The role of business analytics in the controllers and management accountants’ competence profiles: An exploratory study on individual-level data

Thuy Duong Oesterreich (Department of Accounting and Information Systems, Osnabrück University, Osnabrück, Germany)
Frank Teuteberg (Department of Accounting and Information Systems, Osnabrück University, Osnabrück, Germany)

Journal of Accounting & Organizational Change

ISSN: 1832-5912

Publication date: 3 June 2019

Abstract

Purpose

In recent years, the rise of big data has led to an obvious shift in the competence profile expected from the controller and management accountant (MA). Among others, business analytics competences and information technology skills are considered a “must have” capability for the controlling and MA profession. As it still remains unclear if these requirements can be fulfilled by today’s employees, the purpose of this study is to examine the supply of business analytics competences in the current competence profiles of controlling professionals in an attempt to answer the question whether or not a skills gap exists.

Design/methodology/approach

Based on a set of 2,331 member profiles of German controlling professionals extracted from the business social network XING, a text analytics approach is conducted to discover patterns out of the semi-structured data. In doing so, the second purpose of this study is to encourage researchers and practitioners to integrate and advance big data analytics as a method of inquiry into their research process.

Findings

Apart from the mediating role of gender, company size and other variables, the results indicate that the current competence profiles of the controller do not comply with the recent requirements towards business analytics competences. However, the answer to the question whether a skills gap exist must be made cautiously by taking into account the specific organizational context such as level of IT adoption or the degree of job specialization.

Research limitations/implications

Guided by the resource-based view of the firm, organizational theory and social cognitive theory, an explanatory model is developed that helps to explain the apparent skills gap, and thus, to enhance the understanding towards the rationales behind the observed findings. One major limitation to be mentioned is that the data sample integrated into this study is restricted to member profiles of German controlling professionals from foremost large companies.

Originality/value

The insights provided in this study extend the ongoing debate in accounting literature and business media on the skills changes of the controlling and MA profession in the big data era. The originality of this study lies in its explicit attempt to integrate recent advances in data analytics to explore the self-reported competence supplies of controlling professionals based on a comprehensive set of semi-structured data. A theoretically founded explanatory model is proposed that integrates empirically validated findings from extant research across various disciplines.

Keywords

Citation

Oesterreich, T.D. and Teuteberg, F. (2019), "The role of business analytics in the controllers and management accountants’ competence profiles: An exploratory study on individual-level data", Journal of Accounting & Organizational Change, Vol. 15 No. 2, pp. 330-356. https://doi.org/10.1108/JAOC-10-2018-0097

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Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited


1. Introduction

Recently, business environments across industries are being shaped by an ongoing IT-enabled transformation process with far-reaching consequences on the structure and business processes of organizations (Lucas et al., 2013). The advent of digital technologies, such as big data, is expected to transform work practices in a wide range of jobs, while creating entirely novel occupations across industries, such as big data analysis, app development or software design. As a consequence, skills requirements in diverse professions are being shaped, leading to a higher demand for digital skills also outside the technology sector (Berger and Frey, 2016). These new requirements comprise a wide range of new abilities and competences including basic ICT skills such as manipulation of spreadsheets to more advanced ICT skills such as advanced analytics and programming (Berger and Frey, 2016).

In line with these recent developments, the rise of big data is expected to entail several challenges for the controlling and management accounting (MA) profession (Bhimani and Willcocks, 2014). While the “controller” is a common term in German-speaking countries, the management accountant (MA) is a well-known term in the English speaking countries such as the USA and UK (Ahrens and Chapman, 2000). In comparing both professions, Schäffer (2013) states that the tasks of the controller is generally considered broader in scope, not only focussing on accounting issues but also on management issues. In this paper, the terms “controller” and “MA” are used interchangeably, as commonly found in the accounting literature (Ahrens and Chapman, 2000; Loo et al., 2011; Verstegen et al., 2007). Regardless of the job title, the data-intensive tasks and activities of both professions in supporting the managerial decision-making process are expected to be transformed through advanced technologies such as data and business analytics (Brands and Holtzblatt, 2015). Numerous practical and academic publications from the past have highlighted a paradigmatic shift in the skills profile expected from controllers and MAs in organizations across industries. For example, business analytics and information technology (IT) skills have gained importance as one of the major skills areas of the controller’s and MA’s skills profile, being a “must have” capability for the controlling profession (Bhimani and Willcocks, 2014; Karenfort, 2017; Payne, 2014). The role of the controller and MA is, therefore, expected to shift towards a data scientist with strong systematic and mathematical–statistical competences and business analytics capabilities (Karenfort, 2017).

