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An investigation of skill requirements for business and data
analytics positions: A content analysis of job advertisements
Amit Verma*, Kirill M. Yurov
Missouri Western State University
Peggy L. Lane
University of Louisiana Monroe
Yuliya V. Yurova
Nova Southeastern University
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An investigation of skill requirements for business and data
analytics positions: A content analysis of job advertisements
Abstract
Presently, analytics degree programs exhibit a growing trend to meet a strong market demand.
To explore the skillsets required for analytics positions, we examined a sample of online job
postings related to professions such as Business Analyst (BA), Business Intelligence Analyst
(BIA), Data Analyst (DA) and Data Scientist (DS) using content analysis. We present a ranked
list of relevant skills belonging to specific skills categories for the studied positions. Also, we
conducted a pairwise comparison between DA and DS as well as BA and BIA. Overall, we
observed that decision-making, organization, communication and structured data management
are key to all job categories. Our analysis shows that technical skills like statistics and
programming skills are in most demand for Data Scientist. This analysis will be useful for
creating clear definitions with respect to required skills for job categories in the business and
data analytics domain and for designing course curricula for this domain.
Keywords: Data Analytics, Skill Requirements, Content Analysis, IS Curriculum
1. INTRODUCTION
Analytics programs, especially at the graduate level, exhibit a growing trend. There has
been a significant increase in the number of programs and courses offered in analytics (Gellman,
2014). Other labels used for analytics include Business Analytics, Data Analytics and Business
Intelligence (BI). The field of Analytics/Business Intelligence remains an attractive target for IT
investments. According to a recent survey, SIM (Society for Information Management) IT
Trends Study for 2017, “Analytics/Business Intelligence/Data/Mining/Forecasting/Big Data
remained number one on the list of the largest IT investments for the seventh year in a row, and
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IT pros also identified this as the number one area that should get more investment” (Davis,
2017). A related trend is a high demand for analytics skills in the IT profession. Business
intelligence/analytics and Big Data were reported in the group of “the top 10 most sought-after
skills” by Computerworld. These two skills were sought respectively by 26% and 25% of IT
manager-level professionals commenting on their plans to hire IT talent as reported in Forecast
2017 survey administered by Computerworld (Pratt, 2017).
The aforementioned classifications emphasize the importance of analytics skills for
present-day IT talent. Yet, there is a need to provide clarity for the definitions of job categories
as related job requirements can vary significantly. We attempt to develop a job classification
consisting of most representative categories currently in demand in the business and data
analytics domain consisting of Business Analyst, Business Intelligence Analyst, Data Analyst
and Data Scientist. In the present classification, clearly articulated skillsets will be mapped to
the four job categories. By using a content analysis technique to sift through web-based job
postings, we will determine the frequencies of specific skills corresponding to each skillset. The
analysis of the frequencies will help bring to the forefront most critical skills as well as
associated skillsets in our data sample. In this study, we conduct pairwise comparisons between
BA and BIA as well as DA and DS. We are not analyzing similarities and dissimilarities
between all job categories.
The job categories investigated in this study (DA, BA, DS and BIA) represent
respectively such professional domains as data analytics, business analytics, data science and
business intelligence. The definitions of data analytics, business analytics, data science and
business intelligence are closely interrelated. The application context, scale of analytical
activities and multi-disciplinary nature help establish differences among these concepts.
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Aasheim et al. (2015) define data analytics as an extension of statistical analysis to include
capabilities to process large data sets. Further, the authors define business analytics as a
subcategory of data analytics which is applied in business settings to analyze related problems.
Data Science, according to Aasheim et al. (2015), is a multi-disciplinary field closely related to
data analytics, which makes use of computer science techniques to derive insights when
analyzing large data sets. Aasheim et al. (2015) added that the inclusion of business acumen is
an important aspect of the data science field such that insights developed in the course of
analytical activities support the creation of business value. Shirani and Roldon (2009) refer to
business intelligence as an emerging technology domain that emphasizes the use of computer-
based analytical tools such as OLAP, dashboards, scorecards, etc. for solving business problems.
For the present study, a critical factor would be geographical localization as the study
focuses on the demand for job categories in the business and data analytics domain in a selection
of US states. We decided to focus on a limited number of US states to make research effort
relevant for designing/enhancing the requirements for analytics programs at the universities we
teach and, accordingly, we concentrate on the states of Arkansas, Florida, Missouri and Kansas.
