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submitted/accepted for publication in the following source:
Debortoli, S., Müller, O., & vom Brocke, J. (forthcoming).
Comparing Business Intelligence and Big Data Skills: A Text
Mining Study Using Job Advertisements. Business &
Information Systems Engineering.
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Comparing Business Intelligence and Big Data Skills: A Text Mining Study Using Job Ad-
While many studies on big data analytics describe the data deluge and potential applications
for such analytics, the required skill set for dealing with big data has not yet been studied em-
pirically. The difference between big data (BD) and traditional business intelligence (BI) is
also heavily discussed among practitioners and scholars. We conduct a latent semantic analy-
sis (LSA) on job advertisements harvested from the online employment platform monster.com
to extract information about the knowledge and skill requirements for BD and BI profession-
als. By analyzing and interpreting the statistical results of the LSA, we develop a competency
taxonomy for big data and business intelligence. Our major findings are that (1) business
knowledge is as important as technical skills for working successfully on BI and BD initia-
tives; (2) BI competency is characterized by skills related to commercial products of large
software vendors, whereas BD jobs ask for strong software development and statistical skills;
(3) the demand for BI competencies is still far bigger than the demand for BD competencies;
and (4) BD initiatives are currently much more human-capital-intensive than BI projects are.
Our findings can guide individual professionals, organizations, and academic institutions in
assessing and advancing their BD and BI competencies.
Keywords (up to 8) [English]
Big Data, Business Intelligence, Competencies, Latent Semantic Analysis, Text Mining
Big data and big data analytics are among today’s most frequently discussed topics in re-
search and practice (Buhl et al. 2013). In loose terms, big data refers to data sets that are too
large and complex to be processed using traditional storage (e.g., relational database man-
agement systems) and analysis technologies (e.g., packaged software for statistical analysis).
More specifically, researchers and practitioners use the term “big data” to refer to the ongoing
expansion of data in terms of volume, variety, velocity (Laney 2001), and veracity (IBM
Given the current excitement around big data, critical voices question whether big data is “re-
ally something new or […] just new wine in old bottles” (Buhl et al. 2013) or postulate that
we should “forget big data [because] small data is the real revolution” (Polock 2013). Others,
such as Chen, Chiang, and Storey (2012) and Golden (2013), argue that big data is not a revo-
lution but an evolution of traditional business intelligence (BI). According to this view, big
data analytics widen the scope of BI, which focuses on integrating and reporting structured
data residing in company-internal databases, by seeking to extract value from semi-structured
and unstructured data that originates in data sources like the web, mobile devices, and sensor
networks that are external to the company.
Big data offers enormous opportunities for businesses but also poses many challenges (Buhl
2013). A survey of nearly 3,000 executives, managers, and analysts from more than 30 indus-
tries and 100 countries conducted by MIT Sloan Management Review and the IBM Institute
for Business Value finds that top-performing organizations use analytics five times more of-
ten than lower performers do (LaValle et al. 2011), yet not all corporate big data initiatives
are successful. Research shows that “inadequate staffing and skills are the leading barriers to
Big Data Analytics” (Russom 2011), and a study by the McKinsey Global Institute states that
“[t]he United States alone faces a shortage of 140,000 to 190,000 people with deep analytical
skills as well as 1.5 million managers and analysts to analyze big data and make decisions
based on their findings” (Manyika et al., 2011, p. 3).
Given these figures, we academics have to ask ourselves to what degree current research
agendas and curricula satisfy industry’s growing demand for competence in the areas of big
data and analytics. Against this background, the objective of this paper is to clarify the com-
petency requirements of the emerging field of big data (BD) and compare them to the re-
quirements of the established field of BI. In particularly, we seek to (1) identify and catego-
rize competency requirements for BD professionals and BI professionals from a practitioner’s
point of view and (2) highlight theses requirements’ similarities and differences.
The current literature contains only a few contributions on the topic of BI and BD competen-
cies, so we collected and analyzed empirical data from the BI and BD job market. Following
the logic of extant studies on information systems competency requirements (e.g., Gallivan et
al. 2004; Litecky and Aken 2010; Todd et al. 1995), we used online job advertisements as a
data source and performed a quantitative content analysis of 1,357 BI-related and 450 BD-
related job advertisements using a text-mining technique called latent semantic analysis
Our analysis revealed fifteen distinct areas of competency for BI professionals and fifteen
distinct areas of competency for BD professional. On the most abstract level, these areas of
competency can be classified into business competencies and IT competencies. The business
competencies can be further sub-divided into management and domain competencies, and the
IT competencies can be further sub-divided into methodological, conceptual, and product-
specific competencies. Comparing and contrasting the competency requirements for BI and
BD professionals shows areas of overlap, especially regarding IT concepts and methods and
the business domain, as well as clear differences when it comes to IT competencies. While BI
requires skills in the area of commercial software platforms, BD largely relies on software
engineering, statistics skills, and open-source products.
Our empirically grounded frameworks of BI and BD competencies contribute to the IS body
of knowledge by (1) helping professionals to assess and advance their individual competen-
cies, (2) guiding organizations in composing effective portfolios of BI and BD professionals,
and (3) informing the development of academic and professional education programs.
The remainder of this paper is structured as follows. The next section provides research back-
ground on the topic of BI and BD competencies. Then we introduce our methodology and
explain our data-collection and analysis processes. Next, we present our results and discuss
our findings against the background of related work. We close by pointing out the limitations
of our work and implications for future research.
2 Research Background
The resource-based view (RBV) of the firm, especially the framework by Melville et al.
