Cloudera, the enterprise big datacompany thats received significant backing from Intel, has released the expected price range for its IPO. The company says it plans to price its shares between $12 and $14.The price will get finalized the night before Clouderadebuts on the stock market, which is expected to happen later this month.
The proposed price is a significant disappointment for some of the investors and employees of the company because it will mean it has gone down in value since its last private round. If it prices at the top of the range, Clouderawill be valued at $1.8 billion, significantly less than the$4.1 billion valuation from its 2014 round. This scenario has become known as a down round IPO.
The company has raised at least $1 billion, dating back to 2008. Intel is the largest shareholder, owning 22 percent of the company, prior to the IPO. Accel owns 16.3 percent and Greylock Partners owns 12.5 percent.
The companys filing, which was unveiled last month,gave us a glimpse at theirfinancials. Revenue is growing, totaling$261 million for the fiscal year ending in January. Cloudera brought in$166 million for the same period last year.
Losses were $186.32 million, down from $203 million in the same timeframe the year before. The risk factors section of the filing says theyexpect to continue to incur net losses for the foreseeable future.
Competition could be one of the biggest obstacles for Cloudera. HP, IBM, Oracle, Amazon Web Services and Hortonworks are amongst the competitors listed in the S-1 documents.
Intel and Cloudera have been working on partnerships that aim to improve the speed and also the security for data processing. According to the filing, Intel and Cloudera collaborated on optimized data encryption speed through use of arithmetic acceleration built into the Intel Architecture. Intel and Cloudera also collaborated to develop Spot (incubating project), an open source cybersecurity analytics platform built on open data models that provides advanced threat detection using big data analytics and machine learning.