The Boulder BI Brain Trust


February 2013 Archives

Big Data and the Internet of Things

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The physical world (from goods to equipment) is becoming digitally connected through a multitude of sensors.  Sensors can be found today in most industrial equipment, from metal presses to airplane engines, shipping containers (RFID), and automobiles (telematics devices).  Consumer mobile devices are essentially sensor platforms.  These connected devices can automatically provide status updates, performance updates, maintenance requirements, and machine-to-machine (M2M) interaction updates.  They can also be described in terms of their characteristics, their location, etc.  Until recently these sensors have been interconnected using proprietary protocols.  More recently, however, sensors are starting to be connected via IP, to form the Internet of Things, and by 2020 50B devices will be connected in this way.  The connected physical world is becoming a source of immense amount of low-level, structured and semi-structured data, e.g., big data.

Collecting and utilizing sensor data is not new.  For example, GE uses data from sensors to monitor the performance of industrial equipment, locomotives, jet engines and health care equipment.  United Airlines uses sensors to monitor the performance of its planes on each flight. And government organizations, such as the TSA, collect data from the various scanners they use at airports.  The key applications that have emerged through these earlier efforts are remote service and predictive maintenance.  

While our ability to collect the data from these interconnected devices is increasing, our ability to effectively, securely and economically store, manage, clean and, in general, prepare the data for exploration, analysis, simulation, and visualization is not keeping pace.  Today we seem to be pre-occupied with the goal of trying to put all of data we collect into a single database.  Even in this task we are not doing a particularly good job.  The existing database management systems are proving inadequate for this task.  They may be able to process the time series data collected by sensors, but they cannot correlate it.  The effectiveness of newer database management systems (NoSQL), e.g., Hadoop, MongoDB, Cassandra, is also proving inconsistent and depends largely on the type of application accessing the database and operating on the collected data.

The new generation of applications that will exploit the big data collected by sensors must take a ground up approach to the problem they are trying to address, not unlike that taken by Splunk.  In Splunk’s case, the application developers considered the ways the sensor data being collected from data centers must be cleaned, the other data sets with which it must be integrated/fused, the approach to interact with the resulting data sets, etc.  Splunk’s developers were able to accomplish this and deliver a very effective application because they understood the problem, the spectrum of data that must be used to address the problem, and the role the low-level data is playing in this spectrum.  They also appear to have understood the importance of providing effective analyses of the low-level data as well of the higher-level data sets that resulted when several different data sources are fused. 

The Internet of Things necessitates the creation of two types of systems with data implications.  First, a new type of ERP system (the system of record) that will enable organizations to manage their infrastructure (IT infrastructure, human infrastructure, manufacturing infrastructure, field infrastructure, transportation infrastructure, etc.) in the same way that the current generation of ERP systems allow corporations to manage their critical business processes.  Second, a new analytic system that will enable organizations to organize, clean, fuse, explore and experiment, simulate and mine the data that is being stored to create predictive patterns and insights.  Today our ability to analyze the collected data is inadequate because:

  1. The sensor data we collect is too low-level; it needs to be integrated with data from other sensors, as well as higher-level data, e.g., weather data, supply chain logistics data, to create information-richer data sets. Data integration is important because a) high-velocity sensor data must be brought together and b) low-granularity sensor data needs to be integrated with other higher-granularity data.  Today integration of sensor data is still done manually on a case-by-case basis.  Standards-based ways to integrate such data, e.g., RESTful APIs, other types of web services, have not yet adopted broadly in the Internet of Things world and they need to.  We need to start thinking of sensor data APIs in the same way we have been thinking about APIs for higher-level data.  And once we start defining these standards-based APIs we also need to start thinking about API management.
  2. We don't yet know the range of complex analyses to perform on the collected sensor data because we don't know yet what enterprise and government problems we can solve through this data.
  3. Even for the analyses we perform, we often lack the ability to translate any analysis results to specific actions.

Finally, along with these two types of systems we will need to effectively manage the IP addresses of all devices that are being connected in these sensor networks.  IPV6 gives us the ability to connect the billions of sensors using IP.  We need better ways to manage these connected devices.  Most organizations today manage them on spreadsheets.

The big data generated by the Internet of Things is opening up great opportunities for a new generation of operational and analytic applications.  Creating these applications will require taking a ground-up approach from the basic sensor technology and the data sensors can generate to the ways sensors and managed and data is integrated, to the actions that can be taken as a result of the analyzed data.  

