Alpine Delivers the Next Generation of its Chorus Advanced Analytics Platform to Connect Machine Learning to Business Behavior

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Alpine_Data_logoAlpine Data announced Chorus 6, a significant enhancement to its award-winning integrated analytics platform that brings machine learning, data, and people together to create operational solutions for business users. Unlike other platforms that focus primarily on algorithms, Chorus uniquely adds collaboration and governance to machine learning projects. The innovations in Chorus 6 serve two goals: to shorten the path from data to action, and to provide a clear, repeatable process for connecting data science to business outcomes.

Traditional enterprises have spent billions of dollars on big data projects in an effort to fundamentally improve the way they compete. But the vast majority is nowhere near changing their behavior, and is failing to capture any real value from those investments. Too often, efforts are derailed by business indifference, the search for the “right” algorithm, and battles over data infrastructures and governance.  As a result, most organizations experience significant friction in the process. According to Forrester Research, “…While 74% of enterprise architects say they aspire to be data-driven, only 29% say their firms are good at translating the resulting analytics into measurable business outcomes.”1 To generate far greater value from big data, organizations need to view data science and machine learning not as a project, but as integrated into the fabric of how they operate.

Clearer Collaboration Between Business Leaders and Data Scientists

Chorus 6 delivers new capabilities that help business leaders manage a repeatable process for connecting machine learning to business behavior. Chorus 6 adds new collaboration and project management capabilities to make it easier and clearer to organize teams of people to take a data science project from inception to completion.

Chorus 6 provides data science teams with greater options to develop and deliver analytic assets more quickly. Chorus integrates Jupyter Notebooks, allowing for rapid exploratory data analysis in Python. Chorus provides APIs to quickly and easily pull data from the relational databases and Hadoop clusters it has configured. Notebooks are executed, persisted, and versioned server-side so that teams can easily collaborate in Python, and team leads have visibility into the work underway.

The innovations in Chorus 6 reflect that fact that successful machine learning strategies start with a well-posed business problem, and more than ninety percent of the value in machine learning is created outside the algorithm,” said Steven Hillion, Chief Product Officer, Alpine Data. “The beauty of Chorus 6 is enabling enterprise teams to organize and apply rigor to the data science process all the way from defining the business problem, through to an operational solution.”

Enabling Data Science Teams to Deliver More Value, Faster

Chorus 6 also provides a new integration that brings Trifacta’s leading user experience for data transformation and cleansing to the Chorus platform. Trifacta allows analysts and data scientists to more productively discover, structure, clean, enrich, validate and publish data for analysis.

Chorus addresses a large unmet need in the enterprise by managing data science projects from end-to-end and involving business stakeholders. By connecting natively to Hadoop, Chorus makes it easy to build machine learning models on data transformed and cleansed in Trifacta Wrangler Enterprise.” Wei Zheng, VP, Product, Trifacta.

And Chorus 6 makes it easier to leverage Alpine Touchpoints to operationalize analytic assets that are useful to the organization. Touchpoints is an industry-first application services layer that enables business users to interact with complex analytic assets and incorporate them into their existing activities and workflows. Touchpoints transforms the genius of data science into business assets that enrich the activities of the people who drive business value every day.


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