Limiting the data sample geographically to these four states allowed us to focus this study
specifically on the job requirements in the local economies and consequently can help educators
compare the offerings of upcoming and existing business and data analytics programs with the
skillsets required in a given local job market. In the present study, we analyzed job postings
from online job boards and we do not attempt to formulate specific recommendations as far as
coursework and related skillsets are concerned.
This study contributes to the literature of curriculum development in business and data
analytics programs by identifying skills currently in demand for a variety of job categories. The
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findings of this study could, therefore, be useful for creation/modification of degree programs in
the higher education industry to better align with current industry needs. In a similar manner, the
results of our analysis can be exploited by employers seeking talent in the domain of data
analytics. Clearly articulated skillsets would help create a more structured approach to
formulating requirements for jobs in the analytics domain.
Our paper is organized as follows. First, we will discuss the extant literature on the topic
of job requirements in the business and data analytics domain. One stream in the literature
focuses on the perspective of employers. The second stream that supports the pedagogical
perspective addresses the link between job requirements and related degree programs. The
classification framework, method and data collection will be discussed next. We relied on
content analysis for analyzing online job postings. The discussion of findings will focus on the
calculated frequencies of skills corresponding to a skillset which in turn is mapped to a particular
job category. In addition, critical similarities/dissimilarities for the two pairs of job categories,
BA/BIA and DA/DS will be discussed. Finally, we will present conclusions and outline
directions for future research.
2. PRIOR RESEARCH
Extant research on the topic of skill requirements for professionals in information
systems, including data analytics, is divided into the perspectives of employers and educators.
For the first perspective, the focus is on identification of the most critical skills and areas of
expertise that are valued by employers (Aasheim, Shropshire, Li and Kadlec, 2012; Debortoli, S.,
Müller and vom Brocke, 2014; De Mauro, Greco, Grimaldi and Nobili, 2016; Kim and Lee,
2016; Lee and Han, 2008; Shirani and Roldan, 2009). Job market demand is typically examined
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via sifting through online job postings to ascertain the requirements for certain job categories.
The proliferation of online platforms for job advertisements has created a readily accessible pool
of data for research purposes. Rich data resources, coupled with an automated way of data
processing via content analysis, offer valuable research opportunities for classification tasks.
The aforementioned stream of research examined skillsets for a variety of job domains.
In the general IS domain, Kim, Hsu & Stern (2006), Todd, McKeen and Brent (1995), Webb
(2006) and Wilkerson (2012) studied job skills required for the IS positions. De Mauro et al
(2016) recognized Business Analyst and Data Scientist as individual job categories in the study
that examined job postings related to Big Data skills. Shirani and Roldan (2009) collected data
from job advertisements to formulate skillsets for BI, data warehousing and database job
categories. Kim and Lee (2016) focused on Data Scientist as a job category and associated
skillset. They found that statistics, data modeling, programming, database systems and
understanding of specific business domains were the main requirements for the data scientist
positions. Kim and Lee (2016) considered DS, DA, BIA and Operations Research Analyst as
closely related occupations. In the present study, we include DS, DA, BIA and BA to represent
job categories in our classification whereas Kim and Lee (2016) concentrated on one job
category as discussed above. Thus, we aim to explicitly detect differences/similarities in
skillsets for a variety of job categories in the analytics domain.
The pedagogical perspective focuses on the analysis of the coursework in the existing
degree programs in the data analytics and related domains (Aasheim, Williams, Rutner &
Gardiner, 2015; Asamoah, Sharda, Zadeh and Kalgotra, 2017; Boyle and Strong, 2006; Mills,
Chudoba and Olsen, 2016). Aasheiim et al (2015) studied course descriptions to identify
similarities and differences in undergraduate programs in Data Analytics and Data Science. This
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collective of authors first collected information, from the extant literature, on skills and
competencies essential for these two fields and presented a resultant classification. Then they
compared skills and competencies included in the curricula among the studied undergraduate
programs using the developed classification. Boyle & Strong (2006) surveyed IT professionals
implementing or supporting Enterprise Resource Planning (ERP) systems in order to develop a
skillset required for the graduates of ERP programs. They concluded that this skillset included
the following competencies: ERP technical knowledge, business functional knowledge,
technology management knowledge, industry exposure to ERP, interpersonal skills and team
skills/knowledge.
