is a shame. We urge investors and entrepreneurs to re-focus on solving proven business problems in the core industries of our economy. With human and financial capital properly allocated, we are optimistic humanity will realize increases in prosperity at a rate never experienced before.
B2G: The Excitement Of An Old-Line Industry
Joe|October 15, 2013
“It is not ‘Can any of us imagine better?’ but ‘Can we all do better?’
Object whatsoever is possible, still the question recurs, ‘Can we do better?’”
– Abraham Lincoln, Second Annual Message to Congress, 1862
Government represents one of the most challenging sectors in which to build a business. Yet the challenges represent opportunities for those bold enough to tackle them. Winning requires patience, deep pockets, and cutting-edge technology.Below we discuss four key points that indicate dramatic upside for the best companies: 1) Old technology provides opportunities for order-of-magnitude improvements; 2) Big institutions signal huge markets; 3) Industry pressures demand new efficiencies; and 4) Challenging sales cycles increase barriers to entry and foster customer retention. One can expect similar dynamics across other old-line industries like finance, energy, healthcare, and education.
1. Old Technology Provides Opportunities for Order-of-magnitude Improvements
Government information technology is notoriously antiquated. Interfaces stem from the 1980s and 1990s. Code-bases in most enterprise platforms stem from the 1970s. There’s paper everywhere. Most major deployments are customized monstrosities built by paid-by-the-hour engineers from giant consultancies. Dissatisfied customers represent the norm.
Why is this exciting? Because chances for radical improvement lay around like gold nuggets in 1849. New technologies can streamline basic workflows across whole enterprises, and create transparency and intelligence where little exists. Collectively, this represents an opportunity to fix the world’s most important and pervasive industry by shining a light on inefficient structures and enabling insights and comparisons that are impossible with current IT.
Consider a basic question that the CEO of a city (called a City Manager) might ask: “How much have we spent on police pensions in the last five years?” Finding that answer in most cities constitutes a research project. One might call “IT” to run reports from the accounting system, scroll through Excel spreadsheets with tens of thousands of rows to find a few disparate lines, or sift through a 300-page budget PDF.
With new reporting technology, that question and others like it now require five clicks and about 15 seconds. The answer comes with manifold visualizations, each of which can be exported and shared digitally or in print.
More complicated workflows result in more dramatic gains. Comparing actual operating expenses to budget, for instance, requires hours each month from department, division, and program directors across a city. Rather than catch bad guys, the police chief must sift through Excel spreadsheets to see whether his units are spending according to plan. Some governments forego certain analyses altogether, waiting until the end of the year to evaluate performance.
These workflows can now take minutes, and department heads and finance professionals can complete them on the web from their laptops in their pajamas over the weekend (rather than the customary in-office desktop log-in). For its part, the City Council (like a Board of Directors) can prepare for weekly and monthly meetings without bothering senior staff for expenditure reports.
Man-machine symbiosis, which we have described in The Coming Transformation, reaches its apogee in the government space. Hard, zero-sum decisions present themselves weekly, and highly trained and experienced managers make million-dollar decisions as a matter of course. The best technology enables better policy analysis and trade-offs, while increasing the accuracy of, and reducing the time required to, pull data and crunch it.
2. BIG INSTITUTIONS SIGNAL HUGE MARKETS
Even small governments make for big customers. By revenue, many cities and counties would rank in the Fortune 500. These massive institutions, the number of them, and the need for productivity and technology gains means that extracting value is possible at scale.
The U.S. boasts 19,000 cities and towns, and twice that number of Special Districts. (Cities are typically general-purpose governments that provide a range of services like fire, police, library, and parks, whereas special districts are special-purpose governments that serve one function like the provision of water, sewers, or flood control). There are 12,000 school districts, 3,000 counties, and 50 states, each with hundreds (or thousands) of separate agencies and departments.
The United States has 50 states, 3,000 counties, 12,000 school districts, 19,000 cities and towns, 38,000 special districts, and tens of thousands of state agencies and departments.
The nearly $60 BN spent in the U.S. by these 80,000 state and local governments on IT will grow around the rate of inflation (3%+). Software sales will grow faster both nominally and as a percentage of the pie. Spending on hardware and consulting will shrink. Whether one looks to create a niche business or accrue a big chunk of the market, opportunities are ripe for software-as-a-service (SaaS) models, business intelligence, and waste and fraud prevention technology.
