5 Tips for Helping Your Data Scientists Do Their (Intended) Jobs

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Software Interface

Don't be fooled by visualizations.

We're all human, and a dazzling interface can be deceiving to even the most seasoned professionals. But for business teams selecting analytics software, you can't let the UI dictate the final purchase. While sophisticated visualization tools are a core fundamental of good analytics software, the data being analyzed is still more important. As the analytics market becomes more accessible to non-technical teams, it is more important than ever to loop in technical experts on product evaluation to ensure the data being processed is the right data rather than just the data that happens to be most accessible.

The data scientist has practically become a mythical creature in business lore. Revered, rare, and – purportedly -- supernatural in their abilities. As early as 2012, the data scientist was lauded as the "Sexiest Job of the Century." However, as Big Data has matured into a household term, businesses have begun to transition away from the hype. In many cases, ambitious analytics projects and big budgets have melted away, revealing the day-to-day -- and unforeseen -- problems of implementation.

The problem is that many of the world's data scientists find themselves not so much massaging data for insight, as simply trying to manage data to render it workable. In short, data scientists are not getting to spend much time being data scientists.

Their work is not simply about crunching numbers; it's a nuanced and multi-faceted field that combines equal parts of imagination, curiosity, and technical skill. But it has become clear that even the best data scientists need extensive support from business and IT units in order to perform their true role. In this slideshow, ZL Tech has identified some tips for making it happen.


Related Topics : Business Intelligence Vendors, Data Quality, Data Warehousing, Key Performance Indicators, Knowledge Management

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