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

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Data Management

Don't depend on data scientists for data management.

Let your data scientists focus on data science, not data management. For analysis and statistics on enterprise data to provide insight and ROI, data needs to be properly managed first, and then managed and cleansed over time. Currently, many data scientists spend much of their time not manipulating and analyzing data, but rather trying to simply cleanse and ready it for analysis. Focus on data management architecture that consolidates control and access of data as much as possible.

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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