Equity opportunities may be significant for early employees and investors in major enterprise endeavors. (Opportunity costs for those focused on games, mobile apps, or new social networks may be significant as well). We see an additional element of social responsibility involved, however. Our society and the large institutions that direct its course suffer from a decaying IT infrastructure. New advances in technology make possible a reformulation of sprawling bureaucracies and their workflows. This will result in years of time-savings, improved decisions, and a stronger cultural fabric based on value creation and productivity gains.
3. Industry Pressures Demand New Efficiencies
Three cities in California fell bankrupt in 2012. In 2013, Detroit set the record for the largest Chapter 9 filing. With tax revenues just returning to pre-2008 levels, governments need to do more with less. Accordingly, products that can increase “top line” revenues or reduce “bottom line” costs will win customers and build value.
New technologies will enable cities large and small not only to produce insights from their own data, but also to compare spending trends to those in other cities. Performance benchmarks or “apples-to-apples comparisons” have been called the “holy grail” of municipal finance, because present capabilities force cities to call neighboring cities to gather data or hire expensive consultants to prepare reports that go out of date by the time they print.
In addition to comparative analytics, cities will take advantage of financial trend monitoring and drill-downs to the “checkbook level.” Imagine seeing not only a budget category, like uniforms for the fire department, but also the payments for the boots, pants, and belts. This will result in cost savings through comparisons with purchases from other municipalities and improved competition among vendors in bidding.
Immense technical challenges present themselves. Platforms will leverage natural language processing to map the ontology of charts of accounts (the financial structure of a government), machine learning to recommend quality comparisons and throw up flags for financial trend monitoring (“your reserve balances have declined for three years in a row — might want to check that!”), and rapid processing and searching of massive transactional data sets to answer questions on and find efficiencies in vendor payments (for example, to show all payments between $25,000 and $50,000 out of enterprise funds and help evaluate them against market metrics).
Initially, this type of technology saves time for government knowledge workers accessing and analyzing data. It can then grow into software that will save billions in the aggregate by improving decisions and offering intelligent comparisons and recommendations across the expenditure spectrum. The challenges require rare engineering expertise across a variety of subfields; the solutions will come from true technology companies, not large consultancies.
4. Challenging Sales Cycles Increase Barriers to Entry and Foster Customer Retention
Government purchasing processes are cumbersome. Governments frequently require “wet-signatures” on snail-mailed “hard-copies” of contracts. Vendors have to pay for business licenses, show proof of insurance policies, and file extensive (even notarized) disclosures. Organizational cultures reward risk-aversion and conservative leadership.
The weeks or months spent in the government sales process explains why so many smart technologists focus on the consumer space. Entrepreneurs launch start-ups so they can avoid bureaucracies. But this gap between consumer and enterprise in the startup world gives the best enterprise startups room to run.
In the mobile world, a startup might expect 5% attrition per month. In the government space, expect less than 5% attrition per year. Provided one keeps improving, serving, and building value, “once you’re in, you’re in” and customers may stay customers for years.
With officials recognizing the imperatives of innovation and embracing the Open Government movement in particular, a new generation of leadership will emerge. This leader, like many before her, is highly trained and educated. Yet she carries a tolerance for risk. She sees value in new technology, prizes its acquisition, and remains open to iterating on business processes. She champions innovation within the organization. And, by force of will and repeated wins (and mistakes), she infuses a culture of agile decision-making.
Managers like Jim Keene in Palo Alto and Joni Patillo in Dublin, CA; Finance Directors like Matt Pressey in Salinas and John Adams in Thousand Oaks, CA; and Mayors like Alex Torpey in South Orange, NJ and Mike Kasperzsak in Mountain View, CA demonstrate these qualities. These elected and appointed officials see value in cutting new paths based on better tools. They build trust in their communities and engage other administrators and electeds in the business of politics.
Most startups won’t find these visionaries. Most startups will wither and die for lack of deep pockets. Or they’ll just give up. Selling to bureaucracies often requires creating a network effect, because so few purchasers will go near the bleeding-edge. This is why “red tape” actually excites us with respect to the business we are building at OpenGov.com. The bureaucracy obscures the changes taking place, makes it hard to find the early-adopters and reach critical mass, and ensures that winners take all.
