Analyst Perspectives

In many organizations, advanced analytics groups and IT are separate, and there often is a chasm of understanding between them, as I have noted. A key finding in our benchmark research on big data analytics is that communication and knowledge sharing is a top benefit of big data analytics initiatives, but often it is a latent benefit. That is, prior to deployment, communication and knowledge sharing is deemed a marginal benefit, but once the program is deployed it is deemed a top benefit. From a tactical viewpoint, organizations may not spend enough time defining a common vocabulary for big data analytics prior to starting the program; our research shows that fewer than half of organizations have agreement on the definition of big data analytics. It makes sense therefore that, along with a technical infrastructure and management processes, explicit communication processes at the beginning of a big data analytics program can increase the chance of success. We found these qualities in the Chorus platform of Alpine Data Labs, which received the Ventana Research Technology Innovation Award for Predictive Analytics in September 2014.

Alpine Chorus 5.0, the company’s flagship product, addresses the big data analytics communication challenge by providing a user-friendly platform for multiple roles in an organization to build and collaborate on analytic projects. Chorus helps organizations manage the analytic life cycle from discovery and data preparation through model development and model deployment. It brings together analytics professionals via activity streams for rapid collaboration and workspaces that encourage projects to be managed in a uniform manner. While activity streams enable group communication via short messages and file sharing, workspaces allow each analytic project to be managed separately with capabilities for project summary, tracking and data source mapping. These functions are particularly valuable as organizations embark on multiple analytic initiatives and need to track and share information about models as well as the multitude of data sources feeding the models.

The Alpine platform addresses the challenge of processing big data by parallelizing algorithms to run across big data platforms such as Hadoop and making it accessible by a wide audience of users. The platform supports most analytic databases and all major Hadoop distributions. Alpine was an early adopter of Apache Spark, an open source in-memory data processing framework that one day may replace the original map-reduce processing paradigm of Hadoop. Alpine Data Labs has been certified by Databricks, the primary contributor to the Spark project, which is responsible for 75 percent of the code added in the past year. With Spark, Alpine’s analytic models such as logistic regression run in a fraction of the time previously possible and new approaches, such as one the company calls Sequoia Forest, a machine learning approach that is a more robust version of random forest analysis. Our big data analytics research shows that predictive analytics is a top priority for about two-thirds (64%) of organizations, but they often lack the skills to deploy a fully customized approach. This is likely a reason that companies now are looking for more packaged approaches to implementing big data analytics (44%) than custom approaches (36%), according to our research. Alpine taps into this trend by delivering advanced analytics directly in Hadoop and the HDFS file system with its in-cluster analytic capabilities that address the complex parallel processing tasks needed to run in distributed environments such as Hadoop.

A key differentiator for Alpine is usability. Its graphical user interface provides a visual analytic workflow experience built on popular algorithms to deliver transformation capabilities and predictive analytics on big data. The platform supports scripts in the R language, which can be cut and pasted into the workflow development studio; custom operators for more advanced users; and Predictive Model Markup Language (PMML), which enables extensible model sharing and scoring across different systems. The complexities of the underlying data stores and databases as well as the orchestration of the analytic workflow are abstracted from the user. Using it an analyst or statistician does not need to know programming languages or the intricacies of the database technology to build analytic models and workflows.

It will be interesting to see what direction Alpine will take as the big data industry continues to evolve; currently there are many point tools, each strong in a specific area of the analytic process. For many of the analytic tools currently available in the market, co-opetition among vendors prevails in which partner ecosystems compete with stack-oriented approaches. The decisions vendors make in terms of partnering as well as research and development are often a function of these market dynamics, and buyers should be keenly aware of who aligns with whom.  For example, Alpine currently partners with Qlik and Tableau for data visualization but also offers its own data visualization tool. Similarly, it offers data transformation capabilities, but its toolbox could be complimented by data preparation and master data solutions. This emerging area of self-service data preparation is important to line-of-business analysts, as my colleague Mark Smith recently discussed.

Alpine Labs is one of many companies that have been gaining traction in the booming analytics market. With a cadre of large clients and venture capital backing of US$23 million in series A and B, Alpine competes in an increasingly crowded and diverse big data analytics market. The management team includes industry veterans Joe Otto and Steve Hillion. Alpine seems to be particularly well suited for customers that have a clear understanding of the challenges of advanced analytics and are committed to using it with big data to gain a competitive advantage. This benefit is what organizations find most in over two thirds (68%) of organizations according to our predictive analytics benchmark research. A key differentiator for Alpine Labs is the collaboration platform, which helps companies clear the communication hurdle discussed above and address the advanced analytics skills gap at the same time. The collaboration assets embedded into the application and the usability of the visual workflow process enable the product to meet a host of needs in predictive analytics. This platform approach to analytics is often missing in organizations grounded in individual processes and spreadsheet approaches. Companies seeking to use big data with advanced analytics tools should include Alpine Labs in their consideration.

Regards,

Tony Cosentino

VP and Research Director