Posts Tagged ‘Proof of Concept’

Data Science Lessons: Insights from an Agricultural Proof of Concept

Agriculture has come a long way from ancient times through the industrial revolution to the current digital era. In 2017, modern agricultural organizations have access to increasingly large amounts of data collected by sensors from soil quality measurements, weather sensors, GPS guided machinery, and more. According to a USDA’s recent survey, more than 60 percent of corn and soybean crops are monitored by data collection devices (source). However, there is still a substantial gap between the potential of utilizing this data and what happens in reality. Despite having the data, many companies lack the capability to effectively process, analyze, and efficiently build informative models in order to make data-driven decisions.

That’s where guidance from data service providers, such as Virtustream, can help. Virtustream provides data management expertise, tools and data science consulting to enable customers across different industries to get value from their data resources.

Our data science team in Dell IT recently initiated a Data-Science-as-a-Service Proof of Concept (PoC) as part of a Virtustream service engagement with a large company that plants thousands of farms across USA. Virtustream had enabled the company to become more data-driven by harnessing its large amounts of data, as well as developing and implementing different applications that enable scalable, faster, and more accurate operations – operations that couldn’t be executed with existing tools. Our PoC sought to demonstrate the speed and efficiency of those analytics applications.

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Framing the Data Science Proof of Concept

Brahma Tangella

Brahma Tangella

Sr. Manager, Service Strategy, Dell IT

Whether companies refer to results, outcomes, ROI, or case studies, Big Data and data science are finally moving beyond the hype and proving to deliver dividends over time. Several new Big Data technologies and predictive tools have been launched to meet the growing demand within business and technology groups to harness the constant growth of both structured and unstructured data within and outside of the enterprise. But such technologies and tools won’t be effective unless you define the problem to be addressed.

Most data science initiatives start with a proof of concept (PoC) or in some cases with a proof of value (PoV) if the foundational concept is clearly established. Developing a pipeline of PoC’s can be extremely helpful through working sessions with data scientists, business subject matter experts (SME’s), data experts, and leaders. Following this, prioritize PoCs by stack-ranking each of them based on business value and ease of implementation which factors in availability of data, granularity, and quality.

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