Other important industries promise similar problems and opportunities. In finance, for example, Addepar has battled its way into elite institutions with the promise of improving the financial reporting landscape. Other success stories are emerging in energy, education, healthcare, logistics, and construction.
The right path for a startup-company in an old-line industry is arduous and immensely rewarding. Conventional wisdom says that it’s too hard to build a business in government (or other major industries), and this has kept many from trying. Grand outcomes await for those top young companies bold enough to venture and win.
Joe|June 1, 2013
“If you don’t have a competitive advantage, don’t compete.”
– Jack Welch
How does one build a defensible business? In today’s startup ecosystem, where seed financing is abundant and the barriers to building scalable IT tools are steadily diminishing, the question of how to maintain a competitive advantage is one that must be at the top of every entrepreneur’s mind.Over the past 15 years the consumer space has consolidated around a few massive companies: Google, Facebook, Apple, and Amazon. Even in the face of tens of thousands of would-be competitors with low distribution costs, these players maintain large, loyal user bases and consistently generate billions of dollars in revenue. They accomplish this by strategically controlling the most valuable commodity in the Twenty-First century economy — information.
There is still a massive untapped opportunity to build these kinds of data platforms in the enterprise space. Today, most enterprise IT businesses continue to rely on linear-growth, relationship-driven sales, and payment-for-product monetization models. Owning workflows, not data, is their strategic goal. The largest players in enterprise IT are worth tens of billions of dollars, yet the gap in capabilities between enterprise and consumer software grows wider every year. This is an unsustainable paradigm — one that will be resolved by entrepreneurs who understand and apply the key lessons from the great consumer wins of the last decade.
In The Smart Enterprise Wave, we discussed how an explosion in the size of data sets is overwhelming the antiquated systems of enterprise IT incumbents. New technology solutions are emerging to provide more powerful tools to knowledge workers in major industries like Finance, Healthcare, and Business Services. Owning customer workflows is still essential, but as the core technology behind enterprise and consumer software converges — and as the key value of enterprise software becomes the ability to aggregate and leverage data as opposed to automating basic tasks — a high-touch sales force will become less relevant to business success. In the world of Smart Enterprise, the main imperative for software businesses is to capture information — and thus users — by building defensible product platforms.
Already, we are seeing a preponderance of innovative young companies utilizing data to make enterprises more efficient. We are excited by this development — which we take as an early validation of our thesis — but we caution entrepreneurs breaking new ground to think carefully about whether their product and business strategy is truly defensible. Many companies are building useful tools that will undoubtedly save customers time and money. However, without control over the underlying data these “enterprise apps” are destined for a life that is nasty, brutish, and short. The only way to escape this Hobbesian world is to become the platform that owns the users and the data — the infrastructure upon which the ecosystem is built.
Detailed below are some lessons gained from our partners’ years of experience building technology platforms, as well as some common strategic mistakes entrepreneurs should avoid.
Founder, Palantir and Addepar
Key Aspects of an Enterprise Data Platform
1. Transforming Workflows and High Engagement.
Disruptive technology platforms become an essential part of an individual’s daily workflow and cannot be easily replaced. The product’s benefits must be transformative, not incremental (i.e. minimum of several hours saved per week per user, or gross margin improvement of 15% or greater). The best way to track success here is to closely follow product engagement metrics, such as number of visits per user or average time spent per week using the product. Having this kind of direct connection also enables platforms to turn their users into a valuable asset for third party developers. Traditional enterprise middle-ware and backend solutions don’t have these innate features and should be avoided.
2. Generate and Capture Proprietary Data.
In the Smart Enterprise world, Data Is King. Having proprietary data that is user-generated and owned by the platform is becoming a pre-requisite to building a defensible business. LinkedIn, for instance, encourages users to publish content about themselves that cannot otherwise be found online. This pulls other professionals (and recruiters) onto the platform. The data is also highly structured, giving it valuable second order applications, such as targeted advertising. The most successful platforms will be open to third party development, but in order to be valuable, consumers and businesses must be forced to come to you for data that they cannot get elsewhere.
