Defining Deep Data: What It Is and How to Use It

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Bring in the Data Scientist

When formulating a Deep Data strategy, businesses should take advantage of the emerging breed of data science specialists. A data scientist not only has the specialized mathematical, programming and statistical knowledge to help companies extract business value from the data, s/he also provides valuable insight on the best strategy to help companies meet their goals.

In the era of Big Data, businesses across all industries struggle to efficiently and effectively manage data overload. Many organizations do not know how to pinpoint and extract the value embedded within the heaps of data available to them, and consequently find themselves stuck in the "data hoarding" trap – capturing every piece of available information, rather than focusing on the data that is providing the greatest business value.

Instead of just thinking "big" when it comes to data, companies need to start thinking "deep." The Deep Data framework is based on the premise that a small number of information-rich data streams, when leveraged properly, can yield greater business value at lower cost than vast volumes of data. For example, organizations can use the Deep Data framework to better understand a customer's behavior and provide actionable, scalable insights that simultaneously improve customer engagement and drive economic value of that company's data investment.

What do you need to know to effectively implement a Deep Data strategy? In this slideshow, Badri Raghavan, CTO and chief data scientist at FirstFuel, has outlined valuable insight organizations should consider.


Related Topics : Vulnerabilities and Patches, Resellers, Broadcom, Broadband Services, Supercomputing

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