(2004), can be used to evaluate BI / BD implementations’ generation of business value and to
assess which resources and competencies are required and may lead to competitive advantage.
In the focal firm, IT business value is generated by the deployment of IT and complementary
organizational resources (Melville et al. 2004). However, IT affects organizational perfor-
mance only via intermediate business processes. Melville et al. (2004) operationalize IT based
on Barney’s (1991) classification of firm resources into physical capital (technological IT
resources or TIR, i.e., infrastructure and business applications), human capital (human IT re-
sources or HIR, i.e., technical skills and managerial skills), and organizational capital re-
sources (e.g., organizational structures, policies and rules, workplace practices, culture). Sec-
tion 2.1 elaborates on the technological IT resources associated with BI and BD, Sections 2.2
and 2.3 discuss required human IT resources, and Section 2.4 addresses complementary or-
ganizational capital resources.
2.1 Business Intelligence and Big Data
Howard Dresner of the Gartner Group introduced the term “business intelligence” in 1989,
describing “a set of concepts and methods to improve business decision making by using fact-
based support systems” (Power 2007). The first productive BI systems were implemented at
large consumer goods manufacturers like Procter & Gamble and retailers like Wal-Mart for
the purpose of analyzing sales data (Power 2007). Although Dresner’s original definition of
BI, as well as more recent definitions from analysts like Gartner, Forrester, and TDWI, are
broad in scope, most practitioners associate with the term a narrow set of capabilities, such as
extraction, transformation, and loading (ETL); data warehousing; on-line analytical pro-
cessing (OLAP); and reporting (Davenport 2006). The focus of these traditional BI solutions
is on analyzing historical data in order to answer questions like “how much did well sell in a
certain region?” and “how much profit did we make last quarter?”
At the end of the 1990s, the term “big data” started to appear in the scientific literature, refer-
ring to data sets that were too large to fit into main memory or even local disks (Cox and
Ellsworth 1997; Forbes 2013). The first publications about big data originated from the field
of scientific computing, but in 2001 Doug Laney, an analyst with the Meta Group, transferred
the concept to the business domain and coined the term “the 3Vs” to stand for volume, veloci-
ty, and variety, which quickly became the constituting dimensions of big data (Laney 2001).
After the mid-2000s, fueled by Davenport’s (2006) seminal article “Competing on Analytics,”
businesses became increasingly interested in big data, and the focus shifted from technical
issues around the storage of big data to its analysis. Internet-based businesses like Google,
Amazon, and Facebook were among the first to exploit big data by applying sophisticated
data mining and machine learning techniques. What differentiates today’s big data analytics
applications from traditional business intelligence applications is not only the breadth and
depth of the data processed, but also the types of questions they answer. While BI traditional-
ly focuses on using a consistent set of metrics to measure past business performance
(Davenport 2006), big data applications emphasize exploration, discovery, and prediction. As
Dhar (2013) states, “Big data makes it feasible for a machine to ask and validate interesting
questions humans might not consider.”.
2.2 Business Intelligence Competencies
As we found no literature that studies individual BI competencies, we gained an overview of
individual BI competency requirements by consulting extant work on BI maturity/capability
models, reviews of the BI literature, and panel reports.
Both research and practice have engaged in developing BI maturity/capability models. (For an
overview, see, e.g., Russell, Haddad, Bruni, & Granger, 2010). The general purpose of such
models is to systematize organizational capabilities and outline pathways for advancing them.
Models that originate from industry include the TDWI Business Intelligence Maturity Model
(Eckerson 2004), Gartner’s Maturity Model for Business Intelligence and Performance Man-
agement (Hostmann and Hagerty 2010), Gartner’s Magic Quadrant for Business Intelligence
Platforms (Schlegel et al. 2013), and Logica’s Capability/Maturity Model (Van Roekel et al.
2009). Lahrmann et al. (2011), Dinter (2012), and Cates et al. (2005) provide examples of
academic BI maturity models. Industry maturity models tend to focus on technological capa-
bilities that BI platforms should provide (Russell et al. 2010). For example, Gartner lists thir-
teen essential capabilities, including reporting, OLAP, and visualization (Schlegel et al.
2013). Such functional IT capabilities provide some guidance for assessing and developing
individual-level BI competencies but largely neglect the business-related aspects of BI, such
as project management and domain skills. By contrast, the academic models provide a high-
level view of strategic BI capabilities like architecture planning, IT-business alignment, and
generation of business value. While these topics are key to engaging effectively in BI on an
organizational level, we believe that they are too abstract to be useful in assessing and devel-
oping individual-level BI competencies.
The purpose of literature reviews is to analyze and synthesize the academic body of
knowledge, so it is reasonable to expect that reviews can provide insight into competency
requirements by, for example, outlining curricula. We identified one review in the area of BI
that explicitly comments on aspects of education. Based on market research results from
Gartner, Chen et al. (2012) perform a bibliometric study of academic and industry publica-
tions on business intelligence and analytics and structured the business intelligence and ana-
lytics (BI&A) discipline into three evolutionary waves—BI&A 1.0 (database-based, struc-
tured content), BI&A 2.0 (web-based, unstructured content), and BI&A 3.0 (mobile and sen-
sor-based content)—and five emerging research areas—big data analytics, text analytics, web
analytics, network analytics, and mobile analytics. Chen et al. (2012) also outline and map the
competency requirements for each of these fields and advocate that higher education should
consider these competencies in their curricula. Examples of the competencies Chen et al.
(2012) name include relational database management systems (RDBMS), data warehousing,
ETL, data mining, statistical analysis, web crawling, recommender systems, social network
theories, smartphone platforms, machine learning, process mining, in-memory DBMS, cloud
computing, sentiment analysis, and web visualization.
