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October 2022 - Amp it Up by Frank Slootman

This month we cover our third Frank Slootman book, Amp it Up! It covers Slootman’s overall philosophy with a specific focus on achieving significant growth at scale and how companies can push the boundary of their growth potential. Frank only wrote the book because Snowflake’s CMO encouraged him to do so.

Tech Themes

  1. Expanding the TAM. One core idea that Slootman has used across both ServiceNow and Snowflake is the idea of expanding the TAM. By expanding the TAM, you lengthen your growth runway because there are more people who are capable of using your software. Slootman employed this strategy perfectly at ServiceNow. When on the IPO roadshow for the company, analysts at Gartner kept telling potential investors that ServiceNow had a small TAM of only $1.5B. An old short report of ServiceNow by Kerrisdale Capital highlights this confusion: “ The overall ITSM market size is only $1.5 billion, less than one-third of NOW's $4.7 billion market capitalization. Leading technology research firm Gartner estimates that the IT Service Management market opportunity is $1.5 billion, and is growing at a modest 7% per year. Furthermore, Gartner's research predicts that only 50% of IT organizations will move to SaaS by 2015, implying that the total market opportunity for NOW's ITSM business is less than $1 billion. Given emerging competition from other SaaS ITSM service providers, we believe that the company will have a difficult time exceeding 30% market share. At $207m of LTM revenue, NOW appears to already control 10% to 15% of the market. So even if NOW's market share rises to 30%, which we don't see happening until 2014 at the earliest, NOW's ITSM business should be generating less than $600m in revenue with limited additional growth opportunities. The result of the limited market size and increasing competition will be flattening growth over the next few years.” Kerrisdale was clearly incorrect. Market size estimates are now closer to $12-15B. Slootman and the team realized that to complete the full remediation of issues, more people in the organization needed to access ServiceNow’s tools and core ticketing system. They deliberately went function by function (network engineers, sys admins, database admins) and added specific functionality to enhance the user experience of these groups. One of these product enhancements was ServiceNow’s configuration management database or CMDB, which keeps a log of every device and its exact specifications to allow for faster triage of issues. Slootman has taken this approach to Snowflake, which started out by focusing on just the data warehousing workload but has since expanded into seven unique workloads: data warehouse, data engineering, data science, collaboration, data sharing, unistore, and cybersecurity. These workloads now bring in more people to the Snowflake platform: database administrators, data engineers, analytics engineers, data analysts, data scientists, and cybersecurity analysts. Each new set of tools added, enhances the overall value of the platform and the stickiness of the solution within the organization. This is a great roadmap for how to keep growth elevated in horizontal markets.

  2. Strategy vs. Execution. “Culture eats strategy for breakfast.” Peter Drucker, a famous consultant, and author of the Concept of the Corporation, believed that culture was far more important than strategy. Slootman agrees and even takes it one step further: “Execution has to be your number one goal. Strategy can’t be mastered until you can execute. Great execution is rarer than great strategy.” Slootman actually disagrees with Drucker on the management by objectives framework, “Another source of misalignment is management by objectives, which I have eliminated at every company i’ve joined in the last twenty years. MBOs cause employees to act as if they are running their own show, because they get compensated on their personal metrics, it is next to impossible to pull them off projects. They will be negotiating with you for relief. That is not alignment, that is every man for himself. If you need MBOs to get people to do their jobs, you may have the wrong people, the wrong managers, or both.” In Slootman’s eyes, management by objectives, which sets objectives for an entire organization that are translated into individual goals, ends up being abused by managers. Managers may rely on the objectives solely, and discount the leadership and creative thought necessary to succeed beyond an objective. “A person can do an excellent job according to objective measurement standards, but can fail miserably as a partner, subordinate, superior, or colleague. It is common for people not to be promoted for personal reasons than because of technical inadequacies.” For Slootman, superior execution comes from good judgment, and good judgment comes from bad judgment. Bad judgment is only made clear through experience, which can be the best teacher in his eyes. “New managers have to learn from and through their management chain. Organizations cannot scale and mature around inexperienced management staff.” At Data Domain, Slootman’s team finally started seeing success when they found the right leader for their contract manufacturing organization; at ServiceNow, when they found the right leader for cloud infrastructure; at Snowflake, when they found the right leader for scaling. “The organization needs innovation and discipline, or else the place will simply implode on itself. The common mistake is to rely on our innovators for discipline.” 5 dysfunctions of a team. Why execution is harder than strategy. But need to Prepare your next strategy early so you are ready when you get there.

  3. Recruiting Talent. Slootman urges leaders to recruit drivers, not passengers. “Passengers are people who don't mind simply being carried along by the company's momentum, offering little or no input, seemingly not caring much about the direction chosen by management. They are often pleasant, get along with everyone, attend meetings promptly, and generally do not stand out as troublemakers. They are often accepted into the fabric of the organization and stay there for many years. The problem is that while passengers can often diagnose and articulate a problem quite well, they have no investment in solving it. They don't do the heavy lifting. Drivers, on the other hand, get their satisfaction from making things happen, not blending in with the furniture. They feel a strong sense of ownership for their projects and teams and demand high standards from both themselves and others. They exude energy, urgency, ambition, even boldness. Faced with a challenge, they usually say, ‘Why not’ rather than ‘That’s impossible.’ These qualities make drivers massively valuable. Finding, recruiting, rewarding, and retaining them should be among your top priorities.” What I find most interesting about this philosophy is that most jobs train people to be passengers. Most CEOs prefer the calm and non-trouble making attitude of passengers over the outspokenness and aggression that sometimes comes with drivers. So what do you do when you find passengers? Its simple - get them off the bus. Although it can be intense, you need to execute by removing people first, getting the right people in, and then getting the right people in the right spots. We talked about this analogy in the Jim Collins book Good to Great. “At a struggling company, you need to change things fast by switching out people whose skills no longer fit the mission or never really did in the first place. The other advantage of moving fast is that everyone who stays on the bus will know that you are dead serious about high standards. The good ones will be energized by those standards.” The challenge with moving quickly is finding the right balance for what the organization can absorb at any given time. Moving too quickly when the organization is not ready, or moving too quickly when the plan hasn’t been set can lead to drastic consequences.

