Example 1: Data Center Software — Splunk
Our first example is from deep inside the data center. Technically, Splunk isn’t a SaaS company. While its software enables cloud application providers such as Salesforce.com, it is itself a versioned, old-fashioned software company. However, the trend it taps into is very much consistent with the cloud wave.
Splunk is surfing a massive trend — that the volume of “data exhaust” from IT systems is growing at an accelerating pace. IT managers need a way to manage this data, and make sense of what their systems are telling them. Just as brands want to separate the signal from the noise when it comes to social data about their products, corporate IT departments want to differentiate a routine alert from something that might presage a broader security problem, for example. Splunk’s software enables them to do just that.
So far, that part of the business looks like a traditional software model. Where Splunk has a tremendous opportunity is in making sense of data not just within one client, but across numerous clients.
While there are the beginnings of this trend in the form of Splunk clients sharing information and applications with each other, Splunk has not yet fully capitalized on this opportunity. Standing in its way, in part, is the fact that it is a software offering, with client silos that don’t talk to each other, or share the same database.
It can address this, of course, by offering an online version, which it now does. There are also emerging startups that are betting on the same transition to online — namely online services Sumo Logic and Loggly. It remains to be seen whether these new services, or Splunk’s online offering, will be able to create true network effects by having one view of machine data across clients.
One early stage startup that is trying to do just that is CloudPhysics. They’re also focused on the data center, but specifically on the unique challenges and opportunities associated with virtual machines. Since virtual machines hog real network and storage resources, managing them effectively has real consequences. Where this becomes an information business is that millions of virtual machines can share their configuration and performance information through the cloud, enabling more effective use of resources across the board.
Example 2: Social Software — Bazaarvoice
With its successful IPO this year, Bazaarvoice (which Nadim joined as a result of its acquisition of PowerReviews and which has a current market cap of $950 million) is recognized as one of the winners in the social software movement. Bazaarvoice, along with PowerReviews, which it acquired in June 2012, built its core business by allowing online retailers to use its software modules to gather, display and analyze customer reviews and ratings (what would have been called a “widget” before the term became unfashionable).
In that respect, the company is a classic enterprise software success story, replacing a piece of software that was built in-house with a stronger product. Just as retailers moved from home-grown ecommerce platforms to packaged solutions such as Magento and Demandware, they also turned to Bazaarvoice and PowerReviews to power the social interactions that sit on top of their e-commerce stores and to BloomReach to capture maximum demand from their content, thereby enabling them to compete with Amazon.com. So far, it looks like a software business.
What makes the company an information business, however, is the fact that there are built-in network effects in its usage. For example, the more retailers it signs up — such as its customers Walmart and BestBuy — the more value it can provide to branded manufacturers, such as Proctor & Gamble and Samsung. This is because it can analyze retailers’ real-time customer review data to provide valuable insights to brands.At scale, this is quite powerful. And indeed, the scale is quite large — by one estimate, the company’s clients process 18X the ecommerce volume that Amazon.com does.
This enables Bazaarvoice to monetize not just its software, but also its data. For example, by analyzing the millions of pieces of social interactions on behalf of its brands. It has also announced plans to go beyond software and analytics to offering advertising solutions, by powering more effective ads for brands; for example, by dynamically serving up a customer testimonial from someone like you.
So can other social software companies tap into this opportunity?
Given the inherently large amount of social “data exhaust” that such B2B2C companies are dealing with the answer is yes. Companies in the social identity management and gamification spaces are ones to watch — such as Gigya, Janrain,Badgeville and Bunchball. Gigya, for example, recently announced plans to go beyond powering social login for media and retail sites to managing the user data itself. This is potentially a much bigger opportunity than the social login piece (though potentially fraught with privacy landmines).
Example 3: Energy Software — Opower
The final example is an application serving a very different industry from e-commerce, namely the energy industry. Opower, which is rumored to be filing for an IPO soon, has built itself a nice recurring revenue business by serving the needs of these lumbering giants, such as PG&E.
Specifically, it generates better electric bills for them — yes, the pieces of paper you get in the mail — by actually customizing your utility bill with your personalized usage data, and how you might compare with neighbors. The goal is to get you to use less energy, especially the peak kind that results in firing up inefficient power plants. This change in consumer behavior through better presentation of usage information is incredibly valuable for utilities, because even a 1% reduction in peak usage can mean significant savings and subsidies from the government.
Of course, the company has plans to do a lot more than this. But the essence of their business is providing consumers with better usage data. Utilities would rather outsource this to Opower, at the right price, and they do.
So far, this looks like another typical software business, in this case software that creates better utility statements. But where it has an opportunity to become an information business is in doing more with this energy data. What Opower shares with Bazaarvoice is scale, and the fact that it is a B2B2C business. If almost half of North American e-commerce transactions touch Bazaarvoice in some way, the same or greater percentage of households’ energy consumption data flows through Opower.
This becomes quite powerful, if Opower offers new services built on top of that data. For example, Opower is ramping up its consumer business, which involves selling intelligent, network-connected thermostats to end users. Not only do these devices provide more control to consumers, they also have the potential to allow energy companies to centrally control residential energy consumption, in the event of a shortage, for example.
Even though Opower hasn’t done much to espouse this vision, the opportunity at stake is quite large, given how important energy is to our future (if you have any doubts, play the 2012 Presidential debate highlights again!).
Big Data Whitespace
So what other companies can benefit from such an approach? An obvious one is Tom Siebel’s C3 Energy, which attempts to do for commercial users what Opower does for residential users. And perhaps startups targeting other regulated industries, such as healthcare, can benefit from similar dynamics. Electronic heath record (EHR) providers come to mind, such as Practice Fusion and CareCloud, who are taking on healthcare giants such as McKesson.
In summary, traditional software models are giving way to “big data” approaches that seek to monetize the underlying information contained within the software. This means that even cloud computing leaders such as Marc Benioff’s salesforce.com will have to pay attention to not being stuck in yesterday’s SaaS paradigm. Successful cloud applications of the future will need to look more like LinkedIn–combining subscription businesses with ad-driven models–and less like Salesforce.com.
Above, we’ve seen three very different applications of such approaches, at varying stages of maturity. There are undoubtedly many more. What is clear is that both humans and machines are generating more data than ever before, and software players that can uncover intelligence from such data will prosper.