13 more big data & analytics companies to watch

So many big data and analytics-focused startups are getting funding these days that I’ve been inspired to compile a second slideshow highlighting these companies. This new batch has reined in some $250 million this year as they seek to help organizations make sense of the seemingly endless pool of data going online.

So many big data and analytics-focused startups are getting funding these days that I’ve been inspired to compile a second slideshow highlighting these companies (see “13 Big Data and Analytics Companies to Watch” for the previous collection). This new batch has reined in some $250 million this year as they seek to help organizations more easily access and make sense of the seemingly endless amount of online data.

Alation
Founded: 2012
Headquarters: Redwood City, Calif.
Funding/investors: $9M in Series A funding led by Costanoa Capital and Data Collective.

Focus: Its data accessibility platform is designed to make information more usable by the masses across enterprises. The company is led by former Oracle, Apple, Google and Microsoft engineers and executives, and its on-premises and virtual private cloud-based offerings promise to help data analysts get in sync, optimize data across Hadoop and other stores, and ensure data governance. Boasts customers including eBay and Square.

Aviso
Founded: 2012
Headquarters: Menlo Park, Calif. (with operations in India, too)
Funding/investors: $15M in Series B funding led by Scale Venture Partners and Next World Capital, bringing total funding to $23M.

Focus: Data science-driven predictive analytics software for sales teams, including the newly released Aviso Insights for Salesforce. Co-founder and CEO K.V. Rao previously founded subscription commerce firm Zuora and worked for WebEx, while Co-founder and CTO Andrew Abrahams was head of quantitative research and model oversight at JPMorgan Chase. The two met about 20 years ago at the National Center for Supercomputing Applications.

Birst
Founded: 2004
Headquarters: San Francisco
Funding/investors: $156M, including a $65M round in March led by Wellington Management.

Focus: Cloud-based business intelligence and analytics that works across compliance-sensitive enterprises but also gives end users self-service data access. This company, formed by a couple of ex-Siebel Analytics team leaders, has now been around for a while, has thousands of customers and has established itself as a competitor to big companies like IBM and Oracle. And it has also partnered with big companies, such as AWS and SAP, whose HANA in-memory database can now run Birst’s software.

BlueData
Founded: 2012
Headquarters: Mountain View
Funding/investors: $39M, including a $20M Series C round led by Intel Capital in August.

Focus: A founding team from VMware has delivered the EPIC software platform designed to enable customers to spin up virtual on-premises Hadoop or Spark clusters that give data scientists easier access to big data and applications. (We also included this firm in our roundup of hot application container startups.)

Datameer
Founded: 2009
Headquarters: San Francisco
Funding/investors: $76M, including $40M in Series E funding led by ST Telemedia.

Focus: Big data analytics application for Hadoop designed to let any employee analyze and visualize structured and unstructured data. Counts British Telecom and Citibank among its customers.

Deep Information Sciences
Founded: 2010
Headquarters: Boston
Funding/investors: $18M, including an $8M Series a round in April led by Sigma Prime Ventures and Stage 1 Ventures.

Focus: The company’s database storage engine employs machine learning and predictive algorithms to enable MySQL databases to handle big data processing needs at enterprise scale. Founded by CTO Thomas Hazel, a database and distributed systems industry veteran.

Looker
Founded: 2012
Headquarters: Santa Cruz
Funding/investors: $48M, including a $30M B round in March led by Meritech

Focus: Web-based business intelligence platform that provides access to data whether in a database or the cloud. A modeling language called LookML enables analysts to create interfaces end users can employ for dashboard or to drill down and really analyze data. Founded by CTO Lloyd Tabb, a one-time principal engineer at Netscape, where he worked on Navigator and Communicator. Looker claims to have Etsy, Uber and Yahoo among its customers.

Maana
Founded: 2012
Headquarters: Palo Alto
Funding: $14M, including $11M in Series A funding in May, with backers including Chevron Technology Ventures and Intel Capital.

Focus: Semantic search engine that plows through big data from multiple sources and delivers information in a way that can be consumed by line-of-business application users. The company announced in June that its platform is now powered by Apache Spark. Co-founder Donald Thompson spent 15 years prior to launching Maana in top engineering and architect jobs at Microsoft, including on the Bing search project.

RapidMiner
Founded: 2007
Headquarters: Cambridge, Mass.
Funding/investors: $20M, including $15M in Series B funding led by Ascent Venture Partners.

Focus: This company, which got its start in Germany under founder Ingo Mierswa, offers an open source-based predictive analytics platform for business analysts and data scientists. The platform, available on-premises or in the cloud, has been upgraded of late with new security and workflow capabilities. Peter Lee, a former EVP at Tibco, took over as CEO in June.

Reltio
Founded: 2011
Headquarters: Redwood Shores, Calif.
Funding/investors: $10M in Series A funding in March, from Crosslink Capital and .406 Ventures.

Focus: The team behind Informatica/Siperian MDM started Reltio, which offers what it calls data-driven applications for sales, marketing, compliance and other users, as well as a cloud-based master data management platform. The company claims its offerings break down silos between applications like CRM and ERP to give business users direct access to and control over data.

Sensai
Founded: 2014
Headquarters: Palo Alto
Funding/investors: $900K in seed funding from investors including Andreessen Horowitz and Formation8.