Business Themes

5 dysfunctions of a team.png
  1. Turnarounds as a Training Ground. Famous football coach Bill Walsh joined the San Francisco 49ers after they were the last placed team in the NFL with a 2-14 record. The next season, Walsh’s first, the 49ers repeated the performance - 2-14 again. Walsh at one point broke down on a flight home from a crushing defeat against Miami. 16 months later, he was Super Bowl champion. Turnarounds provide an unbelievably difficult training ground for young executives. It is sink or swim, it is kill or be killed. As discussed in our last book, Bill McDermott took over the struggling SAP North America division before righting the ship and accelerating SAP to growth. Frank Slootman began his managerial career in similar situations. After stints at Burroughs Corporation in corporate planning and Comshare in product management, Frank joined Compuware as head of non-mainframe Product Management. While there, Compuware acquired the dutch company, uniface, as we touched on in the Tape Sucks book. “I jumped at the opportunity return to Amsterdam to take on the entire operation, which seemed in disarray. Colleagues warned me not to go because the place could not be saved, and they worried I’d go down with the ship. Compuware had bought uniface toward the end of its viable product software. But by now, my career had been about taking on what seemed like long odds, jobs nobody else would touch with a 10 foot pole. It was the only avenue open to me anyway and it didn’t matter how hairy these deals were. As a young person, you easily overestimate your capabilities, this is when I started learning what happens when you step into the wrong elevators. We did manage to stabilize uniface. That became a formative career experience in my mid-30s. I’d never had multiple numerous large, mission-critical customers before and hundreds of employees in my charge. I also started to develop an eye for talent which became a cornerstone of my management focus going forward.” Next, Slootman jumped to Ecosystems, a Compuware subsidiary based in silicon valley. He stabilized the struggling company, but they kept losing talent because mid-western Compuware wasn’t able to retain silicon valley employees. He then joined Borland as SVP of product operations, which had also fallen on hard times. They resurrected the brand and the business. Even by 40 years old, he was taking on problem children, and he kept getting offered CEO jobs at companies that were elevators to nowhere. Slootman interviewed over and over for CEO roles but was passed on because “you’ve never run sales.” He later commented on being passed over: “I led from the front and sold shoulder to shoulder with sales. These rejections left me with an unfavorable opinion of many venture capitalists who couldn’t recognize talent if it smacked them in the face.” Turnarounds, especially those inside big companies offer management challenges that most people don’t get to experience until its too late. For Slootman and McDermott, these were the right opportunities for their personalities and approaches at the right time of their career.

  2. Frank doesn’t believe in a Customer Success department. At Snowflake, there is no customer success department. In Slootman’s eyes: “They were happy to follow the trend set up by other companies like ours. But not me. I pulled the plug on these customer success departments in both companies, reassigning the staff back to the departments where their expertise fit best. Here’s why I was so opposed - if you have a customer success department that gives everyone else an incentive to stop worrying about how well our customers are thriving with our products and services. That sets up a disconnect that can create major problems down the road. People can become more focused on hitting the narrow goals of their silo rather than the broader and more important goal of customer satisfaction, which ultimately drives customer retention, word of mouth, profitability, and the long-term survival of the whole company. For instance, at ServiceNow, some of the customer success people grew quite dominant in the interaction with the customer and coordinated all the resources of the company for the customer’s benefit, including technical support, professional services, and even engineering. This had the effect that other departments sat back, became more passive, and felt less ownership of customer success. Customer success is the business of the entire company, not merely one department.” While this approach may work for Snowflake, it is not the norm in the SaaS world. In fact, there are entire companies like Gainsight, Totango, and ChurnZero, that help companies accelerate their Customer Success motion. Openview Venture Partners views customer success as critical for an effective product-led growth sales motion. Sales and Customer Success are important ways of generating product feedback from customers, but organizations need to make sure not to overwhelm product and engineering priorities. Often product teams don’t invest enough time in understanding the sales organization and the sales team views the product team as simply delivering on features to close deals. Leadership is necessary to help set priorities and collaboration across these departments.

  3. 5 steps to Amp it Up. Slootman outlines a five-step process for business leaders to accelerate growth and transform their organizations. The first step is to raise your standards and set ambitious goals for your company. This is followed by aligning your people and culture to support your vision, which requires careful attention to hiring, training, and communication. The third step is to sharpen your focus and prioritize the most critical areas of your business for growth. Once you have a clear focus, the fourth step is to pick up the pace and execute with speed and urgency. Finally, the fifth step is to transform your strategy by continually adapting to changes in the market and taking bold actions to stay ahead of the competition. By following these five steps, Slootman believes that business leaders can create a culture of high performance and achieve extraordinary results. Underpinning everything, is a culture of trust. Ultimately high performance cultures can be challenging and Slootman had times where former founders like Fred Luddy disagreed with his decisions. But as Slootman puts it: “In the long run, success trumps popularity. In my early days at several companies, founders openly regretted my hiring and openly complained to the board behind my back. But when companies succeed massively, as all of our companies have, founders will eventually get over it. Yes, its nice if they love you, but you can’t let yourself get rattled if they don’t. Your mission is to win, not to achieve popularity.”

Dig Deeper

  • Original Amp It Up Blog Post from 2018

  • Snowflake CEO Frank Slootman: taking ownership, increasing velocity & cultivating talent

  • The CEO Behind Software's Biggest IPO Ever | Forbes

  • Frank Slootman Is a Malcontent—That’s How He Likes It

  • The ServiceNow Story by Fred Luddy and Doug Leone

  • Knowledge12 Report: The world according to Frank Slootman

tags: Frank Slootman, Snowflake, ServiceNow, Data Domain, Sequoia, Borland, Burroughs, Compushare, ITSM, Peter Drucker, MBO, Jim Collins, Bill Walsh, Bill McDermott, SAP, Openview, Gainsight
categories: Non-Fiction
 

February 2021 - Rise of the Data Cloud by Frank Slootman and Steve Hamm

This month we read a new book by the CEO of Snowflake and author of our November 2020 book, Tape Sucks. The book covers Snowflake’s founding, products, strategy, industry specific solutions and partnerships. Although the content is somewhat interesting, it reads more like a marketing book than an actually useful guide to cloud data warehousing. Nonetheless, its a solid quick read on the state of the data infrastructure ecosystem.