3. Network Effects and Infrastructure.
The value of the platform (to the end user) should grow with the number of users in a virtuous cycle that encourages adoption virality. Ebay, Microsoft Office, and Visa are all classic examples of this phenomenon. As a general rule, a system that controls the reciprocal exchange of information, goods, or money will tend to gain network effects. The largest and most enduring Smart Enterprise platforms will likely have B2B marketplace and communication/collaboration features at their core. Enterprise applications that leverage data and user engagement to act as infrastructure for a robust third party development ecosystem will also tend towards winner-take-all dynamics, as occurred with the Microsoft Windows operating system.
4. Owning the End Consumer.
The trend of the “consumerization of the enterprise” is not just about designing better user interfaces for employees; it involves engaging consumers directly, since they are a valuable source of data for companies. This phenomenon will become particularly apparent in Healthcare, Finance, and Government. Smart Enterprise software will change the dynamics of enterprise-consumer interaction by bringing the two parties onto the same data platform. For instance, consumers might use an online tool to track their health information and set fitness goals. Physicians could then utilize this data to monitor their patients’ ongoing health, while insurance companies are able to reduce claims by offering premium discounts to consumers who exercise regularly.
5. Building a Brand.
Winner-take-all-players often become brands that perpetuate their market position. This becomes especially relevant once you move outside of the early adopter subset and into the mass market. Professionals (especially in Medicine and Law) often place a high degree of importance on a product solution’s status as “the gold-standard”, or trusted authority. There will undoubtedly be Smart Enterprise content companies that leverage new analytical and data collection techniques (such as using mobile devices to crowd-source consumer good pricing data in the developing world) to build the next-generation of defensible content businesses to compete with existing brands like Nielson and Thompson Reuters.
Common Pitfalls & Signs of an App, Not a Platform
1. Sales vs. Product-Driven Leadership.
Successful technology platforms require a degree of long-term planning and vision that sales organizations are not well suited to deliver. Sales teams can be great at building a market for a new technology, but as customer needs evolve beyond linear software tools and SaaS pricing models shorten commitment periods, product and data strategy will become a critical part of gaining lock-in and winning a space. Box, for instance, is using a sales team to gain ground in enterprises, with the intent of scaling back these efforts once it achieves critical mass in specific industry verticals. LinkedIn also relied on an early sales team to jump-start engagement from recruiters and companies, which increased adoption by professionals and strengthened the data platform.
2. Product Only Ingests Public Data.
The novel acquisition of public data can be an effective way to create an initial value proposition, but it is not defensible over the long term. We have seen many enterprise applications pull in data from social networks like LinkedIn and Twitter and then run analytics on it — “machine learning”, “semantic analysis”, and “natural language processing” are the new buzz words of this current technology wave. This might be a “Big Data” play, but the critical question that must be asked is where is the defensibility? If the product is simply pulling in the same data that anyone else can extract via public APIs, what stops another team from doing the same thing incrementally better?
3. Reliance on Technology Over Strategy.
In today’s competitive environment, technological prowess is a pre-requisite to business success. However, technology is not a defensible attribute over the long-term. Technology (such as a more intelligent data parsing or prediction algorithm) can help create an initial value proposition, but given the current velocity of innovation, the competition is never that far behind. Software patents don’t hurt, but they shouldn’t be considered core to the business model. Ultimately, unless you capture valuable proprietary data or become an irreplaceable part of the customer’s workflow, you have not achieved defensibility. Historically, the most successful companies — like Google or Apple — use technology to create disruptive value but then quickly build a defensible platform and network around it to protect their position (such as app ecosystems reliant on the platform’s proprietary data).
4. Claims That “Data Scale” Is a Platform Effect.
Many companies — especially tools that rely on prediction and recommendation algorithms — will claim that by increasing the volume of data being captured by their application, they will be able to use machine learning to improve product quality and gain an edge over the competition. Scaling data is certainly important, but once you hit the point of statistical significance we believe the advantage tends to wear off and you need other elements of defensibility — like proprietary data or network effects — in order to win a market. The exception may be in networks where a high degree of value is created by outliers (such as in scientific or medical research communities) and it is important to capture the long tail.
General Partner, 8VC
The Smart Enterprise Wave
Joe|January 1, 2013
“One must put themselves in the path of giants.”