The year ended well for our SaaS portfolio companies, and particularly well for our online marketing and advertising platform companies.  While Europe remained soft, NA more than made up for it.  All of online advertising platform companies exceeded their budgets, in some cases by as much as 40%.  We also saw margin expansion as several of our SaaS companies were able to continue raising license prices.  However, these companies continue to hire aggressively (sales, engineering, customer support) as they try to keep up with the accelerating adoption of SaaS. Based on the end of the year activity it became obvious that, as I had written, the enterprise buyers were holding back in previous quarters and, once again, flushed their budgets during 4Q.  The buying activity and our interactions with solution buyers led us to conclude that 2013 is starting with strong optimism around online marketing solutions, as well as general SaaS solutions for the mid-upper enterprise.  We are more circumspect about the 2013 application budgets of global enterprises where we, and investment analysts from Morgan Stanley, Piper Jaffray and other banks, continue to see very modest YoY increases.  We expect 2-2.5% for the year for the global enterprise but 4-6% for the mid-upper enterprise and over 10% for online marketing and advertising solutions, in particular. 

Based on the results announced to date by the public SaaS companies we monitor, e.g., Netsuite, Demandware, ServiceNow, Jive, Cornerstone OnDemand, Qualys (Trident portfolio company) their performance during 4Q12 remained strong, in-line with analyst expectations.  Brightcove less so.  Valueclick and Millennial Media, two public online advertising platform companies we follow, announced strong 4Q12.  The public SaaS companies continue to be impacted by the global macro environment and the decreasing IT spending.  We continue to see moderating revenue growth for these companies, compared to the explosive growth we had seen in the past.  Based on the announced results, I expect that for 2013 these companies, and our corresponding portfolio companies, will show consistent YoY growth in the range of 25-50%.

Overall, the number of M&A technology transactions, and the value of those transactions, during 2012 were slightly down from 2011, but cloud computing in general attracted more of the acquirers’ attention.  However, 4Q12 was another active quarter for SaaS M&A.  Oracle bought Eloqua, as it continues to bolster its marketing technology solutions portfolio, Synchross bought Newbay from RIM, and Citrix's acquisition of Zenprise.

In 4Q12 we invested in Fruition Partners.  This is our first investment in a tech-enabled services company that has built its business around a SaaS solution, in this case ServiceNow’s solution.  We were impressed by Fruition’s growth trajectory, capital efficiency, and differentiated IP that is built on top of ServiceNow’s application.

Positive aspects of our SaaS portfolio’s performance:

  1. Strong license revenue growth of 30% QoQ for the online advertising platform companies, and 15-20% of the remaining SaaS companies.  Our portfolio companies saw contracts with higher ARR (due to broader corporate deployments) and fewer multi-year contracts (which we liked).  During the quarter our companies also saw less pressure for discounts, an issue that was a concern in 3Q12.  4Q is typically the strongest quarter for the advertising platform companies, and this year was no exception.  Based on our direct checks regarding advertising budgets for 2013 we are starting the year being very optimistic. 
  2. Steady renewal rates (85-90%) with strong upsells in 15-20% (similar to last quarter) of the renewing customers, with the exception of social application companies where, as I had also written when I was reporting the 3Q12 results, we continue to see higher than expected churn (15% MRR churn) by mid-market clients.
  3. Sales pipelines grew well, indicating continued interest in SaaS applications in general and online advertising platforms. We see continued interest for solutions for the CMO and also in data-driven solutions. 
  4. During the quarter our SaaS portfolio companies saw increasing interest for partnerships by large IT vendors.  Many of these partnerships are driven by large enterprise customers that want to see SaaS applications, particularly social business applications, integrated into larger enterprise platforms, e.g., CRM, HRMS, eCommerce.  We expect that these partnerships will start taking shape during the 1H13.

Negative aspects of our SaaS portfolio performance (none of these are new compared to what we reported in the past):

  1. Higher than expected revenue and customer count churn in social business application companies.  We see this as the result of customers having difficulty establishing the ROI for many applications of this type.  It also follows a more general, and important to follow, trend we observed during 4Q12 regarding social in the enterprise. In particular, we saw that enterprises started merging their social interaction departments/organizations, which during 2011 and 2012 operated as standalone entities, into their marketing departments, presumably to better integrate their overall marketing efforts and thus try to achieve better ROI from these efforts.
  2. Lead generation and nurturing remains uneven.  Lead generation is becoming more effective due to the strong interest in SaaS solutions.  However, nurturing these leads and converting them into sales qualified opportunities still requires too much field sales involvement.

We have been pleased with the end of the year performance of our entire SaaS portfolio and particularly our advertising and marketing platform companies.  2012 was a transformative year for many of them as they entered the growth stage of their lifecycle, aided by the overall market’s growing appetite for SaaS solutions.



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