Tech Themes

  1. The Data Warehouse. A data warehouse is a type of database that is optimized for analytics. These optimizations mainly revolve around complex query performance, the ability to handle multiple data types, the ability to integrate data from different applications, and the ability to run fast queries across large data sets. In contrast to a normal database (like Postgres), a data warehouse is purpose-built for efficient retrieval of large data sets and not high performance read/write transactions like a typical relational database. The industry began in the late 1970s and early 80’s, driven by work done by the “Father of Data Warehousing” Bill Inmon and early competitor Ralph Kimball, who was a former Xerox PARC designer. In 1986, Kimball launched Redbrick Systems and Inmon launched Prism Solutions in 1991, with its leading product the Prism Warehouse Manager. Prism went public in 1995 and was acquired by Ardent Software in 1998 for $42M while Red Brick was acquired by Informix for ~$35M in 1998. In the background, a company called Teradata, which was formed in the late 1970s by researchers at Cal and employees from Citibank, was going through their own journey to the data warehouse. Teradata would IPO in 1987, get acquired by NCR in 1991; NCR itself would get acquired by AT&T in 1991; NCR would then spin out of AT&T in 1997, and Teradata would spin out of NCR through IPO in 2007. What a whirlwind of corporate acquisitions! Around that time, other new data warehouses were popping up on the scene including Netezza (launched in 1999) and Vertica (2005). Netezza, Vertica, and Teradata were great solutions but they were physical hardware that ran a highly efficient data warehouse on-premise. The issue was, as data began to grow on the hardware, it became really difficult to add more hardware boxes and to know how to manage queries optimally across the disparate hardware. Snowflake wanted to leverage the unlimited storage and computing power of the cloud to allow for infinitely scalable data warehouses. This was an absolute game-changer as early customer Accordant Media described, “In the first five minutes, I was sold. Cloud-based. Storage separate from compute. Virtual warehouses that can go up and down. I said, ‘That’s what we want!’”

  2. Storage + Compute. Snowflake was launched in 2012 by Benoit Dageville (Oracle), Thierry Cruanes (Oracle) and Marcin Żukowski (Vectorwise). Mike Speiser and Sutter Hill Ventures provided the initial capital to fund the formation of the company. After numerous whiteboarding sessions, the technical founders decided to try something crazy, separating data storage from compute (processing power). This allowed Snowflake’s product to scale the storage (i.e. add more boxes) and put tons of computing power behind very complex queries. What may have been limited by Vertica hardware, was now possible with Snowflake. At this point, the cloud had only been around for about 5 years and unlike today, there were only a few services offered by the main providers. The team took a huge risk to 1) bet on the long-term success of the public cloud providers and 2) try something that had never successfully been accomplished before. When they got it to work, it felt like magic. “One of the early customers was using a $20 million system to do behavioral analysis of online advertising results. Typically, one big analytics job would take about thirty days to complete. When they tried the same job on an early version of Snowflake;’s data warehouse, it took just six minutes. After Mike learned about this, he said to himself: ‘Holy shit, we need to hire a lot of sales people. This product will sell itself.’” This idea was so crazy that not even Amazon (where Snowflake runs) thought of unbundling storage and compute when they built their cloud-native data warehouse, Redshift, in 2013. Funny enough, Amazon also sought to attract people away from Oracle, hence the name Red-Shift. It would take Amazon almost seven years to re-design their data warehouse to separate storage and compute in Redshift RA3 which launched in 2019. On top of these functional benefits, there is a massive gap in the cost of storage and the cost of compute and separating the two made Snowflake a significantly more cost-competitive solution than traditional hardware systems.

  3. The Battle for Data Pipelines. A typical data pipeline (shown below) consists of pulling data from many sources, perform ETL/ELT (extract, load, transform and vice versa), centralizing it in a data warehouse or data lake, and connecting that data to visualization tools like Tableau or Looker. All parts of this data stack are facing intense competition. On the ETL/ELT side, you have companies like Fivetran and Matillion and on the data warehouse/data lake side you have Snowflake and Databricks. Fivetran focuses on the extract and load portion of ETL, providing a data integration tool that allows you to connect to all of your operational systems (salesforce, zendesk, workday, etc.) and pull them all together in Snowflake for comprehensive analysis. Matillion is similar, except it connects to your systems and imports raw data into Snowflake, and then transforms it (checking for NULL’s, ensuring matching records, removing blanks) in your Snowflake data warehouse. Matillion thus focuses on the load and transform steps in ETL while Fivetran focuses on the extract and load portions and leverages dbt (data build tool) to do transformations. The data warehouse vs. data lake debate is a complex and highly technical discussion but it mainly comes down to Databricks vs. Snowflake. Databricks is primarily a Machine Learning platform that allows you to run Apache Spark (an open-source ML framework) at scale. Databricks’s main product, Delta Lake allows you to store all data types - structured and unstructured for real-time and complex analytical processes. As Datagrom points out here, the platforms come down to three differences: data structure, data ownership, and use case versatility. Snowflake requires structured or semi-structured data prior to running a query while Databricks does not. Similarly, while Snowflake decouples data storage from compute, it does not decouple data ownership meaning Snowflake maintains all of your data, whereas you can run Databricks on top of any data source you have whether it be on-premise or in the cloud. Lastly, Databricks acts more as a processing layer (able to function in code like python as well as SQL) while Snowflake acts as a query and storage layer (mainly driven by SQL). Snowflake performs best with business intelligence querying while Databricks performs best with data science and machine learning. Both platforms can be used by the same organizations and I expect both to be massive companies (Databricks recently raised at a $28B valuation!). All of these tools are blending together and competing against each other - Databricks just launched a new LakeHouse (Data lake + data warehouse - I know the name is hilarious) and Snowflake is leaning heavily into its data lake. We will see who wins!