Focus: A “data science platform for the unstructured world.” Sensai’s offering makes it possible to quantify and analyze textual information, such as from news articles and regulatory filings. The company is focused initially on big financial firms, like UBS, though also has tech giant Siemens among its earlier customers. Two of Sensai’s co-founders come from crowdfunding company Rally.org.

spare5
Founded: 2014
Headquarters: Seattle
Funding/investors: $13.25M, including a $10M Series A round led by Foundry Group, New Enterprise Associates and Madrona Venture Group

Focus: This iPhone app enables businesses to tap into smartphone users (or “Fives”) to clean up big data in their spare time for a little spare cash. The idea is that computing power alone can’t be counted on to crunch and analyze big data. Micro-tasks include everything from SEO-focused photo tagging to conducting surveys.

Treasure Data
Founded: 2011
Headquarters: Mountain View
Funding/investors: $23M, including $15M in January in Series B funding led by Scale Venture Partners.

Focus: Provides cloud services designed to simplify the collection, storage and analysis of data, whether from mobile apps, Internet of Things devices, cloud applications or other sources of information. This alternative to Hadoop platforms and services handles some 22 trillion events per year, according to the company, which has a presence not just in Silicon Valley, but in Japan and South Korea as well.

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Big Data without good analytics can lead to bad decisions

Big Data does not necessarily mean Good Data. And that, as an increasing number of experts are saying more insistently, means Big Data does not automatically yield good analytics.

If the data is incomplete, out of context or otherwise contaminated, it can lead to decisions that could undermine the competitiveness of an enterprise or damage the personal lives of individuals.

One of the classic stories of how data out of context can lead to distorted conclusions comes from Harvard University professor Gary King, director of the Institute for Quantitative Social Science. A Big Data project was attempting to use Twitter feeds and other social media posts to predict the U.S. unemployment rate, by monitoring key words like "jobs," "unemployment," and "classifieds."

Using an analytics technique called sentiment analysis, the group collected tweets and other social media posts that included these words to see if there were correlations between an increase or decrease in them and the monthly unemployment rate.

While monitoring them, the researchers noticed a huge spike in the number of tweets containing one of those key words. But, as King noted, they later discovered it had nothing to do with unemployment. "What they hadn't noticed was Steve Jobs died," he said.

In the telling, it's a somewhat humorous story, outside of the tragedy of Jobs' untimely passing. But the lesson is a deadly serious one for those looking to rely on the magic of Big Data to guide their decisions.

King said the mix-up over the dual meanings of "jobs" is, "just one of many similar anecdotes. Anyone working in this area has had similar experiences."

"Lists of keywords, curated by human beings, work OK for the short run, but tend to fail catastrophically over the long run," he said. "You can fix it up by adding exceptions, but there's a lot of human labor involved."

He said it is easy for anyone to create their own example just by entering a keyword into the Bing Social page.

"You'll see some relevant things and some irrelevant. If you don't change the query and watch over time, you will often find the conversation veering away in some way -- sometimes a little, sometimes not at all for a while, and sometimes dramatically," he said.

But King said that overall there are many examples of big data analytics producing useful things, "so failures tend not to appear in the literature."

Kim Jones, senior vice president and CSO of Vantiv, said this is not a new problem, but one that can be magnified if people think massive amounts of data are magically going to produce good analytics.

"The Jobs example was a classic case of data without context. Data by itself doesnt equal intelligence," he said.

King agrees that context is key. He is co-founder and chief scientist of Crimson Hexagon, a big-data analytics firm that, in the words of Wayne St. Amand, its executive vice president of marketing, seeks to provide, "context, meaning and structure to online conversations."

Yet there are increasing examples of data without context driving decisions. The Wall Street Journal reported in February on health insurance companies using Big Data to create profiles of their members. Among the things the companies tracked was a history of buying plus-sized clothes, which could lead to a mandatory referral to weight-loss programs.

Few people would argue with encouraging people to live healthier lives, but the privacy implications are disturbing. It is possible the person buying those clothes might have been doing so for another family member. And it is not always so benign. Bloomberg BusinessWeek reported in 2008 on individuals being denied health insurance based on a history of prescription drug purchases that suggested even minor mental health conditions.

Adam Frank, writing on the National Public Radio blog, noted that in some cases banks will deny a loan to someone based in part on their contacts on the employment networking site LinkedIn or the social networking behemoth Facebook. If your "friends" are deadbeats, your credit-worthiness may be based on their reliability.

Frank quoted Jay Stanley, senior policy analyst at the ACLU, noting on that groups blog that, "Credit card companies sometimes lower a customer's credit limit based on the repayment history of the other customers of stores where a person shops. Such 'behavioral scoring' is a form of economic guilt-by-association based on making statistical inferences about a person that go far beyond anything that person can control or be aware of."

Kim Jones said the tendency to jump to a conclusion from correlations without further analysis could have affected him personally. "During the late '80s and early '90s, data showed that Hispanic and Black males between the ages of 20 and 27 who were driving an entry-level luxury car on the I-95 corridor were likely to be drug runners," he said.

"I fit some of that profile -- I'm African American, I was that age and at that time I was driving a car like that. But if I had been stopped, the police would have seen that I was wearing an Army uniform with Second Lt. bars and had a West Point ring," he said.

The point, he said, is that, "its always bad to rely just on data analytics. When you take the human element out of the equation, you by definition create a higher error rate."

In short, Big Data is a tool, but should not be considered the solution. "It can help you narrow something down from millions to perhaps 150," Jones said, "but the temptation is to let the computer do it all, and that is what is going to get you in trouble."

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