Wixom et al.’s (2011) panel report notes that industry trends raise concerns that “academia
may be behind the curve in delivering effective Business Intelligence programs and course
offerings to students.” Based on surveys conducted at BI practitioner events, Wixom et al.
(2011) formulate four academic BI best practices that would close the gap between BI market
needs and the content of IS education programs: (1) provide a broader range of BI skills, (2)
take an interdisciplinary approach to BI programs, (3) develop reusable teaching resources,
and (4) align with practice. Besides arguing for the need for technical skills, Wixom et al.
(2011) argue that a deep understanding of business subjects (e.g., finance, marketing) and
strong communication skills are required.
2.3 Big Data Competencies
No scientific literature on the topic of BD competences has yet been published, although a
number of articles and web resources anecdotally describe the profile of BD specialists or
similar jobs, such as those of data scientists.
In an influential Harvard Business Review article, Davenport and Patil (2012) describe a data
scientist as “a hybrid of data hacker, analyst, communicator, and trusted adviser” (p. 73) and
call the job of the data scientist “the sexiest job of the 21st century” (p. 70). Likewise, Ham-
merbacher, who created the first data science team at Facebook, portrays a data scientist as “a
team member [who] could author a multistage processing pipeline in Python, design a hy-
pothesis test, perform a regression analysis over data samples with R, design and implement
an algorithm for some data-intensive product or service in Hadoop, or communicate the re-
sults of our analyses to other members of the organization” (as cited in Loukides 2012).
These characterizations seem to call for a hybrid of a computer scientist and statistician, yet
many more business-related authors state that, in the world of big data, one cannot separate
data processing from analysis or from domain knowledge (e.g., Chen et al., 2012; Davenport
& Patil, 2012; Loukides, 2012; Provost & Fawcett, 2013; Waller & Fawcett, 2013). Hence,
BD specialists must have substantial industry knowledge in order to make sense of statistical
analyses and communicate effectively with business colleagues.
2.4 Organizational Setup of Business Intelligence and Big Data Teams
The differences between BI and BD also have consequences on how they are organized. Tra-
ditionally, BI teams are located in internal consulting organizations, centers of excellence, or
IT departments, where they provide managers and executives with reports for their well-
defined and stable information needs (Burton et al. 2006; Davenport et al. 2012; Varon 2012).
However, since most BD initiatives lack predefined questions and are much more experi-
mental in nature (Casey et al. 2013), BD specialists must be organized so they are close to
products and processes in organizations, that is, co-located with business units (Davenport et
While the literature provides first insights into the topic of BI and BD competencies, it is not
grounded in empirical data. Therefore, we study the competencies required of BI and BD pro-
fessionals by performing an automated content analysis of job ads using a text mining tech-
nique called latent semantic analysis (LSA), a quantitative method for analyzing qualitative
data. LSA extracts word usage patterns and their meaning through statistical computations
(Landauer et al. 1998) based on the idea that the contexts (e.g., documents, paragraphs, sen-
tences) in which a word appears or does not appear largely determine the word’s meaning.
LSA is based on the classical vector space model (Salton et al. 1975), in which documents are
represented as vectors of terms, and a collection of documents is represented as a term-
document matrix that contains the number of times each term appears in each document
(Manning et al. 2008). In a fashion similar to exploratory factor analysis, LSA performs a
matrix operation called singular value decomposition (SVD) on the term-document matrix in
order to reduce its dimensionality. The latent semantic factors that are extracted during this
process can be interpreted as topics running through the collection of documents analyzed.
LSA has received growing attention in the IS discipline for quantitative content analysis of
academic papers (e.g., Larsen et al. 2008; Sidorova et al. 2008), social media posts (e.g.,
Evangelopoulos & Visinescu, 2012), sustainability reports (e.g., Reuter et al. 2014), vendor
case studies (e.g., Herbst et al. 2014), and customer feedback (e.g., Coussement & Poel,
A typical LSA is comprised of three phases. (For a more detailed introduction and numerical
examples, see Landauer et al. (1998) and Evangelopoulos, Zhang, & Prybutok (2012)). In the
first phase, a collection of documents is transformed into a term-document matrix. This step
typically requires pre-processing of documents (e.g., removing irrelevant or duplicate docu-
ments) and terms (e.g., uni- and bi-gram tokenization, filtering out uninformative terms,
weighting terms according to their relative importance).
In the second phase, the term-document matrix undergoes SVD to reduce the dimensionality
of the term-document matrix without losing essential information by identifying groups of
highly correlated terms (i.e., terms that co-occur together in documents) and highly correlated
documents (i.e., documents that contain similar terms). The result of the SVD is a set of fac-
tors (topics) with associated high-loading terms and documents. Together, they form patterns
of word use that represent topics in the underlying collection of documents.
The extracted word-use patterns are interpreted in the third phase, which usually involves
additional statistical analyses and, most importantly, expert judgment.
4 Data Collection and Analysis
The next sections illustrate how we applied LSA to analyze BI- and BD-related job adver-
tisements. We followed the procedure described in Section 3 and depicted in Figure 1. As
Figure 1 indicates, LSA often requires multiple iterations in which experts review statistical
results, and inputs (e.g., documents, terms) and parameters (e.g., term weights, number of
factors to be extracted, loading thresholds) are fine-tuned in order to yield optimal results.