An interesting data platform battle is brewing that will play out over the next 5-10 years: The Data Warehouse vs the Data Lakehouse, and the race to create the data cloud

Who's the biggest threat to @snowflake? I think it's @databricks, not AWS Redshifthttps://t.co/R2b77XPXB7

— Jamin Ball (@jaminball) January 26, 2021

Business Themes

Lakehouse_v1.png
architecture-overview.png
  1. Marketing Customers. This book at its core, is a marketing document. Sure, it gives a nice story of how the company was built, the insights of its founding team, and some obstacles they overcame. But the majority of the book is just a “Imagine what you could do with data” exploration across a variety of industries and use cases. Its not good or bad, but its an interesting way of marketing - that’s for sure. Its annoying they spent so little on the technology and actual company building. Our May 2019 book, The Everything Store, about Jeff Bezos and Amazon was perfect because it covered all of the decision making and challenging moments to build a long-term company. This book just talks about customer and partner use cases over and over. Slootman’s section is only about 20 pages and five of them cover case studies from Square, Walmart, Capital One, Fair, and Blackboard. I suspect it may be due to the controversial ousting of their long-time CEO Bob Muglia for Frank Slootman, co-author of this book. As this Forbes article noted: “Just one problem: No one told Muglia until the day the company announced the coup. Speaking publicly about his departure for the first time, Muglia tells Forbes that it took him months to get over the shock.” One day we will hear the actual unfiltered story of Snowflake and it will make for an interesting comparison to this book.

  2. Timing & Building. We often forget how important timing is in startups. Being the right investor or company at the right time can do a lot to drive unbelievable returns. Consider Don Valentine at Sequoia in the early 1970’s. We know that venture capital fund performance persists, in part due to incredible branding at firms like Sequoia that has built up over years and years (obviously reinforced by top-notch talents like Mike Moritz and Doug Leone). Don is a great investor and took significant risks on unproven individuals like Steve Jobs (Apple), Nolan Bushnell (Atari), and Trip Hawkins (EA). But he also had unfettered access to the birth of an entirely new ecosystem and knowledge of how that ecosystem would change business, built up from his years at Fairchild Semiconductor. Don is a unique person and capitalized on that incredible knowledgebase, veritably creating the VC industry. Sequoia is a top firm because he was in the right place at the right time with the right knowledge. Now let’s cover some companies that weren’t: Cloudera, Hortonworks, and MapR. In 2005, Yahoo engineers Doug Cutting and Mike Cafarella, inspired by the Google File System paper, created Hadoop, a distributed file system for storing and accessing data like never before. Hadoop spawned many companies like Cloudera, Hortonworks, and MapR that were built to commercialize the open-source Hadoop project. All of the companies came out of the gate fast with big funding - Cloudera raised $1B at a $4B valuation prior to its 2017 IPO, Hortonworks raised $260M at a $1B valuation prior to its 2014 IPO, and MapR $300M before it was acquired by HPE in 2019. The companies all had one thing in problem however, they were on-premise and built prior to the cloud gaining traction. That meant it required significant internal expertise and resources to run Cloudera, Hortonworks, and MapR software. In 2018, Cloudera and Hortonworks merged (at a $5B valuation) because the competitive pressure from the cloud was eroding both of their businesses. MapR was quietly acquired for less than it raised. Today Cloudera trades at a $5B valuation meaning no shareholder return since the merger and the business is only recently slightly profitable at its current low growth rate. This cautionary case study shows how important timing is and how difficult it is to build a lasting company in the data infrastructure world. As the new analytics stack is built with Fivetran, Matillion, dbt, Snowflake, and Databricks, it will be interesting to see which companies exist 10 years from now. Its probable that some new technology will come along and hurt every company in the stack, but for now the coast is clear - the scariest time for any of these companies.

  3. Burn Baby Burn. Snowflake burns A LOT of money. In the Nine months ended October 31, 2020, Snowflake burned $343M, including $169M in their third quarter alone. Why would Snowflake burn so much money? Because they are growing efficiently! What does efficient growth mean? As we discussed in the last Frank Slootman book - sales and marketing efficiency is a key hallmark to understand the quality of growth a company is experiencing. According to their filings, Snowflake added ~$230M of revenue and spent $325M in sales and marketing. This is actually not terribly efficient - it supposes a dollar invested in sales and marketing yielded $0.70 of incremental revenue. While you would like this number to be closer to 1x (i.e. $1 in S&M yield $1 in revenue - hence a repeatable go-to-market motion), it is not terrible. ServiceNow (Slootman’s old company), actually operates less efficiently - for every dollar it invests in sales and marketing, it generates only $0.55 of subscription revenue. Crowdstrike, on the other hand, operates a partner-driven go-to-market, which enables it to generate more while spending less - created $0.90 for every dollar invested in sales and marketing over the last nine months. However, there is a key thing that distinguishes the data warehouse compared to these other companies and Ben Thompson at Stratechery nails it here: “Think about this in the context of Snowflake’s business: the entire concept of a data warehouse is that it contains nearly all of a company’s data, which (1) it has to be sold to the highest levels of the company, because you will only get the full benefit if everyone in the company is contributing their data and (2) once the data is in the data warehouse it will be exceptionally difficult and expensive to move it somewhere else. Both of these suggest that Snowflake should spend more on sales and marketing, not less. Selling to the executive suite is inherently more expensive than a bottoms-up approach. Data warehouses have inherently large lifetime values given the fact that the data, once imported, isn’t going anywhere.” I hope Snowflake burns more money in the future, and builds a sustainable long-term business.

