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.
The ease with which we have long been able to retrieve information from the World Wide Web (WWW) using increasingly efficient and high quality search engines underscores a less-than-impressive performance from search engines serving the enterprise environment. Off-the-shelf tools that let organizations retrieve their enterprise information just do not give us the same experience as Google or Bing. But what if you could build your own enterprise information retrieval system by leveraging open source tools and platforms?
In this blog, we will explore the feasibility of doing just that.
In an age when most companies invest to become data-driven, the value of data is increasingly a key criteria for making IT decisions, and the protection of the data becomes paramount to those decisions
When making backup-related decisions, price justification involves the potential capital loss to the organization when a data loss or unavailability occurs. Understanding the value of data and access to that data is key when prioritizing backup technology or even for deciding which infrastructure to protect during a cyber-attack. However, estimating this price is not trivial.
I recently worked on a research project with a team of academic partners at Ben-Gurion University for prioritizing data replication to minimize the monetary loss in the case of a disaster. The method we derived can limit the costs of data loss, and could provide a high return on investment (ROI) of up to one million dollars per incident.
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