Data Col lection and
of factors to be
loadings for terms
loadings (for terms
Interpret and label
Fig. 1. Data Collection and Analysis Process
4.2 Data Collection and Pre-Processing
We performed multiple crawls of the global online recruitment website monster.com, down-
loading job advertisements from the U.S., Canada, Australia, and the U.K. that included either
the term “business intelligence” or the term “big data.” We downloaded the data as two sin-
gle-day snapshots in September 2013 and March 2014. After removing irrelevant hits (e.g.,
spam, non-English ads), we had an initial pool of 4,246 BI-related job ads and 1,411 BD-
related job ads.
Following common text-mining procedures, we reduced the vocabulary in our document col-
lection by removing stop words (e.g., “and,” “or,” “then”) and eliminating terms that occurred
in less than 1 percent of the documents (Manning et al. 2008). The remaining vocabulary con-
tained 6,813 terms, which we then manually reviewed to filter out other irrelevant terms while
keeping only those terms that describe competencies. In particular, we removed standard hu-
man resources terms like “salary,” “bonus,” and “apply.” After this manual data clean-up, the
final dictionary that we used as a go-list for the further analysis contained 1,570 terms.
Based on the controlled vocabulary and the two document sets, we built two term-document-
matrices, one for BI jobs and one for BD jobs. These matrices contained the number of times
a competency-related term appeared in a job ad. Then we weighted terms based on their oc-
currence in and across documents, applying the commonly used TF-IDF (Term Frequency-
Inverse Document Frequency) weighting scheme, which promotes the occurrence of rare
terms (e.g., “hadoop”) and discounts the occurrence of more common terms (e.g., “business,”
“analysis”) (Manning et al. 2008). The two weighted term-document matrices built the foun-
dation for the subsequent SVD.
4.3 Singular Value Decomposition (SVD)
We performed the SVD using the statistical computing software R. The first step of SVD is to
define the number of factors (topics) to be extracted. Techniques from exploratory factor
analysis, such as scree plots and the Kaiser-Harris criterion, would lead to a high number of
factors, so these techniques are not recommended when the goal of LSA is to identify topics
in a collection of documents. Since there is no standard procedure for determining an optimal
number of topics, we manually explored alternative numbers of factors and qualitatively as-
sessed the results (Evangelopoulos et al. 2012). We tested several dimensionalities, including
2, 5, 10, 15, 20, 30, and 50 factors. For each solution, we performed a SVD to compute term
and document loadings for each factor.
4.4 Analysis and Interpretation
Following Sidorova et al. (2008), we performed a varimax rotation on the matrices with the
term loadings to simplify interpretation of the factors. This procedure rotates the coordinates
of the term loadings matrix in a way that maximizes the variance of a factor’s squared load-
ings on all terms in the matrix. As a result, each factor tends to load either high or low on a
particular term; in other words, a term is either descriptive (high-loading) or not descriptive
(low-loading) for a particular factor. To maintain the representation of the documents in the
same factor space, we performed an identical rotation with the document loadings matrix.
Next, loading thresholds must be defined in order to determine whether a term or document is
descriptive for a given factor. Again, no standard rules for setting this thresholds have
emerged (Evangelopoulos et al. 2012), so we adopted a heuristic that Sidorova et al. (2008)
and Evangelopoulos et al. (2012) apply in their LSA-based literature analyses and set the
threshold based on the probability distribution of term and document loadings. For a k-factors
LSA, we retained the top-1/k high-loading documents, so each term and each document loads,
on average, on one factor. However, terms and documents that load high on multiple factors
or that load on no factor at all are to be expected.
The final step consisted of the manual sense-making and interpretation of the extracted factors
and associated high-loading terms and documents. Two researchers independently interpreted
and labeled each factor by examining the lists of extracted high-loading terms and documents.
In almost all cases, factor interpretation was straightforward, and any minor disagreements in
labeling factors were resolved during a final discussion.
5.1 Exploratory Data Analysis
After downloading the job advertisements, we conducted an exploratory data analysis to get a
first feeling for the data. We observed that there were about three times more BI-related job
advertisements than BD-related job ads on monster.com. As a next step, we conducted a word
frequency count, looking for overlaps between job ads (cf. Table 1). The results showed that
about 15 percent of the BD jobs also include the term “business intelligence,” while only 5
percent of the BI job ads also included the term “big data,” perhaps an indicator that BD re-
quires some basic BI-related skills, but BI does not necessarily require BD skills.
Table 1. Exploratory Data Analysis
The word frequency count also showed that the frequency with which the terms “business
intelligence” and “big data” appeared in the job ads was unbalanced, as many ads contained
the search terms only once. A manual inspection of a sample of these ads revealed that the
search terms often occurred only in the company descriptions (e.g., “our company specializes
in big data solutions”) and that the companies were not looking for any BD- / BI-related em-
ployees but for, for example, a team assistant. Therefore, we filtered out job ads that included
the keywords “big data” or “business intelligence” only once, which narrowed our data set
down to 450 BD-related ads and 1,357 BI-related ads. (The ratios displayed in Table 1 were
5.2 Competency Requirements for Business Intelligence Professionals
On the most abstract level of the LSA, the two-factor solution, jobs were assigned to only two
topics. The first factor was associated with high-loading descriptive terms like “developer,”
“sql server,” “data warehouse,” “etl,” and “bi developer.” Associated titles of job ads included
“BI Developer SQL Server,” “ETL Developer,” and “SQL Server DBA.” Terms like “sales,”
“business development,” “marketing,” “account,” and “new business” described the second
group of jobs, with such exemplary associated job titles as “Business Development Manager
BI,” “Sales Executive BI,” and “New Business Sales Executive.” We had no difficulty or dis-
agreement in making sense of and interpreting these results and labeled the two areas of com-
petency “BI Architecture” and “Sales and Business Development.”