Dig Deeper

  • Early Youtube Videos Describing Snowflake’s Architecture and Re-inventing the Data Warehouse

  • NCR’s spinoff of Teradata in 2007

  • Fraser Harris of Fivetran and Tristan Handy of dbt speak at the Modern Data Stack Conference

  • Don Valentine, Sequoia Capital: "Target Big Markets" - A discussion at Stanford

  • The Mike Speiser Incubation Playbook (an essay by Kevin Kwok)

tags: Snowflake, Data Warehouse, Oracle, Vertica, Netezza, IBM, Databricks, Apache Spark, Open Source, Fivetran, Matillion, dbt, Data Lake, Sequoia, ServiceNow, Crowdstrike, Cloudera, Hortonworks, MapR, BigQuery, Frank Slootman, Teradata, Xerox, Informix, NCR, AT&T, Benoit Dageville, Mike Speiser, Sutter Hill Ventures, Redshift, Amazon, ETL, Hadoop, SQL
categories: Non-Fiction
 

November 2020 - Tape Sucks: Inside Data Domain, A Silicon Valley Growth Story by Frank Slootman

This month we read a short, under-discussed book by current Snowflake and former ServiceNow and Data Domain CEO, Frank Slootman. The book is just like Frank - direct and unafraid. Frank has had success several times in the startup world and the story of Data Domain provides a great case study of entrepreneurship. Data Domain was a data deduplication company, offering a 20:1 reduction of data backed up to tape casettes by using new disk drive technology.

Tech Themes

Data Domain’s 2008 10-K prior to being acquired

Data Domain’s 2008 10-K prior to being acquired

  1. First time CEO at a Company with No Revenue. Frank is an immigrant to the US, coming from the Netherlands shortly after graduating from the University of Rotterdam. After being rejected by IBM 10+ times, he joined Burroughs corporation, an early mainframe provider which subsequently merged with its direct competitor Sperry for $4.8B in 1986. Frank then spent some time at Compuware and moved back to the Netherlands to help it integrate the acquisition of Uniface, an early customizable report building software. After spending time there, he went to Borland software in 1997, working his way up the product management ranks but all the while being angered by time spent lobbying internally, rather than building. Frank joined Data Domain in the Spring of 2003 - when it had no customers, no revenue, and was burning cash. The initial team and VC’s were impressive - Kai Li, a computer science professor on sabbatical from Princeton, Ben Zhu, an EIR at USVP, and Brian Biles, a product leader with experience at VA Linux and Sun Microsystems. The company was financed by top-tier VC’s New Enterprise Associates and Greylock Partners, with Aneel Bhusri (Founder and current CEO of Workday) serving as initial CEO and then board chairman. This was a stacked team and Slootman knew it: “I’d bring down the average IQ of the company by joining, which felt right to me.” The Company had been around for 18 months and already burned through a significant amount of money when Frank joined. He knew he needed to raise money relatively soon after joining and put the Company’s chances bluntly: “Would this idea really come together and captivate customers? Nobody knew. We, the people on the ground floor, were perhaps, the most surprised by the extraordinary success we enjoyed.”

  2. Playing to his Strengths: Capital Efficiency. One of the big takeaways from the Innovators by Walter Issacson was that individuals or teams at the nexus of disciplines - primarily where the sciences meet the humanities, often achieved breakthrough success. The classic case study for this is Apple - Steve Jobs had an intense love of art, music, and design and Steve Wozniak was an amazing technologist. Frank has cultivated a cross-discipline strength at the intersection of Sales and Technology. This might be driven by Slootman’s background is in economics. The book has several references to economic terms, which clearly have had an impact on Frank’s thinking. Data Domain espoused capital efficiency: “We traveled alone, made few many-legged sales calls, and booked cheap flights and hotels: everybody tried to save a dime for the company.” The results showed - the business went from $800K of revenue in 2004 to $275 million by 2008, generating $75M in cash flow from operations. Frank’s capital efficiency was interesting and broke from traditional thinking - most people think to raise a round and build something. Frank took a different approach: “When you are not yet generating revenue, conservation of resource is the dominant theme.” Over time, “when your sales activity is solidly paying for itself,” the spending should shift from conservative to aggressive (like Snowflake is doing this now). The concept of sales efficiency is somewhat talked about, but given the recent fundraising environment, is often dismissed. Sales efficiency can be thought of as: “How much revenue do I generate for every $1 spent in sales and marketing?” Looking at the P&L below, we see Data Domain was highly efficient in its sales and marketing activity - the company increased revenue $150M in 2008, despite spending $115M in sales and marketing (a ratio of 1.3x). Contrast this with a company like Slack which spent $403M to acquire $230M of new revenue (a ratio of 0.6x). It gets harder to acquire customers at scale, so this efficiency is supposed to come down over time but best in class is hopefully above 1x. Frank clearly understands when to step on the gas with investing, as both ServiceNow and Snowflake have remained fairly efficient (from a sales perspective at least) while growing to a significant scale.

  3. Technology for Technology’s Sake. “Many technologies are conceived without a clear, precise notion of the intended use.” Slootman hits on a key point and one that the tech industry has struggled to grasp throughout its history. So many products and companies are established around budding technology with no use case. We’ve discussed Magic Leap’s fundraising money-pit (still might find its way), and Iridium Communications, the massive satellite telephone that required people to carry a suitcase around to use it. Gartner, the leading IT research publication (which is heavily influenced by marketing spend from companies) established the Technology Hype Cycle, complete with the “Peak of inflated expectations,” and the “Trough of Disillusionment” for categorizing technologies that fail to live up to their promise. There have been several waves that have come and gone: AR/VR, Blockchain, and most recently, Serverless. Its not so much that these technologies were wrong or not useful, its rather that they were initially described as a panacea to several or all known technology hindrances and few technologies ever live up to that hype. Its common that new innovations spur tons of development but also lots of failure, and this is Slootman’s caution to entrepreneurs. Data Domain was attacking a problem that existed already (tape storage) and the company provided what Clayton Christensen would call a sustaining innovation (something that Slootman points out). Whenever things go into “winter state”, like the internet after the dot-com bubble, or the recent Crpyto Winter which is unthawing as I write; it is time to pay attention and understand the relevance of the innovation.