– Lillian Cauldwell
Over the last hundred years, five major trends have dominated Silicon Valley (SV): “Electronic Tools,” “Semiconductor,” “Enterprise,” “Telecom,” and “Consumer.” A sixth trend has emerged. We call it “Smart Enterprise.”
The Smart Enterprise wave will disrupt nearly every major sector of the global economy and dramatically improve productivity within those sectors, because it disrupts non-linear decision-making processes that are central to how major industries conduct business and create value. These decision-making processes have been complicated over the last two decades by the vast growth of digital information. In the past five years alone, the amount of data in existence has grown nine-fold, to over 2 trillion gigabytes. This increase has brought a corresponding increase in data complexity, formats, and silos that require sophisticated technology platforms to help knowledge workers process and leverage the information effectively.
Emerging Smart Enterprise platforms represent a significant investment opportunity. Companies can now measure, analyze, and aggregate large data sets to inform mission critical projects. Examples of digital data that were not previously accessible include energy consumption and production data from sensor networks; data on government expenditures and transactions; exposure and transaction data from institutional and private wealth portfolios; genomic IT data and information-related outcome analysis in healthcare; logistics and network distribution information; and personalized education data on student cohorts. At 8VC, our investment thesis involves identifying, building, and investing in the most value-creating platforms, especially in these industries with inherent winner-take-all dynamics.
Old Enterprise to Smart Enterprise
Companies like Oracle and SAP led the first enterprise wave by streamlining back-office processes to make corporations more efficient. Enterprise software helped push paper faster and speed up routine business tasks; think of “TPS reports” from the movie “Office Space”. Large, big-box software solutions streamlined the “assembly line” and brought basic automation to linear processes, such as payroll, accounting, supply chain and inventory management.
Although pioneering in its time, innovation in this space grew stagnant. Existing technology infrastructure is ill-equipped to address the novel information challenges that major industries face, making the process difficult and error-prone to append new features and add-ons. Clunky and dysfunctional user interfaces demand lengthy employee-training sessions and CTOs pay IT consultants large contracts just to create reports from the data. Implementation of the systems themselves can take months and sometimes years. The biggest companies devote much of their resources to sales and marketing efforts rather than serious innovation. Perhaps for these reasons, Y-Combinator once wrote: “If you don’t think you’re smart enough to start a startup doing something technically difficult, just write enterprise software.
In the Smart Enterprise space, companies are re-inventing and replacing the decades old technology infrastructure behind major industries. To accomplish this, engineers are solving hard technology problems involved in integrating disparate data into conceptual structures that knowledge workers can intuitively access and manipulate. Valuable, machine-generated data resides in diverse sources: databases, excel spreadsheets, and other unstructured forms on the internet. By unlocking the patterns in and usability of the data, knowledge workers will solve problems the creators of the software did not even foresee. For example, in the intelligence community, analysts can complete complex tasks — like tracking international moneylaundering schemes or re-building communities in war-torn areas — that require connections across networks informed by structured and unstructured data. The same goes for global bankers, multinational lawyers, retailers with distributed supply chains, medical researchers, and other professionals who face complex challenges.
Defining Smart Enterprise
1. Integrate heterogeneous big data and empower knowledge workers to solve non-linear problems.
2. Leverage recent IT advances — chiefly from the consumer wave — to solve critical challenges in major industries.
3. Potential to harness network effects within industry verticals and become platforms, increasing innovation by enabling novel applications to quickly spread throughout the industry.
Platforms typically begin as useful applications to solve single problems, such as electronic healthcare records, student information services, or energy consumption reduction. As the platforms gain access to increasing amounts of data, the owners of the platform may build additional applications and eventually an open infrastructure to allow third party developers to innovate as well. These vertical platforms can enable application developers to reach tens of thousands of users without having to re-create the application for each medical office, school system, or energy grid.
At present, for instance, if a developer builds a market-changing technology to improve operations at a municipal water facility, the facility owner can apply the product in that locale only. However, developing a standardized platform across water districts will exponentially increase the platform’s value, by simplifying complexity in huge networks of information. For another example, consider the mobile space, where extensive software and hardware ecosystems have grown around the Android and iOS platforms. An iOS developer can build one application and potentially reach over 400 million iOS devices.