The fifteen-factor solution revealed clearly distinguishable BI-related topics that were neither
too broad nor too specific. Table 2 provides an overview of the results and shows the high-
loading terms and job ad titles as well as the manually assigned labels for each of the extract-
ed factors. The terms and job ad titles are presented in order of descriptiveness, as expressed
by the factor loadings calculated during SVD. (Uninformative terms and duplicate job titles
were removed.) We will refer to these factors as competency requirements or competencies.
Table 2 makes clear that industry demands both business and IT competencies. The group of
business-oriented competencies includes those related to specific domains (i.e., healthcare and
digital marketing) and those related to managerial competencies (e.g., project management).
The IT competencies can be divided into those related to vendor-specific products (e.g., Mi-
crosoft, SAP) and those related to general concepts and methods (e.g., database administra-
tion, BI architecture). Figure 2 aggregates the fifteen areas of competency in a taxonomy.
Fig. 2: Business Intelligence Competency Taxonomy
A more detailed examination of the descriptive terms and job ads associated with each factor
gives insights into the corresponding competency requirements. Among the vendor-specific
competencies are product and technology names of specific vendors. For example, BI profes-
sionals working with SAP technologies (Factor BI15.04) need competencies in SAP Busi-
nessObjects (“business objects”), SAP Business Warehouse (“sap bw”), and/or the SAP High
Performance Analytical Appliance (“hana”). Vendors focus on varying aspects of BI, as com-
petencies related to the SAS BI Platform (Factor BI15.07) are described using terms like “sta-
tistical,” “analytics,” and “mining,” and important descriptors for IBM BI Platform compe-
tencies (Factor BI15.12) are “etl,” “report,” and “query.” The varying foci and strengths of
each vendor explain these differences, as SAS is strong in data mining and IBM Cognos is a
leader in data warehousing.
Our analysis also produced some generic IT competencies, such as database administration,
software engineering, and BI architecture. Database administration requires SQL knowledge
as well as knowledge in performance tuning of applications. Typical job ads that include these
competencies are titled with “DBA” and its variants, depending on the operating platform
(e.g., Oracle or MS SQL). Software engineering describes the competency of building custom
pieces of software for data analysis. In particular, Java programming skills and web front-end-
development knowledge are demanded. Last, the factor BI architecture describes a demand
for expertise along the whole BI stack, from ETL to building reports.
Table 2. Competency Requirements for Business Intelligence Professionals
(# of jobs)
High-loading descriptive terms
Titles of high-loading job ads (excerpt)
care, health, systems, reporting,
Business Analyst Regulatory Healthcare, Report
Writer Business Analyst, Manager Clinical De-
Sales and Busi-
sales, business development,
executive, legal, sales team
Legal Sales Executive, Business Development
Manager, Sales Executive Business Intelligence,
Sales Manager Business Intelligence
sql server, ssis, ssrs, ssas, mi-
crosoft, microsoft bi, reporting
BI Developer SSIS SSAS SSRS SQL, BI Data
Warehouse Developer SQL Server, SQL Server
Developer, ETL Developer Business Intelli-
gence SSIS SQL SSRS
sap, sap bi, hana, business
objects, sap bw, erp, consult-
ant, business analyst, crystal
SAP BI Principal Consultant, SAP BI Senior
Technical Consultant, SAP BI Report Analyst
Developer, Senior Business Objects Consultant
marketing, digital, campaigns,
product, analytics, segmenta-
Senior Marketing Executive Online Data Solu-
tions Job, Marketing Database Analyst, Email
Marketing Manager, Digital Relationship Mar-
dba, database administrator, sql
server, oracle, sql, production,
Oracle DBA SQL Server Database Administra-
tor, Senior DBA SQL Server Database Adminis-
trator, SQL DBA with BI Business Intelligence,
MS-SQL Server DBA
sas, studio, analytics, statisti-
cal, mining, olap, data mining,
SAS BI Analyst, Data Analytics Business Intel-
ligence Consultant, Senior SAS Developer, SAS
java, eclipse, apache, web,
linux, engineer, software, ja-
vascript, developer, big data
Senior Java Consultant, Senior Java Technical
Consultant, Mobile Developer Java jQuery
HTML5, Front End Engineer, Senior Backend
Even though we retained the top-1/k term and document loadings and set the computed threshold value accord-
ingly, we followed Evangelopoulos et al. (2012) in double-checking and manually selecting a threshold for
each factor separately based on domain knowledge. As a result, we had to reduce the number of jobs that load-
ed on the first factor (BI15.01).
(# of jobs)
High-loading descriptive terms
Titles of high-loading job ads (excerpt)
bi developer, etl, developer, bi
stack, organization, report
Business Intelligence Developer, ETL Business
Intelligence Developer, BI Developer Excel
Microsoft BI SQL Server, Senior BI Developer
project manager, project, man-
agement, head, client, change,
Senior Project Manager, Technical Project Man-
ager, BI Project Manager Data Warehouse Im-
plementations, Sr Project Manager Business
sharepoint, .net, server, mi-
crosoft, administrator, soft-
ware, web, application
SharePoint Developer, SharePoint 2007-2010
Developer, SharePoint Administrator SharePoint
2010 Server, SharePoint Consultant, SharePoint
cognos, studio, manager, re-
port, framework, developer,
ibm, query, analyst, etl
Cognos BI Developer, Cognos BI Manager,
Cognos Designer, MIS Manager with Cognos BI
experience, Cognos 10 Consultant Developer
qlikview, microstrategy, oracle,
obiee, warehouse, etl, architect,
MicroStrategy Business Intelligence Analyst,
MicroStrategy Developer, Senior QlikView
Developer, BI Visualization Consultant, ETL
business analyst, data analyst,
reporting, excel, organization,
Business Analyst, Business Analyst SAP APO
Excel Expert, Data Analyst, Reporting Data
Analyst, BI Report Analyst, Technical Business
consultancy, business devel-
opment, sales, development
manager, account, market
Business Development Manager & Market Intel-
ligence Consultancy, Sales Business Develop-
ment Manager, Sales Account Manager Re-
In addition to analyzing single areas of competency, we determined the current demand for
each competency by calculating how many job ads loaded high on a factor. The relative num-
bers of jobs assigned to a factor, displayed in Table 2, indicate that competencies in BI plat-
forms, healthcare, and sales and business development are among the competencies with the
highest demand on the BI job market. Table 2 also shows that the demand for business-related
jobs and IT-related jobs is almost evenly distributed.