Business Themes

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  1. Importance of Owning Sales. Slootman spends a considerable amount of this small book discussing sales tactics and decision making, particularly with respect to direct sales and OEM relationships. OEM deals are partnerships with other companies whereby one company will re-sell the software, hardware, or service of another company. Crowdstrike is a popular product with many OEM relationships. The Company drives a significant amount of its sales through its partner model, who re-sell on behalf of Crowdstrike. OEM partnerships with big companies present many challenges: “First of all, you get divorced from your customer because the OEM is now between you and them, making customer intimacy challenging. Plus, as the OEM becomes a large part of your business, for all intents and purposes they basically own you without paying for the privilege…Never forget that nobody wants to sell your product more than you do.” The challenges don’t end there. Slootman points out that EMC discarded their previous OEM vendor in the data deduplication space, right after acquiring Data Domain. On top of that, the typical reseller relationship happens at a 10-20% margin, degrading gross margins and hurting ability to invest. It is somewhat similar to the challenges open-source companies like MongoDB and Elastic have run into with their core software being…free. Amazon can just OEM their offering and cut them out as a partner, something they do frequently. Partner models can be sustainable, but the give and take from the big company is a tough balance to strike. Investors like organic adoption, especially recently with the rise of freemium SaaS models percolating in startups. Slootman’s point is that at some point in enterprise focused businesses, the Company must own direct sales (and relationships) with its customers to drive real efficiency. After the low cost to acquire freemium adopters buy the product, the executive team must pivot to traditional top down enterprise sales to drive a successful and enduring relationship with the customer.

  2. In the Thick of Things. Slootman has some very concise advice for CEOs: be a fighter, show some humanity, and check your ego at the door. “Running a startup reduces you to your most elementary instincts, and survival is on your mind most of the time…The CEO is the ‘Chief Combatant,’ warrior number one.” Slootman views the role of CEO as a fighter, ready to be the first to jump into the action, at all times. And this can be incredibly productive for business as well. Tony Xu, the founder and CEO of Doordash, takes time out every month to do delivery for his own company, in order to remain close to the customer and the problems of the company. Jeff Bezos famously still responds and views emails from customers at jeff@amazon.com. Being CEO also requires a willingness to put yourself out there and show your true personality. As Slootman puts it: “People can instantly finger a phony. Let them know who you really are, warts and all.” As CEO you are tasked with managing so many people and being involved in all aspects of the business, it is easy to become rigid and unemotional in everyday interactions. Harvard Business School professor and former leader at Uber distills it down to a simple phrase: “Begin With Trust.” All CEO’s have some amount of ego, driving them to want to be at the top of their organization. Slootman encourages CEO’s to be introspective, and try to recognize blind spots, so ego doesn’t drive day-to-day interactions with employees. One way to do that is simple: use the pronoun “we” when discussing the company you are leading. Though Slootman doesn’t explicitly call it out - all of these suggestions (fighting, showing empathy, getting rid of ego) are meant to build trust with employees.

  3. R-E-C-I-P-E for a Great Culture. The last fifth of the book is all focused on building culture at companies. It is the only topic Slootman stays on for more than a few chapters, so you know its important! RECIPE was an acronym created by the employees at Data Domain to describe the company’s values: Respect, Excellence, Customer, Integrity, Performance, Execution. Its interesting how simple and focused these values are. Technology has pushed its cultural delusion’s of grandeur to an extreme in recent years. The WeWork S-1 hilariously started with: “We are a community company committed to maximum global impact. Our mission is to elevate the world’s consciousness.” But none of Data Domain’s values were about changing the world to be a better place - they were about doing excellent, honest work for customers. Slootman is lasered focused on culture, and specifically views culture as an asset - calling it: “The only enduring, sustainable form of differentiation. These days, we don’t have a monopoly for very long on talent, technology, capital, or any other asset; the one thing that is unique to us is how we choose to come together as a group of people, day in and day out. How many organizations are there that make more than a halfhearted attempt at this?” Technology companies have taken different routes in establishing culture: Google and Facebook have tried to create culture by showering employees with unbelievable benefits, Netflix has focused on pure execution and transparency, and Microsoft has re-vamped its culture by adopting a Growth Mindset (has it really though?). Google originally promoted “Don’t be evil,” as part of its Code of Conduct but dropped the motto in 2018. Employees want to work for mission-driven organizations, but not all companies are really changing the world with their products, and Frank did not try to sugarcoat Data Domain’s data-duplication technology as a way to “elevate the world’s consciousness.” He created a culture driven by performance and execution - providing a useful product to businesses that needed it. The culture was so revered that post-acquisition, EMC instituted Data Domain’s performance management system. Data Domain employees were looked at strangely by longtime EMC executives, who had spent years in a big and stale company. Culture is a hard thing to replicate and a hard thing to change as we saw with the Innovator’s Dilemma. Might as well use it to help the company succeed!

Dig Deeper

  • How Data Domain Evolved in the Cloud World

  • Former Data Domain CEO Frank Slootman Gets His Old Band Back Together at ServiceNow

  • The Contentious Take-over Battle for Data Domain: Netapp vs. EMC

  • 2009 Interview with Frank Slootman After the Acquisition of Data Domain

tags: Snowflake, DoorDash, ServiceNow, WeWork, Data Domain, EMC, Netapp, Frank Slootman, Borland, IBM, Burroughs, Sperry, NEA, Greylock, Workday, Aneel Bhusri, Sun Microsystems, USVP, Uber, Netflix, Facebook, Google, Microsoft, Amazon, Jeff Bezos, Tony Xu, MongoDB, Elastic, Crowdstrike, Crypto, Gartner, Hype Cycle, Slack, Apple, Steve Jobs, Steve Wozniak, Magic Leap, batch2
categories: Non-Fiction
 

October 2020 - Working in Public: The Making and Maintenance of Open Source Software by Nadia Eghbal

This month we covered Nadia Eghbal’s instant classic about open-source software. Open-source software has been around since the late seventies but only recently it has gained significant public and business attention.

