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

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Data Quality

Understand that data quantity does not make quality.

Yes, it's (sometimes) true that bigger data is better, but only if that data is representative of what you want to analyze. Analyzing public social media data for customer sentiment will do little if most of the company's negative feedback comes from private customer support emails. Likewise, a sudden dip in sales provides little information on its own; context must be derived from other sources. Skewed data sets, often used due to convenience or lack of other options, can ultimately be more dangerous than no data at all.

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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