5.3 Competency Requirements for Big Data Professionals
To report on the results for BD-related jobs, we conducted the LSA on several levels of ab-
straction. On the most abstract level, the two-factor solution, we assigned jobs to two topics.
The five highest-loading terms for the first topic were “java,” “developer,” “hadoop,” “web,”
and “sql,” and exemplary titles of high-loading job ads were “Experienced Java Developer,”
“Java Hadoop Developer,” and “Data Scientist Java Hadoop NoSQL.” In contrast, the top five
descriptive terms for the second topic were “digital,” “sales,” “manager,” “advertising,” and
“marketing,” and frequent job titles included “Digital Sales Executive,” “Sales Manager Big
Data,” and “Digital Relationship Marketing Manager.” The examination of the highest-
loading terms and job titles for both factors suggests that the first factor describes jobs related
to the development of BD solutions (big data developers), while the second factor refers to the
use of BD in marketing and sales (big data users).
Table 3 provides an overview of the results of the fifteen-factor solution and shows exemplary
high-loading terms and job titles, as well as the manually assigned labels for each of the ex-
tracted factors. The inspection of the identified areas of competency shows that, just as for BI
jobs, competencies can be clustered into business competencies and IT competencies. The IT
competency area can be further broken down into generic concepts and methods like quantita-
tive analysis, machine learning, and database administration, and products for developing big
data solutions (i.e., a variety of programming languages and NoSQL databases). The group of
business-oriented competencies is made up of domain competencies in the areas of life sci-
ences and digital marketing, as well as managerial competencies in sales and business devel-
opment and working in start-up companies. Figure 3 summarizes these findings in a big data
Fig. 3: Big Data Competency Taxonomy
In contrast to the BI competencies, we find no factors related to the technologies of commer-
cial vendors, yet many conceptual and methodological competencies, as well as programming
skills in various languages are required. In the factor representing competency in NoSQL
(BD15.01), not a single product or technology name of one of the big commercial database
vendors appears. Instead, terms referring to open-source technologies from the Apache Foun-
dation are dominating the descriptions (e.g., “hadoop,” “hive,” “pig,” “cassandra”). Further-
more, conceptual and methodological IT skills like quantitative analysis (BD15.03), machine
learning (BD15.05), database administration (BD15.10), and software engineering and testing
(BD15.13, BD15.14) are in high demand. These findings suggest that the field of BD is not
(yet) dominated by big vendors’ standard software but (still) relies largely on open-source
technologies and custom-made software solutions.
Table 3. Competency Requirements for Big Data Professionals
(# of jobs)
High-loading descriptive terms
Titles of high-loading job ads (excerpt)
hadoop, nosql, java, hive, scripting,
distributed, database, apache,
mapreduce, hbase, pig, cassandra
Java Hadoop Developer, Big Data Solu-
tions Architect, Big Data Consultant, Data-
base Architect, Big Data Scientist, Chief
Architect Big Data Guru
(# of jobs)
High-loading descriptive terms
Titles of high-loading job ads (excerpt)
digital, sales, advertising, manager,
media, forecasting, presentation,
Junior Digital Sales Manager, Digital
Agency Sales Manager, New Business
quantitative, risk, analyst, models,
modeling, matlab, java, algorithms,
physics, phd, financial, mathemat-
ics, data analyst
Senior Quantitative Analyst, Quantitative
Analyst Financial Risk Management, Big
Data Business Systems Analyst, Sr Data
java developer, junit, tdd, hadoop,
maven, git, nosql, hibernate, eclipse,
agile, hive, mongodb, apache, pig
Experienced Java Developer Java Multi
Thread JUnit TDD, Java Developer Big
Data, Java Hadoop Developer, Senior Java
Architects Developers Core Java Pro-
gramming, Senior Java Consultant Java
Spring Hibernate Maven
data scientist, machine learning,
visualization, statistical, algorithms,
mining, predictive, analysis, science,
Data Scientist Machine Learning C Java
Python, Software Engineer Data Scientist
Machine Learning, Security Cleared Big
Data Scientist, Big Data Architect Hadoop
R Machine Learning
startup, sales, analytics platforms,
market, data analytics, applications,
solutions, information, enterprise,
Sales Big Data Software, Front End Devel-
oper for Big Data Startup, Big Data Analyt-
ics Sales Consultant, Junior Python Devel-
oper Big Data Tech Startup, Java Software
Engineers High Profit Big Data Startup
net, sql server, microsoft, visual,
developer, warehouse, api, front
end, scrum, mvc, agile, project,
High Paid Junior C# ASP.NET Developer,
Developer .NET MVC API, C# .NET De-
veloper SQL Server Senior Software Engi-
neer Big Data, Junior C# ASP.NET Devel-
sciences, life, medical, visualization,
revenue, health, care, project man-
agement, industries, consulting
Strategic Account Manager Big Data Life
Sciences, Big Data Engineer Exciting Start
Up, Revenue Analyst, Project Manager Big
Data, Solutions Consultant Big Data Ana-
(PHP / JavaS-
front end, user, css, agile, html,
jquery, web services, api, mysql,
open source, mongodb
Lead PHP Ninja, Front End Developer
Start Up, Senior PHP Developer, UI De-
Web Developer OOP LAMP
(# of jobs)
High-loading descriptive terms
Titles of high-loading job ads (excerpt)
dba, mysql, oracle, high availability,
sql server, linux, database, senior,
MySQL DBA Big Data High Availability
