134
9 Hazard Identification
Fig. 9.8 Big data versus
thick data
9.2.2 Big Data and IoT
Big data and internet of things (IoT) are going to become common features in all sorts
of mining activities. They will help define better ranges for reliability and failure of
a system’s elements, and make it possible to search world-wide occurrence of nearmisses and losses, news, etc. At the other end of the spectrum, thick data are useful
to understand deep motivations and can foster SLO and CSR by fostering proper
communication.
Big data and Thick data are actually two faces of a coin (https://www.riskope.
com/2017/05/24/big-data-or-thick-data-two-faces-of-a-coin/) which can be defined
as follows (Fig. 9.8):
• Big data is a term for large or complex data sets that traditional software has
difficulties processing. Processing generally involves, for example, capture,
storage, analysis, curation, searching, sharing, transferring, visualizing, querying, updating, etc. However, the term Big data also often refers to the use of
predictive analytics, user behaviour analytics or certain other advanced data analytics methods. Analysis of data sets can find new correlations to spot business
trends, prevent diseases, combat crime and so on.
• Ethnographers and anthropologists, adept at observing human behaviour and its
underlying motivations, generate thick data. Thick data is qualitative information
that provides insights into the everyday emotional lives of a given population.
To date, different people have supported and used big data and thick data. These
are organizations grounded in the social sciences (thick data) versus corporate IT
functions (big data). This constitutes a perfect example of silo culture: Big data
(http://www.riskope.com/2014/05/22/big-data-and-risk-assessment/) and thick data
should “talk to each other”, but most of the time do not because of silo culture.
9 Hazard Identification
Fig. 9.8 Big data versus
thick data
9.2.2 Big Data and IoT
Big data and internet of things (IoT) are going to become common features in all sorts
of mining activities. They will help define better ranges for reliability and failure of
a system’s elements, and make it possible to search world-wide occurrence of nearmisses and losses, news, etc. At the other end of the spectrum, thick data are useful
to understand deep motivations and can foster SLO and CSR by fostering proper
communication.
Big data and Thick data are actually two faces of a coin (https://www.riskope.
com/2017/05/24/big-data-or-thick-data-two-faces-of-a-coin/) which can be defined
as follows (Fig. 9.8):
• Big data is a term for large or complex data sets that traditional software has
difficulties processing. Processing generally involves, for example, capture,
storage, analysis, curation, searching, sharing, transferring, visualizing, querying, updating, etc. However, the term Big data also often refers to the use of
predictive analytics, user behaviour analytics or certain other advanced data analytics methods. Analysis of data sets can find new correlations to spot business
trends, prevent diseases, combat crime and so on.
• Ethnographers and anthropologists, adept at observing human behaviour and its
underlying motivations, generate thick data. Thick data is qualitative information
that provides insights into the everyday emotional lives of a given population.
To date, different people have supported and used big data and thick data. These
are organizations grounded in the social sciences (thick data) versus corporate IT
functions (big data). This constitutes a perfect example of silo culture: Big data
(http://www.riskope.com/2014/05/22/big-data-and-risk-assessment/) and thick data
should “talk to each other”, but most of the time do not because of silo culture.