SOURCE: Alpine Data Labs

Alpine Data Labs

July 20, 2011 09:00 ET

Alpine Data Labs Delivers Big Data Predictive Analytics With Release of Alpine Miner 2.0

Simplicity and Affordability of Alpine Miner 2.0 Gives Business Users the Ability to Efficiently Extract Value From Big Data, Facilitating Timely Business Decisions, Innovation and Problem Solving

SAN MATEO, CA--(Marketwire - Jul 20, 2011) - Alpine Data Labs, developer of the first solution for Big Data Predictive Insight, today announced the availability of Alpine Miner 2.0. With this release, Alpine Data Labs makes predictive analytics accessible to businesses that lack the resources and skill set required by traditional predictive analytics. Alpine Miner's simplicity and affordability enable enterprises to institutionalize predictive analytics at all levels of the organization. With the newly accessible power of Big Data made possible by Alpine Miner, enterprises can foster an analytic culture that gives birth to business innovation and agility.

"Alpine Miner has enabled us to efficiently and affordably tap our big data to solve business problems," said H. Walter Young, senior vice president, Zions Bancorporation. "Now virtually any analyst in the company can learn to and make time-sensitive loan level business decisions that, for example, help predict loan defaults and even reduce fraud -- thereby increasing our profitability and improving stress testing capabilities."

Big Data is touted for its business value and potential as a competitive differentiator. In order to extract that value to gain a competitive advantage, enterprises need a reliable, cost-effective way to apply predictive analytics. However, most data modeling solutions are expensive and complex, and much of that value has remained locked up in data stores -- until now.

"Many organizations are storing Big Data, but they don't have the resources -- human or financial -- to unlock its value," said Julie Lockner, senior analyst and vice president of data management solutions at Enterprise Strategy Group. "Alpine Data Labs changes that by putting affordable, powerful, easy-to-use predictive analytics in the hands of Big Data business users."

According to a recent report from the 451 Group, in-database predictive analytics is a growing area of interest, fuelled by the boom in MPP (massively parallel processing)-based warehouse appliances and the advent of cheaper MPP hardware. The report pointed out that the combination of affordability -- with the ability to perform both modeling and scoring within a database kernel -- and serving up highly accurate models appears to be Alpine Miner's main high point. The target audience for Alpine Miner is customers with fast-growing data needs that want to add new variables to predictive models and gain fresh insights, as opposed to just sampling data, and are looking for an affordable data-mining product to meet this requirement.

Alpine Data Labs' Alpine Miner is intuitive, affordable and optimized for fast experimentation, collaboration and an ability to work within the database itself -- in short, enabling customers to do incredible things by making it easy and inexpensive to find important insights hidden in massive datasets. With Alpine Miner, predictive analytics moves from being a specialized activity practiced by a few skilled individuals to a vital and highly used competitive tool for modern business.

Key highlights of Alpine Data Labs' Alpine Miner:

  • Supports all data analytics operations expected by business users. This includes exploring, transforming, predictive modeling, data mining, scoring, automated model fitting and automated model exporting operations.
  • Affordable and intuitive. Alpine Miner breaks down the wall between organizations and the predictive power of their data by dramatically lowering the cost and complexity of predictive analytics. Plus, Alpine Miner's intuitive drag-and-drop visual interface makes the predictive analytic process straightforward and accessible -- business users and business experts can work side-by-side with analytics experts or use the interface themselves.
  • Speed. Alpine Miner embeds statistical algorithms in the database to leverage the innate capabilities of peta-scale parallel processing databases, like EMC Greenplum, and deliver the fastest end-to-end Big Data Predictive Analytics (BDPA) process from modeling to scoring to operationalizing. The streamlined workflow enables rapid experimentation and iteration that leads to true innovation. As the analytic workflow is reduced from months to days, business users' analytic productivity increases.
  • Increased ROI. A shorter, simpler BDPA process and sheer processing speed deliver faster business results. Alpine Miner overcomes the BDPA process and execution challenges that exist when using conventional tools and techniques. The ability to use all of the data means more accurate models are possible because the model is not "overfit" to a small sample. Better model accuracy means better returns, and better use of all data means immediate return on data investments.
  • Completely scalable. Organizations can use all of their data to develop models, no matter how big their data gets.
  • Secure. Analyzing the data within the database reduces the risk of dataset leakage from the data warehouse.
  • Transformative. Alpine Miner facilitates collaboration and the capturing of collective intelligence with libraries and self-documenting workflows. It enables business users throughout an organization to institutionalize the predictive modeling process and become truly data-driven.

About Alpine Data Labs
Alpine Data Labs has developed the first solution for Big Data predictive insight, making it faster, easier and less complex to achieve predictive insights from the massive datasets companies can now collect and store due to declining storage costs. Incubated within Greenplum (acquired by EMC in 2010) and founded by an experienced team with strong ties to China, Alpine Data Labs has Big Data in its DNA. The company is based in Silicon Valley and is backed by Sierra Ventures, Mission Ventures, Sumitomo Corporation and Stanford University.

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