218
application of newly available analytical approaches – the analytics dimension of
big data.
Some agriculturally oriented scholars (Coble et al. 2016; Weersink et al. 2018)
will include veracity as a fourth dimension of big data. In this context, veracity
references data quality that is employed within big data analyses. Indeed agricultural analysis has a long tradition which emphasizes the need for accurate data, with
the oft-used phrase “Garbage in; garbage out,” exemplifying this concern. However,
as will be discussed in the following section, Analytics, the tools and techniques can
produce useful insights from less than perfectly accurate data. Therefore veracity is
not included here as a big data dimension.
8.4.1 Analytics
Access to lots of data, generated from diverse sources with minimal lag times,
sounds attractive. Managers, however, quickly will ask, “What do I do with all this
stuff?” Without similar advances in analytic capabilities, just acquiring more data is
unlikely to have significant impact within agriculture.
Analytics and its related, more recent term, data science, are key factors by
which big data capabilities can actually contribute to improved performance in the
agricultural sector. Data science refers to the study of the generalizable extraction of
knowledge from data (Dhar 2013). Tools based upon data science are being developed for implementation in the sector, although these efforts are at their early stages.
The associated concept of analytics similarly is maturing and its use refined
(Davenport 2013; Watson 2013). Analytic efforts can be categorized as being of one
of three types:
• Descriptive efforts focus on documenting what has occurred.
• Predictive efforts explore what will occur.
• Prescriptive efforts identify what should occur (given the optimization algorithms employed).
One tool providing predictive capabilities was recently unveiled by the giant
retailer, Amazon (Bensinger 2014). This patented tool would enable Amazon managers to undertake what it calls “anticipatory shipping,” a method to start delivering
packages even before customers click “buy.” Amazon intends to box and ship products it expects customers in a specific area will want but have not yet ordered. In
deciding what to ship, Amazon’s analytical process considers previous orders, product searches, wish lists, shopping-cart contents, returns, and even how long an
Internet user’s cursor hovers over an item.
Relative particularly to agricultural applications and analytics, two key points
warrant specific consideration:
• The first continues the veracity discussion introduced in the prior section. Of
course, it is prudent to strive to capture and use data which is accurate. However,
S. T. Sonka
application of newly available analytical approaches – the analytics dimension of
big data.
Some agriculturally oriented scholars (Coble et al. 2016; Weersink et al. 2018)
will include veracity as a fourth dimension of big data. In this context, veracity
references data quality that is employed within big data analyses. Indeed agricultural analysis has a long tradition which emphasizes the need for accurate data, with
the oft-used phrase “Garbage in; garbage out,” exemplifying this concern. However,
as will be discussed in the following section, Analytics, the tools and techniques can
produce useful insights from less than perfectly accurate data. Therefore veracity is
not included here as a big data dimension.
8.4.1 Analytics
Access to lots of data, generated from diverse sources with minimal lag times,
sounds attractive. Managers, however, quickly will ask, “What do I do with all this
stuff?” Without similar advances in analytic capabilities, just acquiring more data is
unlikely to have significant impact within agriculture.
Analytics and its related, more recent term, data science, are key factors by
which big data capabilities can actually contribute to improved performance in the
agricultural sector. Data science refers to the study of the generalizable extraction of
knowledge from data (Dhar 2013). Tools based upon data science are being developed for implementation in the sector, although these efforts are at their early stages.
The associated concept of analytics similarly is maturing and its use refined
(Davenport 2013; Watson 2013). Analytic efforts can be categorized as being of one
of three types:
• Descriptive efforts focus on documenting what has occurred.
• Predictive efforts explore what will occur.
• Prescriptive efforts identify what should occur (given the optimization algorithms employed).
One tool providing predictive capabilities was recently unveiled by the giant
retailer, Amazon (Bensinger 2014). This patented tool would enable Amazon managers to undertake what it calls “anticipatory shipping,” a method to start delivering
packages even before customers click “buy.” Amazon intends to box and ship products it expects customers in a specific area will want but have not yet ordered. In
deciding what to ship, Amazon’s analytical process considers previous orders, product searches, wish lists, shopping-cart contents, returns, and even how long an
Internet user’s cursor hovers over an item.
Relative particularly to agricultural applications and analytics, two key points
warrant specific consideration:
• The first continues the veracity discussion introduced in the prior section. Of
course, it is prudent to strive to capture and use data which is accurate. However,
S. T. Sonka