Prior research has deployed a variety of approaches for creating classifications
concerning job categories in the analytics domain. One approach focused on a skillset required
for one job category such as Data Scientist and ERP professional. A different approach
compared skillsets taught in undergraduate programs in data analytics and data science. Yet, a
third approach has the overall focus on one skillset such as Big Data for which a job
classification was developed containing several categories. This investigation seeks to make a
contribution to the extant literature by broadening the range of job categories in the analytics
domain. In our classification, we focus on BA, BIA, DA and DS as specific job categories. In
addition to introducing a broader range of job categories, we conduct a pair-wise analysis of
related categories such as BA/ BIA and DA/DS. The extant literature focuses primarily on the
DA/DS pair, whereas the attention to the BA/BIA pair has been scarce. The job category of BA
is of particular interest as the proportion of job advertisements for this category considerably
outweighs the respective shares of other categories. Thus, the findings of this investigation aim
to shed light on the significant job category in the business and data analytics domain that
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remains relatively unexplored. Lastly, the present study contributes to the extant literature by
introducing a novel set of skill categories concerning the BA/BIA/DA/DS job categories.
3. RESEARCH METHODOLOGY
3.1. Classification framework development
In this section, we explain how we have developed a classification framework which is
essentially a common set of skills and competencies that we are going to map against each job
category. We used a list of skills and competencies contained in Aasheim et al (2015) as a
starting point to develop our list. Each component of the aforementioned categorization structure
was analyzed and modified if needed so that perspectives found in prior research and authors’
viewpoint would be reflected. Also, we added more skill categories with associated skills. In
order to measure the reliability of the categorization structure, we asked four independent raters
(university faculty members) to provide their expertise by placing skills and competencies in
appropriate categories. The initial inter-coder reliability based on the alpha coefficient from Kim
and Lee (2016) was 0.918. We will next present categories and associated skills arranged in
Table 1.
Table 1: Classification framework – skill categories and associated skills
Skill Category
Skills
Enterprise systems software
ERP, CRM, SCM, SAP, PeopleSoft, Oracle, Integration,
SAAS
Visualization techniques
Visualization, Tableau, Lumira, Crystal Reports, d3, d3.js
Specialized analytics solutions
Google Analytics, ArcGIS, GIS, QGIS
Programming skills
Mathematical programming, Scala, Python, C#, C++, VB,
Excel Macros, PERL, C, Java, Visual Basic, VB.NET, VBA,
COBOL, FORTRAN, S, SPLUS, BASH, Javascript,
ASP.NET, JQUERY, JBOSS
Project management
Project management, PERT, CPM, PERT/CPM, change
management, project budget, project documentation, PMP,
Microsoft Project, Gannt Chart
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Advanced modeling/analytics
techniques
Neural networks, linear programming, integer programming,
goal programming, queuing, genetic algorithms, expert
systems
Web scraping
Scraping, web scraping, crawling, web crawling
Hardware
Hardware, architecture, devices, printer, storage, desktop, pc,
server, workstation, mainframe, legacy, system architecture
Networking
Internet, LAN, WAN, networking, cloud computing, client
server, distributed computing, network security, ubiquitous
computing, TCP/IP
Statistical packages
Statistics, SPSS, SAS, Excel, Stata, MATLAB, probability,
hypothesis testing, regression, pandas, scipy, sps, spotfire,
scikits.learn, splunk, h2o, R, STATA, Statistical
programming
Data mining techniques
Classification, text mining, web mining, stream mining,
knowledge discovery, anomaly detection, associations,
outlier, classify, association, estimation, prediction,
forecasting, machine learning, decision trees
Structured data management
SQL, relational database, Oracle, SQL Server, DB2,
relational DBMS, Microsoft Access, data model, data
management, entity relationship, data warehouse, DBMS,
transactional database, sql server, db2, Cassandra, mongo db,
mysql, postgresql, oracle db
Big data management
Big Data, Unstructured Data, Data Variety, Data Velocity,
Data Volume, Hadoop, Hive, Pig, Spark, MapReduce,
Presto, Mahoot, NoSQL, Spark, shark, oozie, zookeeper,
flume
Decision making skills
Reporting, analysis, modeling, design, problem-solving,
implementation, testing, analytical, strategic thinking
Communication skills
MS Office, MS PowerPoint, presentation, MS Word,
communication, documentation
Organization skills
Teamwork, matrix, ethics, self-motivated, leadership,
organization, team, manage, interpersonal
Business domain
Finance, healthcare, marketing, supply chain, accounting,
computer science, functional, domain
3.2. Method
Our methodology is summarized in Figure 1. We used data contained in online job
boards. In particular, Indeed.com (www.indeed.com) is one such widely used resource which
allows employers to publish information regarding the positions they advertise. Data collected at
Indeed.com was analyzed using content analysis (Neuendorf, 2016). Content analysis is a
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qualitative method which supports, on the basic level, the tasks of identification of relevant
information and counting the number of occurrences when searching for specific words/phrases.