Replication, DBA Data Modeler, Junior
Database Administrator, DBA Systems
Engineer MySQL NoSQL Big Data Unix
marketing, digital, analytics, media,
insights, information, social, re-
Associate Director Digital Media Analyt-
ics, Marketing Director, Senior Analyst Big
Data Digital Media, Digital Relationship
sales, customer, revenue, account,
management, executive, business
development, marketing, relation-
Business Development Manager Big Data
Technology, Business Development Man-
ager Cloud Computing
software engineer, linux, data engi-
neer, online, professional, open,
product, natural language, systems,
Senior Engineer Big Data, Principle Soft-
ware Engineer Head of Software Develop-
ment, Senior Big Data Engineer, Senior
testing, software, engineer, product,
machine learning, automated, devel-
opment, open source, agile, building
Python Test Engineer, Software Test Engi-
neer, Java Tester, Software Design Engi-
neer in Test, Test Lead
etl, data warehouse, business intelli-
gence, manager, project, technical,
Data Warehouse Product Owner, Director
of Data Engineering, Snr Business Intelli-
gence Developer, ETL Engineer, DWH
Comparing the relative demand between business and IT competencies reveals that almost 70
percent of the posted BD-related job ads seek technical skills. Knowledge in NoSQL data-
bases and software engineering and programming are the most highly demanded areas of
technical competency. Digital marketing, business development, and sales constitute highly
demanded business competencies.
We identified a number of similarities between the fields of BI and BD. Especially when it
comes to generic IT concepts and methods and business skills, we observed a considerable
overlap between BI and BDA (cf. Figure 4). For example, working in either field requires a
certain amount of software engineering and database competency. Sales and business devel-
opment skills for managing BI and BD solutions also overlap. Finally, domain knowledge
overlaps in healthcare / life sciences and digital marketing, domains known to be especially
data-driven. The absence of other domain skills is a result of the level of analysis we chose; a
more granular LSA on BI and BD job ads (e.g., 50 instead of 15 factors) would reveal the
additional domains of banking, finance, insurance, and supply chain management.
Fig. 4. Similarities and differences in BI and BD areas of competency
The major differences between BI and BD competencies are discussed in the next section.
Our research revealed highly demanded BI and BD skills in at least two areas, business and
IT. This first finding empirically grounds the ongoing discussion about business knowledge’s
being as important as technical skills for working successfully on BI and BD initiatives (e.g.,
Chen et al., 2012; de Lange, 2013; Waller & Fawcett, 2013; Wixom et al., 2011). For exam-
ple, De Lange (2013) sees programming and statistical expertise as the foundation for data
scientists but also states that “a strong background in business and strategy can help jettison a
younger scientist’s career to the next level.” Chen et al. (2012) argue that BI and analytics
professionals “must be capable of understanding the business issues” and at the same time
capable of “framing the appropriate analytical solutions” to provide useful decision-making
support. Wixom et al. (2011) analyze existing BI-related university programs and courses and
conclude that the BI program of the future should include both business and technical courses,
including at least an understanding of data management, functional business knowledge, sta-
tistics and quantitative analysis, and communication and visualization skills, in order to ad-
dress the widest scale of industry needs. The empirical evidence we provide with this study
underscores these arguments and should encourage IS scholars to develop inter-disciplinary
programs and courses to prepare “the next generation of analytical thinkers” (Chen et al.
We also showed that there are considerable differences between BI skills and BD skills. The
extracted BI competency requirements feature skills related to commercial products of large
software vendors, whereas no BD skills descriptors refer to one of the large BI vendors. In
addition, almost 70 percent of the BD jobs we analyzed asked for strong software develop-
ment skills and statistical knowledge, whereas BI jobs required much less “programming” and
statistical knowledge. While BD jobs demand quantitative analysis and machine learning
skills, there is no explicit mentioning of such terms in BI jobs.
Why did we find such differences, although both BI and BD focus on supporting decision-
making through quantitative analysis of data? There are two possible explanations for this
finding: First, the emerging literature on big data consistently emphasizes its variety, suggest-
ing that big data does not refer to relational data managed in enterprise systems or data ware-
houses but to streams of data in various formats and from various sources (Davenport et al.
2012), mostly the Internet. Because of this variety of data, big data analytics solutions rely
less on standard software products than they do on custom-made solutions. Second, current
big data projects seek answers to highly specialized questions and are often more comparable
to research projects than to traditional IT projects (Marchand and Peppard 2013). Because of
this variety of questions, big data analytics solutions require more tailored software tools and
better methodological skills than traditional BI does. Whatever the explanation, our observa-
tion is in line with Golden’s (2013) argument that big data investments will be open-source.
