Collaborative Analytics and the Benefits of Local Language Support

January 23, 2012
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local language support for collaborative analytics 300x243 photo (advanced analytics)Enterprise companies rely on data scientists from a range of geographies and backgrounds.

local language support for collaborative analytics 300x243 photo (advanced analytics)Enterprise companies rely on data scientists from a range of geographies and backgrounds. To help keep analytics teams on the same page, it’s useful for analytics team members to use the same analytics tools. This can and should include tools that offer local language support  for data scientists who speak Chinese, Russian, Japanese, etc.

The ability for companies to build and draw upon geographically-dispersed collaborative analytics teams has become essential to business success, if not survival. A Frost & Sullivan report reveals that collaboration is a cornerstone of business performance. For instance, 36% of a company’s business performance is tied to its “collaboration index,” according to the report. By comparison, this is more than two times the impact of a company’s strategic direction (16%) and more than five times the impact of market and technological turbulence influences (7%).

Local language support can also help geographically-dispersed analytics teams collaborate on solutions for addressing and targeting local market issues. For instance, analytics teams can use the same set of analytics tools and applications to help identify customer needs and preferences in a particular market (e.g. 18-to-29-year-old females in Tokyo) and then use demographic, transactional, and other customer information to identify the right types of offers and marketing campaigns.

Local language support can help bolster collaborative analytics in other ways. Thanks to the widespread availability of high-speed networks across the globe, companies are increasingly relying upon virtual employees with specific skills who are able to do their jobs well regardless of location.

Because of this, organizational staffs are becoming more geographically dispersed. As such, when companies have decision-support issues to tackle, they don’t necessarily have to rely on fixed teams of data scientists in the same location. The work can be parsed out based on skill sets, time zone synchronization, etc. As a joint paper on the topic by Carnegie Mellon University and Singapore Management University illustrates, globally distributed software projects like these can yield multiple productivity and financial benefits.

And as a recent white paper by Verizon Business notes, “geographically dispersed teams work more cohesively and make better and quicker decisions when they can connect on the fly.” Further, virtual teams “can operate with greater agility to navigate through difficult market conditions and explore new opportunities.”

When far-flung analytics team members can use the same set of tools to communicate and work in concert with one another, the likelihood of achieving successful outcomes is greatly enhanced.