=Paper=
{{Paper
|id=Vol-2761/HAICTA_2020_paper74
|storemode=property
|title=Business Continuity Data Analysis for Key Business Functions in Dairy Farms and Cattle Feed Yard Industries
|pdfUrl=https://ceur-ws.org/Vol-2761/HAICTA_2020_paper74.pdf
|volume=Vol-2761
|authors=Athanasios Podaras
|dblpUrl=https://dblp.org/rec/conf/haicta/Podaras20
}}
==Business Continuity Data Analysis for Key Business Functions in Dairy Farms and Cattle Feed Yard Industries==
https://ceur-ws.org/Vol-2761/HAICTA_2020_paper74.pdf
Business Continuity Data Analysis for Key Business
Functions in Dairy Farms and Cattle Feed Yard
Industries
Athanasios Podaras1
1
Department of Informatics, Faculty of Economics, Technical University of Liberec, Czech
Republic΄ e-mail: athanasios.podaras@tul.cz
Abstract. The present contribution seeks to infer new business continuity
management knowledge based on multidimensional data analysis in the field of
agriculture. The research relies on real business continuity data regarding the
Dairy Farms and the Cattle Feed Yards industries. The utilized data sets enable
the formulation of domain business continuity management data hierarchy
schemas including dimensions and facts for efficient aggregate data analysis
based on online analytical processing operations. The multidimensional models
are proposed for the efficient business continuity management policies in the
selected industries. Specific business continuity reports are also presented as
examples of the possible multidimensional data analysis outputs in the
investigated agricultural industries.
Keywords: agriculture; business continuity management; key business
functions; data set; dairy farms; cattle feed yard; online analytical processing.
1 Introduction
In the modern industry, business decisions and key business functions are fully
supported by information and communication technologies (ICT’s). The process
automation, the digital services provision, the information sharing through multiple
systems, channels and platforms are just some selected examples through which the
importance of ICT has been proven.
One of the advantages of digitization, is the possibility to extract data from multiple
sources and process it in such a way that efficient decisions can be made in terms of
the company’s competitive advantage. Especially in agriculture, the assistance of
computerized systems and the various data collection and processing tools can
significantly boost agricultural productivity (Lytos et al, 2020).
However, the computerized processes are highly exposed to various threats which
can cause their unplanned interruptions. Business Continuity Management (Watters,
2014) aims to the rapid restoration of all the critical systems and key business functions
in the enterprises, the public organizations and the industry. Business Impact Analysis
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(Tamminedi, 2010), which is a crucial part of the BCM, deals with the efficient
settlement of recovery priorities based on the criticality of each key business function.
“Data collection is an important activity throughout the BCM development process”
(Engemann, 2012). A limitation regarding the research around the agricultural
business continuity strategies is the lack of available data sets. Despite this limitation,
research outputs regarding the business continuity discipline in the agricultural
domain, can be based on small data sets following the statement of (Coble et al, 2018)
that for understanding the concept of big data in agriculture one should first explore
small data.
In the current work, an effort to explore a small data set regarding business
continuity management in dairy farms and cattle feed yard industries is attempted. The
data has been gathered from the Panhandle Regional Planning Commission in Texas
(PRPC (a), n.y; PRPC (b), n.y). The contribution includes conducted reports regarding
two key business functions, namely information technologies (IT) and business
administration (BA).
The collected data has been imported to and processed in MS Excel 2013. Online
Analytical Processing (OLAP) operations have been utilized in order conduct
interesting primary results with respect to the business continuity policies in these two
agricultural industries.
2 Research Motivation
The primary concern which has triggered the current investigation stems from a
research conducted with respect to the application of business continuity management
in the Czech agricultural enterprises, where it has been concluded that “agrarian
organizations are not interested in applying BCM according to standards” (Hajek and
Urbancova, 2013).
Additionally, the recent developments regarding the COVID-19 outbreak has
triggered the necessity for rethinking the importance of business continuity
management in the agricultural industry. According to a recent statement of the Food
and Agriculture Organization of the United Nations (FAO, 2020), “the pandemic is
impacting global food systems, disrupting regional agricultural value chains, and
posing risks to household food security”.