Tech Themes

The four types of open source communities described in Working in Public

The four types of open source communities described in Working in Public

  1. Misunderstood Communities. Open source is frequently viewed as an overwhelmingly positive force for good - taking software and making it free for everyone to use. Many think of open source as community-driven, where everyone participates and contributes to making the software better. The theory is that so many eyeballs and contributors to the software improves security, improves reliability, and increases distribution. In reality, open-source communities take the shape of the “90-9-1” rule and act more like social media than you could think. According to Wikipedia, the "90–9–1” rule states that for websites where users can both create and edit content, 1% of people create content, 9% edit or modify that content, and 90% view the content without contributing. To show how this applies to open source communities, Eghbal cites a study by North Carolina State Researchers: “One study found that in more than 85% of open source projects the research examined on Github, less than 5% of developers were responsible for 95% of code and social interactions.” These creators, contributors, and maintainers are developer influencers: “Each of these developers commands a large audience of people who follow them personally; they have the attention of thousands of developers.” Unlike Instagram and Twitch influencers, who often actively try to build their audiences, open-source developer influencers sometimes find the attention off-putting - they simply published something to help others and suddenly found themselves with actual influence. The challenging truth of open source is that core contributors and maintainers give significant amounts of their time and attention to their communities - often spending hours at a time responding to pull requests (requests for changes / new features) on Github. Evan Czaplicki’s insightful talk entitled “The Hard Parts of Open Source,” speaks to this challenging dynamic. Evan created the open-source project, Elm, a functional programming language that compiles Javascript, because he wanted to make functional programming more accessible to developers. As one of its core maintainers, he has repeatedly been hit with requests of “Why don’t you just…” from non-contributing developers angrily asking why a feature wasn’t included in the latest release. As fastlane creator, Felix Krause put it, “The bigger your project becomes, the harder it is to keep the innovation you had in the beginning of your project. Suddenly you have to consider hundreds of different use cases…Once you pass a few thousand active users, you’ll notice that helping your users takes more time than actually working on your project. People submit all kinds of issues, most of them aren’t actually issues, but feature requests or questions.” When you use open-source software, remember who is contributing and maintaining it - and the days and years poured into the project for the sole goal of increasing its utility for the masses.

  2. Git it? Git was created by Linus Torvalds in 2005. We talked about Torvalds last month, who also created the most famous open-source operating system, Linux. Git was born in response to a skirmish with Larry McAvoy, the head of proprietary tool BitKeeper, over the potential misuse of his product. Torvalds went on vacation for a week and hammered out the most dominant version control system today - git. Version control systems allow developers to work simultaneously on projects, committing any changes to a centralized branch of code. It also allows for any changes to be rolled back to earlier versions which can be enormously helpful if a bug is found in the main branch. Git ushered in a new wave of version control, but the open-source version was somewhat difficult to use for the untrained developer. Enter Github and GitLab - two companies built around the idea of making the git version control system easier for developers to use. Github came first, in 2007, offering a platform to host and share projects. The Github platform was free, but not open source - developers couldn’t build onto their hosting platform - only use it. GitLab started in 2014 to offer an alternative, fully-open sourced platform that allowed individuals to self-host a Github-like tracking program, providing improved security and control. Because of Github’s first mover advantage, however, it has become the dominant platform upon which developers build: “Github is still by far the dominant market player: while it’s hard to find public numbers on GitLab’s adoption, its website claims more than 100,000 organizations use its product, whereas GitHub claims more than 2.9 million organizations.” Developers find GitHub incredibly easy to use, creating an enormous wave of open source projects and code-sharing. The company added 10 million new users in 2019 alone - bringing the total to over 40 million worldwide. This growth prompted Microsoft to buy GitHub in 2018 for $7.5B. We are in the early stages of this development explosion, and it will be interesting to see how increased code accessibility changes the world over the next ten years.

  3. Developing and Maintaining an Ecosystem Forever. Open source communities are unique and complex - with different user and contributor dynamics. Eghbal tries to segment the different types of open source communities into four buckets - federations, clubs, stadiums, and toys - characterized below in the two by two matrix - based on contributor growth and user growth. Federations are the pinnacle of open source software development - many contributors and many users, creating a vibrant ecosystem of innovative development. Clubs represent more niche and focused communities, including vertical-specific tools like astronomy package, Astropy. Stadiums are highly centralized but large communities - this typically means only a few contributors but a significant user base. It is up to these core contributors to lead the ecosystem as opposed to decentralized federations that have so many contributors they can go in all directions. Lastly, there are toys, which have low user growth and low contributor growth but may actually be very useful projects. Interestingly, projects can shift in and out of these community types as they become more or less relevant. For example, developers from Yahoo open-sourced their Hadoop project based on Google’s File System and Map Reduce papers. The initial project slowly became huge, moving from a stadium to a federation, and formed subprojects around it, like Apache Spark. What’s interesting, is that projects mature and change, and code can remain in production for a number of years after the project’s day in the spotlight is gone. According to Eghbal, “Some of the oldest code ever written is still running in production today. Fortran, which was first developed in 1957 at IBM, is still widely used in aerospace, weather forecasting, and other computational industries.” These ecosystems can exist forever, but the costs of these ecosystems (creation, distribution, and maintenance) are often hidden, especially the maintenance aspect. The cost of creation and distribution has dropped significantly in the past ten years - with many of the world’s developers all working in the same ecosystem on GitHub - but it has also increased the total cost of maintenance, and that maintenance cost can be significant. Bootstrap co-creator Jacob Thornton likens maintenance costs to caring for an old dog: “I’ve created endlessly more and more projects that have now turned [from puppies] into dogs. Almost every project I release will get 2,000, 3,000 watchers, which is enough to have this guilt, which is essentially like ‘I need to maintain this, I need to take care of this dog.” Communities change from toys to clubs to stadiums to federations but they may also change back as new tools are developed. Old projects still need to be maintained and that code and maintenance comes down to committed developers.

