Ten Big Data Start-Ups To Watch

Ten Big Data Start-Ups To Watch


Jeff Vance has an online article in Tuesday’s (April 15, 2014) Techworld.com, with the title above. Mr. Vance is a Santa Monica, CA. based writer and founder of StartUp 50, a site devoted to emerging tech startups. He writes that “The Big Data Space is heating up to the point that many pundits already see it as the over-hyped heir to the “cloud.” “The hype may be a bit much,” Mr. Vance writes, “but, Big Data is already living up to its potential, transforming entire business lines, such as marketing, pharmaceutical research, and cyber-security.”

The global research firm IDC “sees big things ahead,” for this space in the coming years. IDC predicts that Big Data technologies will reach $32.4B by 2017, or about six times the growth rate of the overall information and communication technology market,” writes Mr. Vance.

Mr. Vance highlights ten Big Data start-ups (below with his observations on each) that he chose, “based on a mix of third-party validation (Venture Capital funding, named customers), experience (pedigree of management team), and market potential (how unique is the product; how much pent-up demand is there for this sort of solution; how well they are positioned competitively). Mr. Vance mixed slightly older start-ups — on the brink of making it big –with early stage start-ups exhibiting raw potential.” The ten companies are as follows:

(1) Sumo Logic: They apply machine learning to data center operations, using data analysis to pinpoint anomalies, predict and uncover potentially disruptive events, and identify vulnerabilities. Sumo, 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 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 the root cause of an operational or security issue. Customers include Netflix, McGraw-Hill, Orange, Pagerduty, and Medallia.

(2) Ayasdi: They apply big-data analysis in order to solve complex problems, including finding clues for cancers and other diseases, exploring new energy sources, and preventing terrorism and financial fraud. Ayasdi believes that looking at the shape of the data (rather that a more often used approach – asking questions and writing quires) is much more useful. Ayasdi argues that the large data sets have a distinct shape, or topology, and that shape has significant meaning. Ayasdi claims to help companies determine that shape within minutes — so they can automatically discover insights from their data — without ever having to ask questions, formulate quires, or write code. Customers include: GE, Citi, Merck, USDA, Mt. Sinai Hospital, the Miami Heat, and the CDC.

(3) Feedzai: The company uses real-time, machine-based learning to help companies prevent online fraud. The impact of online fraud is often under-estimated according to the company. According to the Ponemon Institute, the Target security breach could end up costing the retail giant nearly 700M in revenue. Feedzai claims to be able to detect fraud in any online commerce transaction, whether the credit card is present or not, — in real-time. Feedzai combines artificial intelligence (AI), to build more robust predictive models, and analyze consumer behavior in a way that mitigates risks, protects the customer from fraud, and preserves consumer trust. Feedzai’s software attempts to understand the way consumers behave when they make purchases anywhere, online or off. As a result, Feedzai claims to be able to detect fraud up to 10 days earlier than traditional methods; and, expose up to 60 percent more fraudulent transactions. Clients include Coca Cola, Logica, Vodafone, Ericsson, SIBs, Payment Solutions, and Serverbase Credit Card Solutions.

(4) CloudPhysics: Provides intelligent operations management for virtualized workloads. Virtualization and cloud-management platforms lack actionable information that admins use to better design, configure, operate, and troubleshoot their systems. However, not all data is equal. In order to go beyond basic capture data, decision-makers need to be able to evaluate, validate, and assess information from a variety of perspectives, in order to make real, impactful decisions. CloudPhysics goal is to analyze the world’s IT data knowledge and use the information to transform computing, driving out machine and human costs in ways never before seen. CloudPhysics’ service combines Big Data analytics, with data center simulation and resource management techniques. The company argues that this approach uncover hidden complexities in the infrastructure, discovers inefficiencies and risks that drain and endanger resources, and enables what-if analyses that can inform every data center decision. Customers include: North Shore Credit Union, and United Technologies. Competitors are Splunk and Sumo Logic.