In considering this obvious shift in skills requirements, the question arises whether these new expectations can be fulfilled by today’s controllers and MAs. Notwithstanding the relevance of this question, no attempts have been made to date to study the current skills levels of these professions. In responding to this research gap, the primary purpose of this paper is to examine whether and to what extent competences in business analytics and IT skills are supplied by the controller from the German-speaking area, based on a comprehensive set of 2,331 member profiles from the German business social network XING. Given this major objective, we aim to address the following research questions:

RQ1.

What business analytics competences and IT skills are currently supplied by controllers and to what extent are these skills and competences evident in their skills profiles?

RQ2.

Does a skills gap exist between the controllers’ new skills profile and the currently supplied skills profile?

RQ3.

What are the possible moderators that might affect the supply of business analytics competences and IT skills?

The answer to these questions would be helpful in identifying potential skills gaps in the professional’s current job profile prior to making necessary adjustments. This is a matter for concern as the controller’s new skills are considered helpful for the controller to become irreplaceable for organizations when machine learning has taken over most repetitive tasks (Bhimani and Willcocks, 2014; Seufert and Treitz, 2017). Additionally, our study contributes to research and practice by applying a text analytics approach using the content analytics tool IBM Watson Explorer (WEX) Content Analytics in an attempt to encourage researchers and practitioners to integrate this approach into their research process and thus to advance big data analytics as a method of inquiry.

The remainder of this paper proceeds as follows. In Section 2, we provide insights into the new skills requirements of the controller and MA that has emerged in the business media. Subsequently, in Section 3, we describe the research process including the methods, steps and activities and the tools required for examining the data. Section 4 is focussed on the presentation of results in responding to the research questions. In Section 5, we provide an explanatory model that is aimed at enhancing the understanding towards the skills gap. In Section 6, limitations and implications for research and practice are discussed. Finally, concluding remarks are provided in Section 7.

2. Big data, business analytics and the controlling and management accounting profession

In this section, we describe the impacts that recent advances in big data and business analytics entail for the controlling and MA profession. Besides, we also briefly delineate the new skills requirements of the controller and MA that have emerged in business media due to these recent developments.

2.1 The impact of big data on the controlling and management accounting profession

Over the past years, the rapid rise in popularity of big data has created an enduring IT fashion that attracts attention in both business community and academic circles alike (Madsen and Stenheim, 2016). To date, a plethora of metaphors like “management revolution” (McAfee et al., 2012), “the next frontier” (Manyika et al., 2011) or “extreme velocity of change” (Bhimani and Willcocks, 2014) have been cited to describe the disruptive impact of big data on various businesses and industries. Agarwal and Dhar (2014, p. 443) even go to the extent of claiming that “big data is possibly the most significant “tech” disruption in business and academic ecosystems since the meteoric rise of the Internet and the digital economy”. Big data can, thus, be defined as “transformational” IT (Agarwal and Dhar, 2014), which has the potential to shape and reshape the economy by altering processes, creating new organizations, enabling market entries, changing social relationships and user experiences and gaining new customers (Lucas et al., 2013).

