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10 Big Data startups to watch

Hype may rival that around cloud but there is real innovation out there

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The Big Data space is heating up to the point that many pundits already see it as the over-hyped heir to "cloud." The hype may be a bit much, but Big Data is already living up to its potential, transforming entire business lines, such as marketing, pharmaceutical research, and cyber-security.

While the space is still fairly new, IDC, for one, sees big things ahead. The research firm predicts that the market for Big Data technologies will reach $32.4 billion by 2017, or about six times the growth rate of the overall information and communication technology market.

The startups below were chosen based on a mix of third-party validation (VC funding, named customers), experience (pedigree of the management team), and market potential (how unique is the product; how much pent-up demand is there for this sort of solution; how well are they positioned competitively). We also mixed slightly older startups on the brink of making it big with early stage startups exhibiting raw potential.

1. Sumo Logic

What they do: Apply machine learning to data center operations, using data analysis to pinpoint anomalies, predict and uncover potentially disruptive events, and identify vulnerabilities.Headquarters: Redwood City, Calif.CEO: Vance Loiselle, formerly VP of Global Services at BMC. He joined BMC via the acquisition of BladeLogic, which he co-founded. BMC acquired BladeLogic for $800 million.Founded: 2010Funding: $50 million from Accel Partners, Greylock Partners, and Sutter Hill Ventures.

Why they're on this list: Sumo Logic claims to address the "unknown unknown" problem of machine data: how do you get insights about data that you don't know anything about, or, worse, what do you do when you don't even know what you should be looking for?

Sumo Logic argues that managing machine data the output of every application, website, server, and supporting IT infrastructure component in the enterprise is the starting point for IT data analysis. Many IT departments hope they will be able to improve system or application availability, prevent downtime, detect fraud, and identify important changes in customer and application behavior by studying machine logs. However, traditional log management tools rely on pre-determined rules and thus fail to help users proactively discover events they don't anticipate.

Sumo Logic's Anomaly Detection attempts to solve this pain point by enabling enterprises to automatically detect events in streams of machine data, generating previously undiscoverable insights within a company's entire IT and security infrastructure and allowing remediation before an issue impacts key business services.

Sumo Logic uses pattern-recognition technology to distill hundreds of thousands of log messages into a page or two of patterns, dramatically reducing the time it takes to find a root cause of an operational or security issue.

Customers include Netflix, McGraw-Hill, Orange, Pagerduty, and Medallia.

Competitive Landscape: Sumo Logic will compete with CloudPhysics, Splunk, and open-source alternatives like Elasticsearch and Kibana.

2. Ayasdi

What they do: Apply Big Data analysis in order to solve complex problems, including finding cures for cancers and other diseases, exploring new energy sources, and preventing terrorism and financial fraud.Headquarters: Palo Alto, Calif.CEO: Gurjeet Singh, who was previously a Research Scientist at Stanford.Founded: The company was founded in 2008 but stayed in stealth-mode until its launch in January 2013.Funding: Ayasdi has raised $43.4 million in VC funding from FLOODGATE, Khosla Ventures, Institutional Venture Partners, GE Ventures, and Citi Ventures. The company also received $1.2 million in DARPA and NSF grants.

Why they're on this list: According to Ayasdi, since the creation of SQL in the 1980s, data analysts have tried to find insights by asking questions and writing queries. The query-based approach has two fundamental flaws. First, all queries are based on human assumptions and biases. Second, query results only reveal slices of data and do not show relationships between similar groups of data. While this method can uncover clues about how to solve problems, it is a game of chance that usually results in weeks, months, and years of iterative guesswork.

Ayasdi believes a better approach is to look at the "shape" of the data. Ayasdi argues that large data sets have a distinct shape, or topology, and that shape has significant meaning. Ayasdi claims to help companies determine that shape in minutes so they can automatically discover insights from their data without ever having to ask questions, formulate queries, or write code.

Ayasdi's Insight Discovery platform uses Topological Data Analysis (TDA) in tandem with machine learning techniques to enable data scientists, domain experts, and business analysts to optimize their data without coding.

Customers include GE, Citi, Merck, USDA, Mt Sinai Hospital, the Miami Heat, and the CDC.

Competitive Landscape: The machine learning space is wide open. Ayasdi will compete against IBM's Watson, SAS, and Skytree.


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