We used web scraping as the method for data collection. R was the tool used to write the
code for the web scraping. Indeed.com provides an API for requesting searches and returns an
XML file with the search results. The searches were performed among the job titles hosted at
Indeed.com for a specific time period of 3 months between December 2016 and February 2017.
The query phrases such as Business Analyst, Business Intelligence Analyst, Data Analyst and
Data Scientist were used and job titles containing these query phrases were searched. The search
results of the API call contain relevant information such as job title, job URL, location,
company, posting date and a job summary. However, this job summary is brief and is
insufficient to conduct the content analysis because it lacks useful information such as
programming experience, statistical packages and database skillsets. Therefore, we created a
parser to extract the complete job descriptions from the job URLs collected from search results
of the Indeed.com API. This task is benefited from the fact that jobs hosted at Indeed.com are
constructed using a default template based on the same HTML tags.
The parser extracts the job description of each job URL which is part of the search results
of the Indeed.com API. The job description contains relevant information such as position type,
salary, academic qualifications and required software experience. This job description serves as
an input to our content analysis method which counts the number of jobs associated with each
skill category. The keywords related to the different skill categories are listed in Table 1. All
unigrams, bigrams and trigrams in the job description are gathered and subsequently compared
with the skills belonging to a specific skill category. If a match is found, we conclude that a
given job posting is associated with the corresponding skill in the specific skill category. This
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process is repeated for each skill category for all job postings. At the end, for a specific job title,
we collect the number of jobs associated with each skill and also the number of jobs related to
each skill category. This data will be used in the next section to analyze a relative importance of
skills and skill categories for different job categories.
Figure 1: Summary of solution method
4. FINDINGS
. Search query
. From date
. To date
. XML file
containing job
URLs
. HTML file
containing job
descriptions
. Unigrams
. Bigrams
. Trigrams
. Number of jobs
associated with
each skill and each
skill category
Indeed.com
API
Web scraper
Classification
framework
based on skill
categories
Data parser
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A total sample of 1235 job postings satisfying our criteria was collected from Indeed.com
within the period of December 2016 and February 2017. The sample size of this present study
closely resembles a representative sample size for content analysis studies (Kim and Lee, 2016;
Surakka, 2005). The distribution of job postings by the four separate job categories (BA, BIA,
DA and DS) and four different states (Arkansas, Florida, Kansas and Missouri) is presented in
Table 2.
Table 2: Number of job postings in the dataset
Job Category/State
Arkansas
Florida
Kansas
Missouri
Total
Business Analyst
33
526
48
209
816
Business Intelligence
Analyst
3
23
8
11
45
Data Analyst
10
176
24
72
282
Data Scientist
6
46
5
35
92
Next, we investigate the skillset requirements for each of the four job categories. The
five most frequently listed skill categories required for each job category with associated skills
are reported in Tables 3-6. These skill categories are ranked with respect to the number of job
postings in which at least one skill related to the skill category is present. For each skill category,
we will also report the top five skills. These skills are ranked with respect to the percentage of
the number of related job postings associated with a specific skill (as described in Section 3.2)
relative to the total number of job postings for a specific job category as reported in Table 2.
Due to the fact that we report the top five skill categories and corresponding 5 skills, the
percentage of the total counts do not equal to 100%.
