I originally envisioned this blog as a way to discuss our recent $30 million funding, led by our latest investor, Sequoia Capital, with full participation from Greylock, Sutter Hill and Accel. I've been incredibly impressed with the whole Sequoia team and look forward to our partnership with Pat. Yet despite 300 enterprise customers (9 of the Global 500), lots of recent success against our large competitor, Splunk, and other interesting momentum metrics, I'd rather talk about the ride and lessons learned from my first two years as a CEO. It’s Lonely. Accept It and Move On. My mentor, former boss and CEO of my previous company told me this, years ago. But at the time, it applied to him and not me (in hindsight I realize I did not offer much help). But, like being a first time parent, you really can’t fathom it until you face it yourself. I’m sure there’s some psychology about how certain people deal with it and others don’t. I’m constantly thinking about the implications of tactical and strategic decisions. I’ve learned that if you’re too comfortable, you’re not pushing hard enough. The best advice I can give is to find a Board member you can trust, and use him or her as a sounding board early and often. Trust Your Gut. There have been many occasions when I have been given good advice on key decisions. One problem with good advice, is you can get too much of it, and it isn’t always aligned. The best leader I ever met, and another long-time mentor, would always ask, ‘what is your gut telling you?’ More often than not, your gut is right. The nice thing about following your instincts, the only person to blame if it goes awry is yourself. Act Like It’s Your Money. I grew up in Maine, where $100,000 can still buy a pretty nice house. When I first moved to California from Boston it took me some time to get accustomed to the labor costs and other expenses. The mentality in most of the top startups in Silicon Valley is “don’t worry, you can always raise OPM (other people's money)”. Though I understand the need to invest ahead of the curve, especially in a SaaS-based business like ours, I also believe too much funding can cause a lack of discipline. People just expect they can hire or spend their way around a problem. Don’t Be Arrogant. Just saying it almost disqualifies you. Trust me, I have come across all kinds. Backed by arguably the four best Venture Capital firms in the business, I have had plenty of opportunities to meet other CEOs, founders and execs. Some are incredible people and leaders. Some, however, act like they and their company are way too valuable and important to treat everyone with respect. Life is too short not to believe in karma. Listen Carefully. If a sales rep is having trouble closing deals, put yourself in his shoes and figure out what help he needs. If the engineering team is not meeting objectives fast enough, find out if they really understand the customer requirements. Often the smallest tweaks in communication or expectations can drastically change the results. Lastly, listen to your customer(s). It is very easy to write off a loss or a stalled relationship to some process breakdown, but customers buy from people they trust. Customers trust people who listen. It's a People Business. Software will eat the world, but humans still make the decisions. We're building a culture that values openness and rapid decision-making while aligning our corporate mission with individual responsibilities. This balance is a constant work in process and I understand that getting this balance right is a key to successfully scaling the Sumo Logic business. Find the Right VCs at the Right Time. I can’t take any credit for getting Greylock or Sutter Hill to invest in our A and B rounds, respectively. But I do have them to thank for hiring me and helping me. We partnered with Accel in November of 2012 and now Sequoia has led this recent investment. Do not underestimate the value of getting high quality VCs. Their access to customers, top talent, and strategic partners is invaluable. Not to mention the guidance they give in Board meetings and at times of key decisions. The only advice I can give here is: 1) know your business cold, 2) execute your plan and 3) raise money when you have wind at your back. Venture Capitalists make a living on picking the right markets with the right teams with the right momentum. Markets can swing (check Splunk’s stock price in last 3 months) and momentum can swing (watch the Bruins in the Stanley Cup – never mind they lost to the Canadiens). Believe. It may be cliché, but you have to believe in the mission. If you haven’t watched Twelve O’Clock High, watch it. It’s not politically correct, but it speaks volumes about how to lead and manage. You may choose the wrong strategy or tactics at times. But you’ll never know if you don’t have conviction about the goals. OK, so I’m no Jack Welch or Steve Jobs, and many of these lessons are common sense. But no matter how much you think you know, there is way more that you don’t. Hopefully one person will be a little better informed or prepared by my own experience.
