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

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Product Acquisitions

Keep data scientists in the loop for product acquisitions.

In business, it's not uncommon for purchases to be made by those who aren't necessarily the practitioners who will be using the product. In the case of analytics, this can be a critical – and expensive – mistake. In order for analytics professionals to successfully do their job, they depend on the quality of data and the capacity for an analytics product to handle both the scale and variety of content involved. Sticker price isn't everything; use the expertise of your data scientist to evaluate whether the tool or platform will be appropriate for the job and a good value.

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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