This openness pushes new waves of innovation as go-to-market costs plummet for application developers. The owners of the platforms, in turn, capture winner-take-all dynamics. In major old-line industries, where incumbent players feed off of closed infrastructure, market shifts will be especially pronounced for those smart enterprise technology companies able to insert themselves into the technology infrastructure and integrate disparate data sets.
The major enterprise industries, including government, energy, finance, healthcare, and business services, comprise 70% of industry value as a percent of GDP. Each of these industries suffers deep inefficiencies. To see some of these problems and the solutions underway, consider the following examples from companies in the 8VC portfolio and investment pipeline.
Problem: State and local governments spend more than $30 billion on old enterprise software, built for the paper environment. Knowledge workers cannot derive insights, ask relevant questions, or manage information flow. Sometimes they cannot even tell how much money their entity spends. As a result, cities across America find themselves in financial crisis — in California alone, three cities filed for bankruptcy in 2012.
Solution: The platform being built by OpenGov allows governments to visualize critical financial data; analyze the data to flag waste; perform cross-city comparisons and benchmarks to find best practices and new efficiencies; and share financial transactions and budget colloborations to improve transparency and workflows.
Problem: Energy consumption in developed and emerging markets continues to rise while production becomes more challenging. As easily accessible sources are depleted, producers must target more technically complex fields to extract natural resources. For example, more than 50% of original U.S. oil reserves remain down hole leaving 100 billion barrels ($10 trillion) which are not economically recoverable today.
Solution: Equipping production wells with sensors and big data applications could drive 100% improvements in efficiency and extraction. Through novel sensor technology and software, NeoTek optimizes production and reservoir models to overcome the problem of leaving much of the oil within the reservoirs behind. Taxon Biosciences utilizes a proprietary bioinformatics approach to develop and identify microbial species that accelerate the conversion of unconventional energy sources (heavy oil, coal, shale oil.) to natural gas.
Problem: Private wealth management firms spend more than $10 billion to separately create and maintain their financial aggregation, reporting, and analysis infrastructure. Innovation is slow because no single platform exists. Millions of people receive PDFs from funds and manually enter data into their systems from a variety of schemas, causing confusion, enabling fraud, and inhibiting sophisticated analysis. itinfrastructure
Solution: Addepar is building an open platform to help investors access and understand their information. Addepar aggregates disparate sets of data, reconciles and augments that data, and provides best-in-class analysis and reporting for large private banks and registered investment advisors (RIA). For the first time, investors see all their data in real time, allowing them to perform lightning-fast analysis to address concerns while they are manageable and relevant. As an open platform, Addepar will foster an ecosystem of applications and fundamentally change how a large segment of the financial sector sells products and services. For example, a company selling tax deferral insurance products could write an application for the platform to empower an advisor to see exactly which of his clients could save money with its products and how it would work. Rather than being re-written and customized for every RIA or family office, an open platform allows the application to automatically and intelligently manage trillions of dollars of capital.
The IT infrastructure that runs major industries has been kludged together in pieces in vain attempts to keep up with the demands of big data analyses, and is ready to be replaced.
Problem: The healthcare industry remains largely paper-based, and current systems are ill-equipped to handle transformational innovations, such as electronic medical records, cheaper testing solutions, and full genetic sequencing for individuals.
Solution: Innovation from startups like Health Tap, Palantir, and Practice Fusion may save hundreds of billions of dollars, by bringing data and doctor interaction online and then enabling patients and doctors to make informed decisions. Leveraging these interactions and data with insurance companies and healthcare organizations will add massive value per patient and simultaneously lower costs.
Problem: Although many functions, such as sales, recruiting, and business development, have transitioned into the cloud, data analysis still requires manual collection and input. Volume, accuracy, and timeliness of information are compromised in the process, which reduces the value of the information in these digital systems.
Solution: RelateIQ is transforming the CRM space by building a technology solution to collect data automatically from available sources to enable intelligent insights for sales, recruiting, investor management, and other critical business pipelines. With access to communication data within and between organizations, RelateIQ leverages advances in data science to create advanced, real-time collaboration tools to dramatically improve key business functions.