Due to the fact that a special BCM concern is dealing with epidemics, agricultural
BCM data can be a valuable tool for improving related policies regarding the
restoration of unexpectedly interrupted key business functions, even those which are
related to information and communication technologies. However, the “amount of
information risk at the dairy farms is small, but not zero” (Dynes, 2009). Such research
outputs cannot be ignored bearing in mind that multiple hazards can influence ICT’s
in the dairy as well as the cattle feed yard industry.
Based on the above, it has been considered necessary to collect real business
continuity data regarding ICT - related key business functions from the agricultural
domain in order to examine the recovery policies, including the consideration of
various hazards which mainly threaten the uninterrupted operation of the agricultural
companies. Data from two industries, namely the dairy farms and the cattle feed yard,
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has been currently spotted and analyzed with the help of online analytical processing
(OLAP) operations.
2 Tools and Methods
2.1 Data Collection
As above stated, the current research has been based on two BCM guides regarding,
a) the Dairy Farms Industry and b) the Cattle Feed Yard Industry in Texas, Panhandle
region. Both guides have been downloaded from on the web site of the Panhandle
Regional Planning Commission (PRPC (a), n.y, PRPC (b), n.y). Both BCM templates
include two different tables namely, the Key Business Functions (Fig.1) and b) the
Hazards, Impacts and Risk Mitigation Measures (Fig.2).
Fig. 1. The Key Business Functions Data Set for the Dairy Farm and the Cattle Feed Yard
Industries. (Source: Authors’ work in MS Excel based on BCM Data from the Panhandle
Regional Planning Commission).
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Fig. 2. The Hazards, Causes, Risk Exposure and KFs Impacted Data Set (Source: Authors’ work
in MS Excel based on BCM Data from the Panhandle Regional Planning Commission)
The present work seeks to discover information regarding IT and business
administration business functions which are both based on the continuous operation of
information systems, networks and databases. In order to achieve this goal, the
dimensions and hierarchies of the involved data had to be considered. They are utilized
as input to the formulation of online analytical processing reports. The software tool
used for OLAP analysis is MS excel 2013.
2.2 Data Hierarchies
One of the most important tasks related to the effective data analysis is the
observation of possible hierarchies. The specific task is crucial when databases entities
and business intelligence data warehouses are designed. The main source for defining
data hierarchies is either the users’ aspect and requirements or the existence of a data
set from which hierarchies can be discovered. After spotting the hierarchies, the
corresponding schemas can be generated.
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Strict Hierarchies: According to Vaisman and Zimanyi (2014), hierarchy types can
be strict and non-strict. In the former type and the relationship type between parent and
child levels is one to many.
Non-Strict Hierarchies: in the occasion when a many-to-many relationship exists
between a higher and a lower level then the hierarchy type is non-strict.
2.2 Online Analytical Processing (OLAP)
In business intelligence systems, multidimensional schemas are used in order to
analyze data based on multiple dimensions, different granularity levels (hierarchy
levels or levels of detail for each dimension), measurements and facts (else called
numeric measurements) (Grossman and Rinderle-Ma, 2015). The authors describe the
data cube as a “common metaphor” which successfully delineates such schemas, while
(Kimball and Ross, 2010) support that multidimensional schemas enable “diving the
world in measurements and context”. The most representative OLAP operations which
are accompanied by aggregate measurements (mean, count, max, avg) are:
• Drill-down and Rollup: navigate through different granularity levels
• Slicing and Dicing: maintaining granularity levels but modifying the cube
size, and
• Pivoting: rotating between rows and columns and measure facts from
different perspective.
For the current research, MS Excel Pivot Tables have been incorporated in order
to generate useful OLAP reports.
3 Results and Discussion
3.1 Hierarchies and Multidimensional Schema based on the Key Business
Functions Data Set
The following multidimensional hierarchy schema (Fig.3) has been determined with
respect to the Key Business Functions data set (Fig.1):
Non-Strict Hierarchy (n:m relationship): Industry Key Business
Function Sub-Function Department Criticality
Ranking.