The annual craze of getting up at 4am to either stand in line or shop online for the “best” holiday deals is upon us. I know first-hand, because my daughter and I have participated in this ritual for the last four years (I know - what can I say - I grew up in Maine). While we are at the stores fighting for product, many Americans will be either watching football, or surfing the web from the comfort of their couch looking for that too-good-to-be-true bargain. And with data indicating a 50% jump in Black Friday and Cyber Monday deals this year, it’s incumbent on companies to ensure that user experiences are positive. As a result, the leading companies are realizing the need to obtain visibility end-to-end across their applications and infrastructure, from the origin to the edge. Insights from machine data (click-stream in the form of log data), generated from these environments, helps retailers of all stripes maximize these two critical days and the longer-term holiday shopping season. What are the critical user and application issues that CIOs should be thinking about in the context of these incredibly important shopping days? User Behavior Insights. From an e-commerce perspective, companies can use log data to obtain detailed insights into how their customers are interacting with the application, what pages they visit, how long they stay, and the latency of specific transactions. This helps companies, for example, correlate user behavior with the effectiveness of specific promotional strategies (coupons, etc) that allow them to rapidly make adjustments before the Holiday season ends. The Elasticity of The Cloud. If you’re going to have a problem, better it be one of “too much” rather than “too little”. Too frequently, we hear of retail web sites going down during this critical time. Why? The inability to handle peak demand - because often they don’t know what that demand will be. Companies need to understand how to provision for the surge in customer interest on these prime shopping days that in turn deliver an exponential increase in the volume of log data. The ability to provide the same level of performance at 2, 3 or even 10x usual volumes in a *cost-effective* fashion is a problem few companies have truly solved. The ability of cloud-based architectures to easily load-balance and provision for customer surges at any time is critical to maintaining that ideal shopping experience while still delivering the operational insights needed to support customer SLAs. Machine Learning for Machine Data. It’s difficult enough for companies to identify the root cause of an issue that they know something about. Far more challenging for companies is getting insights into application issues that they know nothing about. However, modern machine learning techniques provide enterprises with a way to proactively uncover the symptoms, all buried within the logs, that lead to these issues. Moreover, machine learning eliminates the traditional requirement of users writing rules to identify anomalies, which by definition limit the ability to understand *all* the data. We also believe that the best analytics combine machine learning with human knowledge about the data sets - what we call Machine Data Intelligence - and that helps companies quickly and proactively root out operational issues that limit revenue generation opportunities. Security and Compliance Analytics. With credit cards streaming across the internet in waves on this day, it’s imperative that you’ve already set up the necessary environment to both secure your site from fraudulent behavior and ensure your brand and reputation remain intact. As I mentioned in a previous post, the notion of a perimeter has long since vanished which means companies need to understand that user interactions might occur across a variety of devices on a global basis. The ability to proactively identify what is happening in real-time across your applications and the infrastructure on which they run is critical to your underlying security posture. All this made possible by your logs and the insights they contain. Have a memorable shopping season and join me on twitter - @vanceloiselle - to continue the conversation.
This morning, Sumo Logic announced a $30M Series C investment round. On behalf of our entire company, I am pleased to welcome Accel Partners to the Sumo Logic family, and to join us in our mission of enabling IT and Operations teams to generate instant, actionable insights from the vast amount of machine data their organizations generate. This investment round, led by Accel and joined by existing investors Greylock Partners, Sutter Hill Ventures and Shlomo Kramer, is a major testament to the value that enterprises are gaining from deploying Sumo Logic’s powerful and highly scalable cloud-based log management and analytics service. It is also further testimony to the natural intersection between Cloud and Big Data, and Sumo Logic’s leading position as the Enterprise Cloud for Machine Data. With Sumo Logic, customers obtain a number of benefits not available with traditional on-premise solutions: A seamless and elastic Big Data platform which automatically scales to meet the demands of the modern enterprise Real-time monitoring and visualization powered by our streaming query engine Significantly reduced TCO as there is no need to deploy expensive on-premise hardware or dedicated personnel Patent pending real-time analytics that help you search for what you know and analyze what you don’t Rapid time to value through immediate insight from vast amounts of machine data With this $30M investment, we will further accelerate research and development and expand our innovations around machine data and analytics. Which of course means we are hiring the best and brightest. If you have passion for Big Data and the Cloud, and the talent to go along with it, we’d love to speak with you. Or, if you are a potential customer and want to experience the power of Sumo Logic for yourself, sign up instantly and for free. And lastly, if you’re interested in knowing a bit more about why I’m so bullish, check out my first blog post as Sumo Logic CEO. Everything I wrote then is even more true today.