Business Themes

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  1. Revenue Model Matching. One of the earliest code-hosting platforms was SourceForge, a company founded in 1999. The Company pioneered the idea of code-hosting - letting developers publish their code for easy download. It became famous for letting open-source developers use the platform free of charge. SourceForge was created by VA Software, an internet bubble darling that saw its stock price decimated when the bubble finally burst. The challenge with scaling SourceForge was a revenue model mismatch - VA Software made money with paid advertising, which allowed it to offer its tools to developers for free, but meant its revenue model was highly variable. When the company went public, it was still a small and unproven business, posting $17M in revenue and $31M in costs. The revenue model mismatch is starting to rear its head again, with traditional software as a service (SaaS) recurring subscription models catching some heat. Many cloud service and API companies are pricing by usage rather than a fixed, high margin subscription fee. This is the classic electric utility model - you only pay for what you use. Snowflake CEO Frank Slootman (who formerly ran SaaS pioneer ServiceNow) commented: “I also did not like SaaS that much as a business model, felt it not equitable for customers.” Snowflake instead charges based on credits which pay for usage. The issue with usage-based billing has traditionally been price transparency, which can be obfuscated with customer credit systems and incalculable pricing, like Amazon Web Services. This revenue model mismatch was just one problem for SourceForge. As git became the dominant version control system, SourceForge was reluctant to support it - opting for its traditional tools instead. Pricing norms change, and new technology comes out every day, it’s imperative that businesses have a strong grasp of the value they provide to their customers and align their revenue model with customers, so a fair trade-off is created.

  2. Open Core Model. There has been enormous growth in open source businesses in the past few years, which typically operate on an open core model. The open core model means the Company offers a free, normally feature limited, version of its software and also a proprietary, enterprise version with additional features. Developers might adopt the free version but hit usage limits or feature constraints, causing them to purchase the paid version. The open-source “core” is often just that - freely available for anyone to download and modify; the core's actual source code is normally published on GitHub, and developers can fork the project or do whatever they wish with that open core. The commercial product is normally closed source and not available for modification, providing the business a product. Joseph Jacks, who runs Open Source Software (OSS) Capital, an investment firm focused on open source, displays four types of open core business model (pictured above). The business models differ based on how much of the software is open source. Github, interestingly, employs the “thick” model of being mostly proprietary, with only 10% of its software truly open-sourced. Its funny that the site that hosts and facilitates the most open source development is proprietary. Jacks nails the most important question in the open core model: “How much stays open vs. How much stays closed?” The consequences can be dire to a business - open source too much and all of a sudden other companies can quickly recreate your tool. Many DevOps tools have experienced the perils of open source, with some companies losing control of the project it was supposed to facilitate. On the flip side, keeping more of the software closed source goes against the open-source ethos, which can be viewed as organizations selling out. The continuous delivery pipeline project Jenkins has struggled to satiate its growing user base, leading to the CEO of the Jenkins company, CloudBees, posting the blog post entitled, “Shifting Gears”: “But at the same time, the incremental, autonomous nature of our community made us demonstrably unable to solve certain kinds of problems. And after 10+ years, these unsolved problems are getting more pronounced, and they are taking a toll — segments of users correctly feel that the community doesn’t get them, because we have shown an inability to address some of their greatest difficulties in using Jenkins. And I know some of those problems, such as service instability, matter to all of us.” Striking this balance is incredibly tough, especially in a world of competing projects and finite development time and money in a commercial setting. Furthermore, large companies like AWS are taking open core tools like Elastic and MongoDB and recreating them in proprietary fashions (Elasticsearch Service and DocumentDB) prompting company CEO’s to appropriately lash out. Commercializing open source software is a never-ending battle against proprietary players and yourself.

  3. Compensation for Open Source. Eghabl characterizes two types of funders of open-source - institutions (companies, governments, universities) and individuals (usually developers who are direct users). Companies like to fund improved code quality, influence, and access to core projects. The largest groups of contributors to open source projects are mainly corporations like Microsoft, Google, Red Hat, IBM, and Intel. These corporations are big enough and profitable enough to hire individuals and allow them to strike a comfortable balance between time spent on commercial software and time spent on open source. This also functions as a marketing expense for the big corporations; big companies like having influencer developers on payroll to get the company’s name out into the ecosystem. Evan You, who authored Vue.js, a javascript framework described company backed open-source projects: “The thing about company-backed open-source projects is that in a lot of cases… they want to make it sort of an open standard for a certain industry, or sometimes they simply open-source it to serve as some sort of publicity improvement to help with recruiting… If this project no longer serves that purpose, then most companies will probably just cut it, or (in other terms) just give it to the community and let the community drive it.” In contrast to company-funded projects, developer-funded projects are often donation based. With the rise of online tools for encouraging payments like Stripe and Patreon, more and more funding is being directed to individual open source developers. Unfortunately though, it is still hard for many open source developers to pursue open source on individual contributions, especially if they work on multiple projects at the same time. Open source developer Sindre Sorhus explains: “It’s a lot harder to attract company sponsors when you maintain a lot of projects of varying sizes instead of just one large popular project like Babel, even if many of those projects are the backbone of the Node.js ecosystem.” Whether working in a company or as an individual developer, building and maintaining open source software takes significant time and effort and rarely leads to significant monetary compensation.

Dig Deeper

  • List of Commercial Open Source Software Businesses by OSS Capital

  • How to Build an Open Source Business by Peter Levine (General Partner at Andreessen Horowitz)

  • The Mind Behind Linux (a talk by Linus Torvalds)

  • What is open source - a blog post by Red Hat

  • Why Open Source is Hard by PHP Developer Jose Diaz Gonzalez

  • The Complicated Economy of Open Source

tags: Github, Gitlab, Google, Twitch, Instagram, E;, Elm, Javascript, Open Source, Git, Linus Torvalds, Linux, Microsoft, MapReduce, IBM, Fortran, Node, Vue, SourceForge, VA Software, Snowflake, Frank Slootman, ServiceNow, SaaS, AWS, DevOps, CloudBees, Jenkins, Intel, Red Hat, batch2
categories: Non-Fiction
 

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