Alpine Data Labs Offers Visualization Tools to Create In-Database Analytics Models

Alpine Data Labs, or ADS, didn’t invent the concept of in-database predictive analytics. SAS Institute has been collaborating with data warehouse vendors Netezza (now part of IBM) and Aster Data (since acquired by Teradata) to inject predictive modeling and analytics capabilities into their MPP analytic databases since last summer.

But Alpine Data Labs, based in San Mateo, Calif., says it has found a way to make it easier and faster to create the analytics models that make in-database predictive analytics possible. A handful of venture capitalists, including Mission Ventures, apparently agree. ADS recently announced it received $7.5 million in Series A funding.

The funding will be used to help Alpine Data Labs increase staff in China and the United States and accelerate product development and sales, ADS said in a statement. ADS was spun out of data warehouse vendor Greenplum, which itself was then acquired by EMC, last year.

The main benefits of in-database analytics are speed and breadth. Analytic models reside inside the data warehouse next to the data, removing the need to extract data from the warehouse and moving it into a separate analytic application. Extracting and loading data into a separate application can take weeks or longer and, as a consequence, companies often end up extracting only small samples of their data in order to speed this process up. With in-database analytics, it is easier to run analytics against entire data sets.

Catalina Marketing CEO Eric Williams told me last June, for example, that he is able to run predictive analytics against large volumes of customer data, which reside in a Netezza TwinFin data warehouse appliance, 30% to 40% faster than before thanks to in-database analytics technology.

Still, creating the analytic models that are then inserted into the data warehouse requires significant expertise in data science and in the analytic tools themselves. Though it has taken strides to improve its functionality, SAS, for example, has long had a reputation as a complex modeling tool. This is where ADS has a chance to distinguish itself.

ADS CEO Anderson Wong says his company’s core product, called Alpine Miner, uses simple, intuitive visualization tools to allow non-data scientists to easily create predictive analytics data models without having to write a single line of code. Combined with Alpine Miner’s cloud-based, on-demand delivery model, Wong says ADS makes getting up and running with in-database analytics even easier, faster and less expensive than before. This means companies can us in-database analytics to more easily experiment with “what-if” scenarios and make business decisions faster than with competing offerings.

While intriguing, ADS is scant on details on how its analytic modeling tools work. I’m anxious to see a demo. With only 10 customers in production, there aren’t many customer proof-points to turn to either. Complicating matters further is the data warehouse vendor landscape, which recently went through a significant period of consolidation.

With three of the leading next-generation data warehouse vendors (Greenplum, Netezza and Aster Data) having been acquired in the last year by larger software and infrastructure vendors (EMC, IBM and Teradata, respectively), ADS may find it difficult to strike up partnerships. Teradata, for example, already has a deal in place with SAS on in-database analytics, and IBM is using technology from data mining specialist SPSS, which it acquired in 2009, to pursue in-database analytics on its own. The most likely partner for ADS is EMC, ADS having been developed as a unit of Greenplum.

But if Alpine Miner is as easy to deploy and use as Wong says it is, customers may be willing to take on the task of integrating the tool with its data warehouse of choice with out the blessing of their data warehouse vendor. ADS also says Alpine Miner offers “platform neutrality,” meaning it is compatible with any data warehouse. Again, if accurate, that gives ADS a big advantage over proprietary tools.

About Jeffrey Kelly

As Wikibon’s lead Big Data analyst, Jeff Kelly applies a critical eye to trends and developments in the Big Data and business analytics markets, with a strong focus on helping practitioners deliver business value. Jeff’s research includes market analysis, emerging technologies, enterprise Big Data case studies, and more. He also appears frequently on theCUBE to share his insights. Prior to joining Wikibon, Jeff spent seven years as a writer and editor at TechTarget, where covered a number of business and IT topics including IT services, mobile computing, data management and business intelligence. He holds a BA from Providence College and an MA from Northeastern University.