Table 3: Business Analyst
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Skill Category
Skill
Percentage Count (%)
Decision Making
Analytical
69.49
Design
44.98
Testing
42.03
Implementation
40.69
Reporting
32.60
Organization
Teamwork
63.48
Manage
34.80
Organizational
34.31
Leadership
28.19
Interpersonal
23.04
Communication
Communication
60.78
Documentation
38.73
Microsoft Office
14.34
Presentation
11.52
Microsoft Word
2.82
Domain
Functional
42.16
Financial
30.15
Computer Science
19.49
Healthcare
16.05
Accounting
11.40
Structured Data Management
SQL
27.21
Database
19.73
SQL Server
7.11
Data Warehouse
3.43
Data Management
3.80
Table 4: Business Intelligence Analyst
Skill Category
Skill
Percentage Count (%)
Decision Making
Analytical
73.33
Design
60.00
Reporting
55.56
Implementation
46.67
Problem Solving
44.44
Structured Data Management
SQL
73.33
Database
48.89
Data Warehouse
26.67
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SQL Server
26.67
Relational Database
17.78
Organization
Teamwork
75.56
Manage
44.44
Organizational
33.33
Interpersonal
31.11
Leadership
22.22
Communication
Communication
55.56
Documentation
35.56
Presentation
26.67
Microsoft Office
6.67
Microsoft Word
4.44
Statistics
Microsoft Excel
57.78
Statistics
26.67
SAS
13.33
R
6.67
Regression
2.22
Table 5: Data Analyst
Skill Category
Skill
Percentage Count (%)
Decision Making
Analytical
64.18
Reporting
48.58
Design
30.50
Problem Solving
19.86
Modeling
16.31
Organization
Teamwork
57.45
Organizational
41.84
Manage
21.63
Leadership
17.73
Interpersonal
15.25
Structured Data Management
SQL
47.52
Database
30.14
Data Warehouse
8.87
SQL Server
8.52
Data Models
7.80
Statistics
Microsoft Excel
51.42
Statistics
19.86
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SAS
10.99
R
10.64
SPSS
4.26
Communication
Communication
48.23
Presentation
21.28
Documentation
19.86
Microsoft Office
17.73
Microsoft Word
2.84
Table 6: Data Scientist
Skill Category
Skill
Percentage Count (%)
Decision Making
Analytical
72.83
Modeling
54.35
Design
52.17
Implementation
26.09
Reporting
17.39
Statistics
Statistics
60.87
R
56.52
SAS
40.22
Microsoft Excel
21.74
Regression
21.74
Organization
Teamwork
66.30
Organizational
23.91
Leadership
22.83
Manage
15.22
Interpersonal
13.04
Domain
Computer Science
39.96
Marketing
39.13
Financial
18.48
Healthcare
7.61
Supply Chain Management
6.52
Programming
Python
45.65
Java
15.22
C
10.87
Scala
5.43
PERL
3.26
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Having reported the breakdown of skillsets and associated skills per each job category,
we turn to the analysis of collected data. Prior research suggests that analysis is conducted via
two main approaches. The first approach focuses on one job category such as Data Scientist
(Kim and Lee, 2016) and ERP specialist (Boyle and Strong, 2006). The second approach
involves comparison of two job categories or skillsets such as Data Scientist and Data Analyst
(Aasheim et al., 2015); Data Warehousing and Business Intelligence (Shirani and Roldan, 2009).
Following Aasheim et al (2015) and Shirani and Roldan (2009), we compare two pairs of job
categories, BA and BIA as well as DA and DS. This breakdown allows us to compare business
analytics-oriented job categories separately from data analytics-oriented categories.
From Table 3 and Table 4, we conclude that structured data management skills are
relatively more important for BIA compared to BA. There is a higher demand for skills in
statistical software in case of BIA compared to BA. The BA jobs place more emphasis on
domain-specific knowledge than the BIA jobs. Specific areas that employers emphasize in the
BA jobs are finance, computer science, healthcare and accounting. While the statistical packages
required for BIA consist of Excel, SAS and R, one clear leader (Excel) dominates tools required
for BA in this skill category.
Similarities for the BA and BIA jobs, on the other hand, can be observed in decision-
making, organization, communication and structured data management skills. Both job
categories require analysis, design and reporting skills within the broad framework of decision-
making skills. Regarding organization skills, the BA and BIA job categories emphasize team
management and leadership skills. From Tables 3 and 4, we conclude communication skills are
similarly crucial for both BA and BIA. This skillset includes documentation and presentation
skills using MS Office tools such as MS Word and MS PowerPoint. As we observed in Table 3
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and Table 4, the key structured data management skills required for BA and BIA are established
data management tools like SQL and SQL Server.
The technical skills like programming and visualization appear to have a minor
significance for the two discussed job categories as they do not appear in the top five skill
categories. We conclude that main differences between BA and BIA positions are present in the
areas of statistics and domain-specific knowledge. The BA jobs place a higher emphasis on
domain-specific knowledge while the BIA jobs prefer candidates with expertise in statistical
software packages. On the other hand, there are multiple similarities. Thus, such areas as
structured data management, decision-making, organization and communication skills are highly
emphasized by employers for the two positions.
