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no single standard of how big a dataset needs to be considered big. And that standard can vary between industries and applications.
An example of one firm’s use of big data is provided by GE—which now collects
50 million pieces of data from ten million sensors everyday (Hardy 2014). GE
installs sensors on turbines to collect information on the “health” of the blades.
Typically, one gas turbine can generate 500 gigabytes of data daily. If the use of that
data can improve energy efficiency by 1%, GE can help customers save a total of
$300 billion (Marr 2014).
The velocity dimension refers to the capability to acquire, understand, and
respond to events as they occur. Sometimes it is not enough just to know what has
happened; rather we want to know what is happening. We have all become familiar
with real-time traffic information available at our fingertips. Google Maps provides
live traffic information by analyzing the speed of phones using the Google Maps
app on the road (Barth 2009). Based on the changing traffic status and extensive
analysis of factors that affect congestion, Google Maps can suggest alternative
routes in real time to ensure a faster and smoother drive.
For analysts interested in retailing, anticipating the level of sales is important.
Brynjolfsson and McAfee (2012) report on an effort to monitor mobile phone traffic
to infer how many people were in the parking lots of a key retailer on Black
Friday — the start of the holiday shopping season in the United States — as a means
to estimate retail sales.
Variety, as a dimension of big data, may be the most novel and intriguing of these
three characteristics. For many of us, data referred to numbers meaningfully
arranged in rows and columns. For big data, the reality of “what is data” is wildly
expanded. The following are just some of the types of data available to be converted
into information:
• Financial transactions
• The movement of your eyes as you read this text
• “Turns of a screw” in a manufacturing process
• Tracking of web pages examined by a customer
• Photos of plants
• GPS locations
• Text
• Conversations on cell phones
• Fan speed, temperature, and humidity in a factory producing motorcycles
• Images of plant growth taken from drones or from satellites
• Questions
The variety dimension is closely linked to the discussion of “what is or can be
agricultural data?” presented earlier in this chapter. Essentially digital technologies,
including those employed in precision agriculture practices, are capturing information as explicit data which previously could only be observed or sensed. Furthermore,
in many cases, this process can be accomplished at costs which are economically
justifiable. Often times, that newly available data can be directly employed without
further analysis. In other instances, the effective use of that information requires the
8 Digital Technologies, Big Data, and Agricultural Innovation
no single standard of how big a dataset needs to be considered big. And that standard can vary between industries and applications.
An example of one firm’s use of big data is provided by GE—which now collects
50 million pieces of data from ten million sensors everyday (Hardy 2014). GE
installs sensors on turbines to collect information on the “health” of the blades.
Typically, one gas turbine can generate 500 gigabytes of data daily. If the use of that
data can improve energy efficiency by 1%, GE can help customers save a total of
$300 billion (Marr 2014).
The velocity dimension refers to the capability to acquire, understand, and
respond to events as they occur. Sometimes it is not enough just to know what has
happened; rather we want to know what is happening. We have all become familiar
with real-time traffic information available at our fingertips. Google Maps provides
live traffic information by analyzing the speed of phones using the Google Maps
app on the road (Barth 2009). Based on the changing traffic status and extensive
analysis of factors that affect congestion, Google Maps can suggest alternative
routes in real time to ensure a faster and smoother drive.
For analysts interested in retailing, anticipating the level of sales is important.
Brynjolfsson and McAfee (2012) report on an effort to monitor mobile phone traffic
to infer how many people were in the parking lots of a key retailer on Black
Friday — the start of the holiday shopping season in the United States — as a means
to estimate retail sales.
Variety, as a dimension of big data, may be the most novel and intriguing of these
three characteristics. For many of us, data referred to numbers meaningfully
arranged in rows and columns. For big data, the reality of “what is data” is wildly
expanded. The following are just some of the types of data available to be converted
into information:
• Financial transactions
• The movement of your eyes as you read this text
• “Turns of a screw” in a manufacturing process
• Tracking of web pages examined by a customer
• Photos of plants
• GPS locations
• Text
• Conversations on cell phones
• Fan speed, temperature, and humidity in a factory producing motorcycles
• Images of plant growth taken from drones or from satellites
• Questions
The variety dimension is closely linked to the discussion of “what is or can be
agricultural data?” presented earlier in this chapter. Essentially digital technologies,
including those employed in precision agriculture practices, are capturing information as explicit data which previously could only be observed or sensed. Furthermore,
in many cases, this process can be accomplished at costs which are economically
justifiable. Often times, that newly available data can be directly employed without
further analysis. In other instances, the effective use of that information requires the
8 Digital Technologies, Big Data, and Agricultural Innovation