In this case, the dimensions which are defined are: Industry which includes the
granularity levels Key Business Function, Sub Function and Departments and, the
dimension Criticality Ranking. Determined Facts are the Number of Functions, Sub-
Functions, Departments and Criticality Ranking.
Respectively, the following multidimensional hierarchy schema (Fig.4) has been
determined with regard to the Hazards, data set (Fig.2):
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Non-Strict Hierarchy (n:m relationship): Industry Hazards Cause
Risk Exposure.
The core dimensions which have been observed are the Industry, Hazards and Risk
Exposure, while the core fact is the number of impacted KBFs.
Fig. 3. The Key Business Functions Hierarchy Schemas
Fig. 4. The Hazards, Risk Exposure and Impact Multidimensional Schema
3.2 OLAP Operations
The following OLAP operations have been selected:
Data set 1: A selected report includes a pivot analysis of the number of Departments
and their Criticality Ranking for both industries. The following illustrations depict
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different perspectives of the analyzed data due to the rotation of rows and columns
(Pivoting Operation) (Fig. 5, Fig. 6)
Count of Column
Department Labels
Least Moderately Grand
Row Labels Critical Critical Total
Cattle
Feedyard 5 4 9
Dairy Farms 9 9
Grand Total 14 4 18
Fig. 5. Total Number of Critical Departments in Each Industry.
Count of Column
Department Labels
Cattle Dairy Grand
Row Labels Feedyard Farms Total
Least
Critical 5 9 14
Moderately
Critical 4 4
Grand
Total 9 9 18
Fig. 6. Total Number of Industry Departments which are Critical.
Moreover, with the help of the Slicer Excel functionality, data can be further filtered
according to the desired granularity level, namely Industry or KBF. The following
example includes results for the percentage of critical departments for each industry
when filtered according to the Industry level. In our example, 43% of the Least Critical
Departments belong to the Business Administration KBF while 57% of the Least
Critical Departments belong to the Information Technology KBF. By filtering the data
according to the Industry level Dairy Farms, the ratio is modified, since 67% of the
Least Critical Departments belong to the BA and 33% belong to the IT KBF. (Fig.7)
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Information Least Critical Business
Technology Administration /
33% Personnel
67%
Business Administration /
Personnel
Information Technology
Fig. 7. The Ratio of the Least Critical Departments which belong to IT and BA KBFs filtered
by the Industry (Dairy Farms).
Another selected OLAP operation concerns the Hazards, Impacts and Risk Exposure
Data set. The data has been analyzed according to the Hazard that affects the two
selected Key Business Functions, its Cause, and the Number of all the KBFs impacted1
Moreover, the data is filtered according to KBF (IT, BA) and Industry (Fig. 8). The
illustrated example includes the Pivot Analysis of the Hazard (Columns), Cause
(Rows), Number of affected KBFs for the Cattle Feed Yard Industry and the Business
Administration KBF.
Fig. 8. Pivot Analysis of the Hazard (Columns), Cause (Rows), Number of affected KBFs for
the Cattle Feed Yard Industry and the Business Administration KBF.
The inferred results indicate that OLAP operations are tools that can be utilized as
drivers towards crisis situations and can support effectively crucial decisions related
to the formulation of effective agricultural business continuity policies. The selected
data can be further utilized as input for data mining predictions too.
1
All the involved business functions are of the selected industries/.
507
4 Conclusions
The current research has been conducted in order to highlight the importance of
efficient data utilization towards the formulation of efficient business continuity
management (BCM) strategies in the agriculture field. The data which has been
utilized concerns the business continuity policy in two selected agricultural industries
in Texas, namely the Dairy Farms and the Cattle Feed Yards. From the collected data,
multidimensional schemas, hierarchies and selected OLAP operations have been
incorporated in order to generate useful BCM reports for two key business functions,
namely Information Technologies and Business Administration. An interesting OLAP
analysis of the criticality ranking as well as the hazards and the vulnerability of the
selected ICT-related functions has been conducted in MS Excel. Future research steps
aim to analyze the total of the KBFs and infer significant predictive BCM results in
the selected industries with the help of data mining tools.
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