As MapR distances itself from Hortonworks, these big data unicorns must realize that a proprietary business model might not be enough.
Talk about kicking someone while they're down.
As if it weren't enough that Hortonworks has seen its stock decline by 36% in light of news that it needs $100 million in cash to keep fueling growth, big data rival MapR CEO John Schroeder somewhat sanctimoniously sanctioned Hortonworks' "staggering cash burn and GAAP losses" as he touted his own company's "good growth" approach.
While it's true that Hortonworks still has much to prove with its business model, MapR and other competitors should probably be a bit more introspective. After all, if we believe that MapR's proprietary approach to big data really does "drive partnership with our customers," the same thing must be true of proprietary incumbents that have had such "partnerships" for decades.
In other words, there's little indication that the business model is really the issue here.
The big data firing line
One thing Schroeder said is unequivocally true: Public markets no longer love growth at all costs. Actually, neither do private markets. While Hortonworks' valuation distress has been the most visible among the big data behemoths, others are also feeling the pain, if privately.
For example, mutual fund manager T.Rowe Price, which has invested heavily in Hortonworks competitor Cloudera, wrote down its investment by 17.2% in Q4 2015, as reported by The Information.
While The Information's Alfred Lee remarks that "it's unclear whether the markdowns are driven by internal financial challenges at the companies or external factors such as public market comparisons," what is clear is that the heady valuations of yesteryear are taking a beating, even if the companies themselves continue to make strides toward inventing the big data future.
This follows similar markdowns by Fidelity and other large investors in a variety of big data unicorns.
So, what is going on? If data is the new currency for business success, why aren't the big data unicorns flying high?
Turn and face the strange
In the case of Hortonworks, its stock market problems partly come down to cost: It's having to spend mountains of cash to build its business. Or, as former Wall Street analyst Peter Goldmacher once told me, big data unicorns are spending $200 million to earn $100 million. That's not a sustainable model.
Especially if, as he went on to tell me, enterprises "want new-school technology, but would prefer to buy it from old-school vendors."
For example, as longtime database executive Jnan Dash writes, "Some of the unicorns claim to be disruptive and a threat to the incumbents. This has not happened." Citing MongoDB as an example, he noted, "MongoDB claimed to disrupt Oracle's business, but Oracle's stock has been growing lately. Investors clearly see that profitless startups may not be as good as incumbents' growth prospects."
While I think it's way too early to make this claim, given the impressive gains MongoDB and DataStax's Cassandra have made against relational database incumbents, it is true that Oracle, in particular, has managed to keep ahead of its NoSQL competition, growing faster than any other database in terms of overall popularity in 2015.
Who makes big data bank?
This suggests that, for MapR, it's not going to be enough to brag about its proprietary business model. A proprietary license simply means interested prospects must pay to use one's software.
But first, they have to be interested, and Dash's warning is that the unicorns face fierce headwinds from enterprises that continue to buy from incumbent vendors, despite the startups living in the trenches of relevant open source projects.
Though dated, Wikibon's coverage of big data revenue in 2014 probably holds true today: Most of the money is being made by old-guard enterprise IT firms like IBM and Oracle, rather than startups like MapR. So long as this trend continues, the big data startups are going to continue to have to invest large amounts of money in order to earn large amounts of money.
Open source offers a way to circumvent the adoption problem, but it fans the flames of a greater revenue problem, one that won't be resolved anytime soon.
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- Find out why the Hadoop future is going to take a long time (TechRepublic)