(5) BloomReach: They provide Big-Data marketing applications, connecting consumers with content and products that they want and need — means that smart businesses end up capturing an even larger slice of that market. Customers include: Guess, Deb Shops, and Neiman Marcus. Others providing similar services include: Kontera, DataSong, and Persado. Big companies like Google, Amazon and IBM also use this kind of technology; but, they keep this technique within house.

(6) Altiscale: The company provides Hadoop-as-a-service, which is a market that is rapidly evolving. Hadoop is quickly becoming a key underlying technology for Big Data, but the problem is that Hadoop is both relatively new, and rather complicated, making it difficult for organizations to find the talent to deploy and manage Hadoop-based applications. Customers include MarketShare and Internet Archive. Amazon-web services is the 800lb guerrilla in the room; but, Altiscale will compete with Cloudera and Hortonworks.

(7) Pursway: Uses big data analytics and proprietary algorithms to help companies identify the customers who are most likely to influence how people in their social networks shop. Marketers are increasingly looking for ways to unlock the power of relationship-based marketing. Most consume opinion is influence by the opinions of people we know and trust: family, friends, and colleagues. While marketers have known this for quite a while, they have trouble acting on it. By imprinting a social-graph onto existing customer and prospect data, identifying actual relationships between buyers, and identifying target customers who have demonstrated influence over others’ purchasing decisions. Customers include Sony, Orange, and Comcast. Competitors include: Angoss, IBM and Sass.

(8) PlaceIQ: Provides a data-driven, mobile advertising and consumer-targeting platform. Mobile advertising and marketing present a unique challenge. The typical way companies try to understand consumer behavior online is through cookies. On SmartPhones, and Tablets, cookies don’t have as much traction. Even if cookies are enabled in mobile browsers, they aren’t terribly useful, since browsers are giving way to apps. However, a potential better replacement is location. Just as cookies track your journeys through the Web, marketers can glean demographic information from the actual physical locations you have visited. PlaceIQ says it “provides multi-dimensional depictions of consumers — across location and time, This allows brands to define audiences and intelligently communicate with those audiences to support better return-on-sales. PlaceIQ’s product, Audiences Now, focuses on targeting customers where they are, in real-time, creating an immediacy to a brand’s marketing strategy. Customers include: Mazda, Disney, and Montana Tourism. The competiton includes Verve Mobile, xAd, Placed, Sensed Networks, jiWire, 4INFO, and Millennial Media.

(9) MemSQL: Provides in-memory database technology for real-time Big Data analytics. Big Data and real-time analytics have the potential to profoundly impact the way organizations operate and how they engage with customers. However, there are challenges that prevent companies from fully extracting value from their data. Legacy database technologies are prone to latency, require complex and expensive architectures, and rely on slow, disc-based technology. MemSQL says it solves this performance bottleneck with a distributed in-memory computing model that runs on cost-effective commodity servers. MemSQL’s in-memorySQL database accelerates applications, powers real-time analytics, and combines structured, and semi-structured data into consolidated Big Data solutions. MemSQL says it empowers organizations to make data-driven decisions, which helps them to better engage customers, discover competitive advantages, and reduce costs. Customers include: Comcast, Zynga, Ziff Davis, and Shutter Stock.

(10) Couchbase: Provides NoSol, document-oriented, database technology, providing scalability and flexible data modeling needed for Big Data projects. Couchbase also claims to offer the first NoSol database for mobile devices. Customers include Cisco, Concur, LinkedIn, Orbitz, Salesforce.com, Zynga, Amadeus, McGraw Hill Education, and Neilson. Competitors include MongoDB, and DataStax.

Which, if any of the companies noted above are publicly traded — I do not know. I intend to use tomorrow’s close of the markets — due to Good Friday — to do some due diligence on the above — for potentially getting an investment footprint. What I was struck by — with all the above — is the “intrusive” nature and “prying” into our private lives that many of the technology/software applications offer. Those who have vilified NSA — wrongly in a lot of cases — seem to have missed this space, and it would seem that the old song, “every move you make, every step you take, I’ll be watching you,” has arrived. V/R, RCP

One comment

  1. Its really tremendous news. I think its a great job by them. Thank you for sharing with us. I think it would be helpful for all.

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