Can you keep a cloud-powered data lake from becoming a data swamp?

Let’s face it: Making business decisions on chance or a hunch is so 20th century. In the past fifteen years, data-driven decision-making has become standard for modern enterprises, all of which can generate millions (or billions) of business data points every year. But how do you store such a vast amount of data? And what’s the best way to use it?

Naturally, enterprises have turned to the cloud to pool together their ever-growing collection of raw business data. Dubbed the “data lake”, these cloud-based data repositories house all business-relevant data in one place, all of which are accessible in their native formats. Having a data lake with terabytes (or even petabytes) of data allows business analysts and data miners to sift for key learnings that can help improve product development, employee workflows, or even find a new marketing angle.  

However, there are pitfalls along the way. If the data is poorly organized or saved in multiple formats, for example,  the entire data lake can stagnate. Slowing down the intake of data in the beginning or automating a data identification system at the start can allow an early course correction to keep a data lake from devolving into a data swamp.

Learn more about data lakes and data swamps on CIO.com.

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