Even though large vendors like SAP are developing analytical solutions like SAP HANA
(vom Brocke et al. 2014) that will offer “predictive analytics, text and big data in a single
package” (SAP 2014), our analysis shows that the BD job market does not yet ask for experts
in the use of these tools.
We also found that the demand for BI competencies is still far bigger than that for BD compe-
tencies, as we found three times more job ads containing the term “business intelligence” than
we did job ads containing the term “big data.” This finding might be surprising given the cur-
rent media excitement around big data (cf. Figure 5), but our empirical results suggest that
most companies are still working on advancing the maturity of their internal BI and are not
yet seeking to exploit big data.
Fig. 5. Google search volume for the search queries “business intelligence” and “big data”
(Source: Google Trends)
Highlighting our results against the background of the resource-based view of the firm (i.e.,
the TIR and HIR mentioned in Section 2), we argue that BI implementations rely heavily on
well-established TIR, as BI platform vendors already provide them. Significant amounts of
knowledge have already been built into the technology itself, and it is at a mature state and
easily deployed in a company. HIR are only required for customizing and adapting the tech-
nology to the organizational context. However, BD still relies on basic TIR, such as pro-
gramming languages and plain database technologies, which require extensive HIR in order to
build the sophisticated, company-specific big data solutions that may lead to temporary com-
petitive advantage. Therefore, we can conclude that BD initiatives are currently much more
human-capital-intensive than BI projects are, so we call for further action in educating current
and future employees.
Contrasting our findings against the three evolutionary BI&A waves Chen et al. (2012) identi-
fy, we observe that the skills that are related to the first wave (structured content residing in
databases) are still the most frequently demanded. Examples include conceptual knowledge
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Business Intelligence Big Data
about data warehousing and practical skills concerning major BI platforms. Supporting deci-
sion-making by extracting knowledge from web-based and unstructured content (i.e., second-
wave BI&A) still seems to be in its infancy, as we found no factor related to text mining, web
mining, or social network analytics, although some of these terms were scattered among the
high-loading terms that described BD factors. These results are surprising, as many experts
point out that these techniques are at the core of big data analytics. Finally, our analysis did
not produce evidence that industry currently demands third-wave BI&A competencies (i.e.,
mobile and sensor data), a finding that disagree with Chen et al. (2012).
This paper set out to shed light on the topic of individual-level BI and BD competencies. Giv-
en the lack of empirical research in this area, we conducted an LSA of 1,357 BI-related and
450 BD-related job ads harvested from the online employment platform monster.com. By
analyzing and interpreting the statistical results of the LSA, we developed BI and BD compe-
tency taxonomies. Our major findings are that (1) business knowledge is as important as tech-
nical skills for working successfully on BI and BD initiatives; (2) BI competency is character-
ized by skills related to commercial products of large software vendors, whereas BD jobs ask
for strong software development and statistical skills; (3) the demand for BI competencies is
still far bigger than the demand for BD competencies; and (4) BD initiatives are currently
much more human-capital-intensive than BI projects are.
Our research contributes to the scientific body of knowledge on BI and BD and has several
implications for practitioners. By uncovering highly demanded skill sets for BI and BD ex-
perts, we complement existing scientific work on BI / BD maturity models. The empirically
grounded taxonomies we developed can be used as a foundation for future empirical studies
on BI and BD, such as efforts to develop measurement instruments for studying BI and BD
professionals or teams. Our findings also inform the assessment and development of BI and
BD curricula. As numerous practitioners and researchers have pointed out, undergraduate and
graduate programs should be created or modified in order to satisfy industry’s high demand
for analytical skills, especially in the areas of software engineering, statistics, and business
skills. Practice may benefit from this study in two ways. At an individual level, our results
provide guidance for individuals’ professional development by, for example, outlining path-
ways for career choices and decisions about continuing education. At an organizational level,
the identified competencies can be used to inform strategic HR management (e.g., establish-
ment of a BI/BD Center of Excellence) and staffing decisions (e.g., for BI/BD projects). In
particular, we advise organizations that want to engage in big data analytics either to invest in
building in-house software engineering and statistical skills or to collaborate with third parties
(e.g., universities) in order to obtain the required competencies.
As in all research, this study is not without several limitations. First, our findings are based on
snapshots of the BI and BD job market taken in September 2013 and March 2014. To gain a
more reliable picture of knowledge and skill requirements and track their development over
time we plan to repeat the study presented here regularly in the future. Second, our data anal-
ysis used job advertisements to elaborate on the differences between BI and BD competen-
cies, as it is reasonable to assume that job advertisements act as proxies for a demand for hu-
man capital in industry and that they can provide insights into competency requirements.
However, one must be aware that job ads do not always reflect an employer’s true require-
ments, as the employer may ask for more competencies than can be reasonably expected from
an applicant, or they may use a specific vocabulary to polish job ads so they are appealing to a
certain group of candidates. Such may be the case especially in the area of BI and BD, which
lacks clear-cut definitions and is full of industry jargon. While we acknowledge that such bi-
ases may exist in our data, we believe that the number of job ads that we examined should be
sufficient to minimize the effect of biases in a few ads. The processing of such a broad data
source as that used in this research gives a particular advantage to the approach we used over
other research methods, such as interviews, because it diminishes the risk of biases caused by
specific contextual backgrounds. Third, our findings are limited to job markets in English-
speaking countries because of the nature of the text mining technique we applied, which can-
not process multilingual texts. Future studies may look at job markets in other major language
regions (e.g., Spanish, French, Portuguese, German, Russian, Hindustani, Mandarin Chinese).
Finally, our study is inductive and exploratory in nature, so future confirmatory research (e.g.,
surveys) is needed in order to test and refine our results.
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