The second pair of job categories that we compare are DA and DS. Our analysis
concluded that, similar to BIA and BA, decision making is the top skill for both categories.
Requirements in the decision making skillset for DA and DS are similar. They comprise of
analysis, design, modeling and reporting. This is similar to the requirements for decision-making
skills for BA and BIA. Next, we will comment on the soft skills for DA and DS such as
organization and communication. The organization skills such as teamwork and leadership are
critical for both job categories. As far as communication skills are concerned, they are more
relevant to DA compared to DS.
A stronger emphasis is placed on technical skills for the DS category compared with the
DA category as stated in the literature (Aasheim et al., 2015). In particular, this difference is
more pronounced for programming skills, data mining and big data tools. DS is the most
technical in nature among the categories considered. In particular, a DS professional is required
to have significant programming expertise.
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As for similarities, both DA and DS categories require a thorough understanding of
statistics. In terms of statistical packages, the DS jobs require a firmer grasp of fundamental
statistical concepts like regression. Both job categories use statistical software like Excel, SAS
and R. However, the use of R is more prominent for DS compared to DA. With respect to
structured data management, the DA jobs require established database tools like SQL and SQL
Server. Programming skills are highly critical for DS compared to DA. In addition to traditional
programming languages such as C and Java, employers seeking talent for the DS jobs advertise
newer programming languages like Python.
Apart from the basic understanding of decision-making and organization skills, the two
job categories differ in many aspects. In particular, the DS jobs rely heavily on statistics and
programming skills. We can, thus, conclude that the DS jobs are more technically oriented and
require fewer soft skills compared to the DA jobs.
The high-level similarities and differences between the four job categories are
highlighted in Table 7. This table presents the relative importance of each skill category for each
job category on a scale of 1 to 5, with a priority rank of 1 being the most important and a priority
rank of 5 being the least important. The dashed entry for any skill category indicates that it does
not appear on the top five ranking.
Table 7: Relative importance of each skill category for each job category
BA
BIA
DA
DS
Decision Making
1
1
1
1
Organization
2
3
2
3
Communication
3
4
5
-
Domain
4
-
-
4
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Structured Data Management
5
2
3
-
Statistics
-
5
4
2
Programming
-
-
-
5
5. CONCLUSIONS AND FUTURE RESEARCH
In this study, we analyzed the job descriptions for the four types of analytics positions in
four US states. We relied on the content analysis method to rank skill categories required for
each job category. Thereafter, we conducted a pair-wise comparison of the top five skills for BA
and BIA as well as DA and DS. We found that decision-making is the most desired skill for all
four job categories. To a varying degree, other skill categories such as organization,
communication and structured data management are also in demand for all categories.
The BA category appears to be the least technical of the four studied job categories. The
BA jobs require a high degree of domain knowledge whereas the BIA jobs focus strongly on
structured data management skills along with some knowledge of statistics. The requirements
for DA overlap with those for DS in the areas of decision-making and organization skills.
Compared to the DA jobs, the DS ones strongly rely on statistical and programming skills.
The present study has identified and ranked skills currently in demand for a variety of
analytics positions. The studied skills are categorized via a number of skillsets that represent an
aggregated level of requirements for the job categories studied. The implications of research
findings are two-fold. First, they can guide development/modification efforts for degree
programs in data analytics, business analytics and data science. Our study discusses a broad job
classification for the aforementioned fields and related skillsets providing the results that could
be useful for designing/modifying related undergraduate and graduate programs. Second, the
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findings can be useful for the hiring managers who are in search for candidates for the analytics
positions. Concise and well-structured skillsets would help make job definitions clearer; thus,
saving employers resources and time in the process for formulating requirements for jobs in the
analytics domain.
The future research can look at expanding this study to include more states, preferably all
of U.S. In addition, scholars can expand the scope of the current study to include job
descriptions in a variety of professional fields such as marketing, accounting, finance and
healthcare. Another direction for future research could be analysis of the curricula of degree
programs in the analytics domain. The findings of the present study could be useful for research
aiming at distinguishing similarities/dissimilarities in the skillsets for the studied job categories
and skillsets backed by the coursework of degree programs in the field of business and data
analytics. This research direction could make recommendations as far as distinct programs,
specific courses and related skillsets are concerned.
6. REFERENCES
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