THE IMPORTANCE OF STARTUPS IN SMART ENTERPRISE
Startups will play an important role in the Smart Enterprise wave. This is not obvious to many outside of Silicon Valley. For most of the twentieth century, innovation came from large corporations, including places like Bell Labs, GE, Xerox PARC, and HP. Those companies invested large sums into innovation; because innovators had to build everything from the ground up, large corporations were among the few entities that could afford to outfit competitive technology teams. They also supported cultures that attracted brilliant people. But the financial cost to innovate has decreased in the last few decades, and it continues to fall. Today’s computers and software systems support rapid conceptual iteration orders of magnitude more powerful than those at the disposal of scientists at Bell Labs. In an inter-temporal hackathon competition, a couple of today’s top engineers would run circles around a team of top talent from 20 to 30 years ago, in any area and at a fraction of the cost.
Technology startups maintain other advantages. Incentives in a focused technology culture are better aligned, because the upside for a particular project is shared among the value-creators. Smaller teams can also maintain more nimble development cycles, flexible work environments, and an absence of politicking and bureaucracy. For these reasons, Smart Enterprise startups attract the top talent now. Palantir, for instance, is regularly cited as attracting the top engineers and other companies like Box, Addepar, and RelateIQ attract top engineers as well.
Given this powerful dynamic, many talented young technologists and entrepreneurs sense the startup opportunity and write off large corporations completely. This is misguided. The global marketplace is an evolving ecosystem, with startups and large corporations playing increasingly complementary roles. To address the world’s hard problems and drive progress, startups need corporations and corporations need startups.
Corporations have scale, existing relationships, and distribution advantages in large, established industries such as energy, education, healthcare, logistics, financial services, and government services. Corporations also possess latent knowledge and expertise by virtue of past work on major problems. They may know better than startups the next complex, valuable problems likely to emerge. Accordingly, and notwithstanding that they may have an advantage on the innovation side, many Smart Enterprise startups would do well to partner with large corporations that control the data, the knowledge workers, and the distribution channels. These partnerships will prove especially valuable in industries that require significant upfront capital investments.
Despite crises in governments and other important institutions, we believe that the Smart Enterprise wave will fundamentally disrupt key sectors of the economy and enable a prosperous 21st century. 8VC invests in new technology platforms that transform how critical industries tackle increasingly difficult challenges. These emerging platforms will re-invent the core intelligent infrastructure of major industries and create out-sized economic value for those who use the platforms, create them, and invest in them. As Smart Enterprise platforms become widely deployed over the coming decade, entrepreneurs will shift their effort toward developing applications on these vertical platforms. Platform-enabled application ecosystems will positively impact global productivity and growth.
At its core, innovation is about solving hard problems that matter to the industries that run our world. Startups will solve these problems. But they, and those that support them, will succeed by understanding the industries dominated by established players and, in many cases, by learning how to work well with industry leaders to solve hard problems and scale the solutions. For their part, large corporations must partner well and share the upside with innovative Smart Enterprise companies, or they will fall behind.
We have many reasons to remain hopeful and must work together to solve the world’s biggest problems. More people aspire to entrepreneurship today than at any point in the past century. Costs to innovate have plummeted. Engineers have the opportunity to build and focus their talent and energy on major global problems. Together, we will confront difficult technology challenges to help knowledge workers operate more efficiently within the largest industries. These tasks are worthy of our top entrepreneurial and engineering talent, and it is the role of the top technology funds to support and nourish these emerging ecosystems.
Founder, Palantir and Addepar
Joe|July 1, 2012
“Aut viam inveniam aut faciam.” (I will either find a way, or make one.)
Please see the attached notes on seed-stage or “angel” investing. I hope this document proves a useful guide and perhaps it will help you avoid a mistake, especially those of you in New York where the technology-investing culture is taking off.
As many of you know, my current fund, Formation | 8 Partners, focuses on early-growth investing. This investment mandate offers the best risk-reward, because my connections in LA and New York and those of my partners with Asian Conglomerates add the most value to early-growth stage companies. Yet, because access to growth companies does not happen automatically, my fund also allocates a portion of its investments to early stage companies. These seed and series A rounds create optionality to gain access to some of the best early-growth deals.