Recently Splunk announced the availability of its cloud offering, which is just further validation that large enterprises, along with the rest of the world, are moving from on-premise to cloud solutions. In this post I’ll share a few thoughts on why Sumo Logic started in the cloud and raise a few questions about Splunk's announcement as it relates to the broader machine data search and analytics market. 1. Why Sumo Logic started in the Cloud? I joined Sumo Logic with first-hand experience trying to shift an on-premise software company to a cloud agenda. The reality is that if you look at the traction and growth in every major category you will see cloud vendors rapidly taking share from the much larger on-premise incumbents. Salesforce.com started it, but recent examples include ServiceNow which just went public, SuccessFactors which was bought by SAP, and Workday, which is in hyper-growth as evidenced by its recent S-1 filing. What makes this transition so hard? On-premise vs. cloud architecture. Most on-premise software companies make the classic mistake of trying to port their legacy architecture to the cloud so they can take advantage of work and features that have already been done. In the majority of cases, this just does not work. Even the newest on-premise software companies tend to be at least 7-8 years old, with their underlying technology, tools and approach even older. This antiquated technology means they’re unable to take advantage of the tools and languages to scale across hundreds or thousands of highly ephemeral cloud computing instances and take advantage of the latest Big Data principles that have evolved significantly in the past two years. Engineering process and priorities. The process followed and pace at which software updates are prioritized, developed, tested and released is drastically different between on-premise and cloud offerings. To do it effectively you really need to invest in two separate teams with separate charters and visions. Most of today’s Cloud offerings seamlessly provide software updates every week, if not every day. Running two different processes and multiple versions can be very distracting and expensive, so you naturally lose focus on the important things, like operating, securing and scaling the cloud service. Business model and revenue recognition. Almost all on-premise software companies rely on closing deals each quarter to drive the majority of their software revenue for that quarter (perpetual licenses). Cloud companies typically take their revenue ratably over the term of the contract (subscription). Switching to a Cloud offering can wreak havoc on the financials if you previously relied on perpetual license sales. Sales model and compensation. Sales reps get paid on quota (how much they sell each year). On-premise deals (perpetual) tend to be at least 2-3x larger than subscription deals because all of the software is sold up front and then you pay maintenance over time. Most on-premise software companies do not properly incent sales reps to sell these cloud deals because they are not willing to pay/cost structure can't support a higher commission rate for deals that by nature are smaller. 2. Is Splunk’s cloud offering meant for the mid-market or enterprise? Though Splunk’s announcement is exciting and a validation for cloud solutions like Sumo Logic’s, I’m not sure we will actually see it in most large enterprise accounts. What happen to tackling Big Data? As InformationWeek points out here “Splunk Storm is decidedly not a big data play”. According to Splunk’s Web site, the pricing for advertised data plans tops out at 1TB of data. That is equivalent to less than 35GB of data per day retained for 30 days. The majority of large enterprises have far more than 35GB of machine data being generated each day – so its not clear if those customers have to move to the on-premise version or if they can scale their data beyond that. Sumo Logic is all about scale and Big Data. The cloud architecture gives us the flexibility to elastically scale any portion of our compute and storage engines on-demand, thus overcoming the headaches and performance bottlenecks of on-premise deployments. What about private clouds? The announcement also states that this “is for organizations that develop and run applications in the public cloud”. Splunk on their recent earnings call wen't on to say "It doesn’t have the features of Splunk Enterprise. It’s very targeted toward developers and being able to help log apps that are in development in the cloud." The majority of large enterprise applications are in private clouds, not in the public cloud, and certainly the bulk of the machine data being generated by these applications is not in the public cloud. So it would seem Splunk will be asking the majority of large enterprises to continue to install, manage and scale Splunk’s on-premise offering within their data center(s) and use the cloud offering for public cloud applications. We at Sumo Logic look forward to seeing how Splunk evolves this first version of their cloud solution and we welcome the opportunity to address large and small enterprises’ machine data search and analytics initiatives with one highly scalable offering for public and private clouds.
What is Free Machine Data Search and Analytics? Today we announce Sumo Logic Free, the first and only free, enterprise-class machine data search and analytics service. The free version of Sumo Logic’s cloud-based service offers full functionality, including: Loading, indexing and archival of machine data Distributed search Real-time analytics Proactive monitoring and alerting Reporting and visualization Role-based access controls Our cloud-based approach eliminates the need for expensive premise-based solutions that require upfront investments in hardware and software as well the headache of constant management and upgrades . The Sumo Logic service delivers a petabyte-scale platform that provides companies with access to valuable operational insights from their machine data—all in real time. Why Are Companies Embracing Machine Data? The volume, velocity and variety of machine data (logs, events, configuration changes, clickstream, etc.) being generated by applications, networks, servers, and mobile devices is overwhelming IT organizations. Our goal is to give every enterprise a way to search and analyze this machine data to troubleshoot application problems, proactively monitor performance and availability, and gain valuable operational and customer insights. For those companies just initiating the search for the right approach to handling their Big Data, we provide a purpose-built, scalable Big Data service to harness all the great information IT organizations have at their disposal. We leverage our patent-pending Elastic Log Processing ™ platform to intelligently scale and tune the service as the enterprise grows, so IT organizations can focus on delivering value to the business. Free Service = Win – Win. We know that once customers start using our service, they’ll quickly see the power and value of the insights they can glean, and will want to use it for more use cases and with more data. In addition, as more customers use our service in interesting and strategic ways, we’ll be able to apply their insights for the benefits of future customers and for future product development.