Early-growth investing and angel investing present different challenges. At the early growth stage, one can measure the existence of a strong technology culture and see other business metrics before success is priced in. The business fits into its ecosystem, engages customers, and builds distribution channels. An investor may even gain a feel for what margins might look like later. Put simply, growth investing involves real businesses that show early signs of greatness and traction. These are the most sought after companies and it’s a full time job to find access to them, invest in them on good terms, and give them advantages to make sure they win.
At the early stages, companies have overcome some market, technical, and management challenges, yet they still face obstacles in scaling and solidifying the early momentum. Investors do not know, and usually can’t tell, what margins, engagement, and distribution will look like. They may not even know if the product will fit the market. Fortunately, these factors are not dispositive for angel investing.
To see what truly matters, please open the attached document and enjoy. Please also feel free to ask me before investing in a seed deal. I’m happy to give you my feedback and I won’t steal your deal. In fact, such early investing benefits from collaboration and it’s a usually a positive sum game. Although the majority of seed investments underperform, it’s great for the technology ecosystem to have so many people involved. And it can be an exciting and high-return area if done with discipline and with the right deal flow.
Best regards, Joe Lonsdale
Ten Principles for Angel Investing
1. Invest in the very best engineers. At least one of the founders should be a technologist. There should be more than one founder if possible. Founders should have a loyal early team that they have known or worked with before. Design cultures are very helpful, but engineers matter most. All the returns in angel investing come from the top companies and virtually all the top tech companies are built by the best engineers.
2. The team should have a concise, inspiring vision. The vision should be unique and ambitious. A smart person who hears about the company should get excited and want to be a part of it.
3. Salaries should be low and upside for employees should be high. This holds especially for the founders and the first few employees. If possible, see that great engineers are willing to come on board for low salary and high upside after the initial team. In Silicon Valley in 2012, low salaries means 40k to 75k. In New York, it might mean 50k to 85k.
4. Invest in a team that creates a top technology culture. In addition to the points just discussed, a) the company should be based in one place without multiple locations, b) people should work late at night and on weekends much of the time, and c) employees should generally display passion for what they are doing and near-obsession with their mission. Perhaps most important, an engineering culture means that engineers should determine the scope of the problem and the approach to solving it. The product guys should not dominate decisions, nor should the business guys. If the company consists of business guys trying to find an engineer to get something done or, worse, ordering engineers to build something, run.
5. The company should focus on a small, well-defined target market. The company should achieve a creative monopoly somewhere. If the team wants to conquer everything in a big area out of the gate, help them narrow it down. Attacking an underplayed or heretofore ignored niche in a large space allows the business to grow without sacrificing future market opportunity. All great entrepreneurs have an expansive bias, but they also know how to apply discipline to focus.
6. Great companies work on really hard problems. They take top technology cultures and expose them to a known problem in a valuable area of the economy. The idea itself is not the value. “Stealth” companies or those that don’t want to reveal their technology almost never win. Rather, teams that create new technological solutions, produce big insights every day for breakfast, and take on and solve really hard problems are the companies to bet on. Incremental improvements are not sufficient when investing in seed companies, because, by the time they reach market, incumbent technology may have improved enough to eliminate seed company advantages.
7. The best teams collect the best directors and advisors. A good company should attract top people from the industry to help it grow. If the company can’t raise money from key people in the industry, that’s a bad sign. Consider bringing in a friend from the industry to invest along with you as a bar.
8. Good teams honestly revise their timelines. Most teams constantly change their timelines and want to forget what they promised in the past. Almost all startups miss their timelines, but those that are honest and adapt and learn from their misjudgments outperform.
9. Don’t invest in me-too commerce, payments, deal-related companies, and media plays. These areas are over-crowded. Unless you have an unfair advantage on your side and know that you have found one of the very best teams in the world, back off. Similarly, don’t invest in tricks. Tricks may include companies in China giving distribution, special deals with a big pharmaceutical group or mobile phone company, or a patent licensing or litigation scheme. These methods may add advantages at the right time, but a company’s team and its principles are what matters early-on. Finally, always ask, “why is this deal coming to me?”
10. I will either find a way, or make one. Everything else aside, you are betting on their drive / determination to succeed … that is perhaps the most important quality, and as far as we know it’s an art to measure this. If you are the militant type, ask yourself: would I go to war with these guys at my back? Don’t bet on any team, or any fund for that matter, that is not obsessed and determined to prevail.