Extant research has often cited the three V’s framework to highlight volume, velocity and variety as the main characteristics of the big data concept. The term volume puts emphasis on the magnitude of data (Gandomi and Haider, 2015). According to a recent report (Reinsel et al., 2018, p. 6), the worldwide data volume is predicted to grow annually by 61 per cent from 33 zettabytes[1] in 2018 to 175 zettabytes by 2025. In other words, it requires a stack of DVDs that would circle the earth 222 times or reach the moon 23 times to store this data volume. The second V, velocity, refers to the speed of data creation, which enables real-time analysis and decision making, offering companies huge opportunities to gain competitive advantages (McAfee et al., 2012). The variety of data as the third V refers to their structural heterogeneity (Gandomi and Haider, 2015). Structured data are tabular data that can be found in relational databases from business applications such as ERP, CRM or SCM systems (Gandomi and Haider, 2015; McAfee et al., 2012). They are generally characterized by their unambiguous values and implied rules about their meanings and the way they are processed, whereas unstructured data, on the contrary, are characterized by a lack of structure such as natural language or free text (Zhu et al., 2014, pp. 12-13). Unstructured data come from a variety of sources, such as messages, images, audios and videos from social networks, sensors, GPS and cell phone data, data from online shopping platforms, etc., (McAfee et al., 2012). As unstructured data cannot be handled by conventional relational databases, they are more difficult to be analyzed (McAfee et al., 2012). Semi-structured data consist of a mixture of both structured and unstructured data. Overall, it is assumed that the share of structured data in all existing data is only 5 per cent, while the rest represents unstructured data (Gandomi and Haider, 2015).

Given these definitions, in this paper, the term big data is used to describe very large and complex data sets from various sources that require advanced techniques for their storage, management, analysis and visualization (Chen et al., 2012). These advanced techniques and technologies, in turn, are referred to as big data analytics, a broad and related field that encompasses a multitude of similar analytical concepts that are often used interchangeably as synonyms such as data analytics, business analytics, real-time analytics, predictive analytics or business intelligence (Chen et al., 2012). Big data analytics is considered beneficial, as it enables us to detect new patterns and correlations out of the huge amounts of data prior to drawing conclusions (John Walker, 2014). The insights gained from these patterns and correlations, in turn, help managers to measure and manage their businesses more precisely, and thus, to improve company performance by making better predictions and more informed decisions (McAfee et al., 2012). With the overall focus on extracting the essential insights from big data (Gandomi and Haider, 2015), big data analytics are based on data mining and statistical techniques (Chen et al., 2012).

To make sense of the huge amounts of data, companies will need highly qualified manpower able to work with large quantities of information (McAfee et al., 2012). Alongside with the advent of big data as one of the major technological trends, McKinsey’s forecast reveals that there will be a shortage of 140,000-190,000 people with deep analytical skills and 1.5 million managers and analyst for big data analytics and decision-making (Manyika et al., 2011, p. 3). Besides, a more recent study conducted by the BCG group has highlighted the need for a new type of job role arising from Industry 4.0, the so-called “industrial data scientist”, being responsible for data preparation, data analytics and data application (Lorenz et al., 2015). The skills required for this new job role encompass, among others, the ability to clean and organize large unstructured data sets, expertise in visualization techniques, analytical skills to identify correlations and draw conclusions, programming skills including capabilities to use both statistical and general-purpose programming languages and an enhanced business understanding and communication skills (Lorenz et al., 2015; McAfee et al., 2012).

Because of the multitude of data-intensive tasks of the controller and MA in supporting the managerial decision-making process, the skills profile of this profession group is also expected to be transformed through advanced technologies such as data and business analytics. For example, the MA is involved in cost management, planning and decision-making, management and operational control and performance measurement, to name just a few (Brands and Holtzblatt, 2015). The increasingly digitized business environment comes along with a large amount of digital data created on various topics across businesses and industries (McAfee et al., 2012). The steadily increasing data volumes and the multitude of data sources constitute a major challenge to the controller’s and MA’s tasks, with new requirements towards business analytics skills and competences for handling these data (Bhimani and Willcocks, 2014; Brands and Holtzblatt, 2015).

2.2 The controller’s and management accountant’s new competence profile

Prior to illustrating the changing skills profile of the controller and MA that has emerged as a direct consequence of the recent big data and business analytics trends, we must first refer to their traditional job profile. A common definition for the traditional role of the controller and MA is given by the literature as follows (Verstegen et al., 2007, p. 11):

A management accountant or controller supports and advises the management of an organization in realizing their economic, public and/or financial goals. Support is interpreted in terms of the design and maintenance of management control and accounting information systems, and the procurement and distribution of information.

While the “controller” is a common term in German-speaking countries, the MA is a well-known term in English speaking countries such as the USA and UK (Ahrens and Chapman, 2000). In comparing both professions, Schäffer (2013) states that the tasks of the controller is generally considered broader in scope, not only focussing on accounting issues but also on management issues. In this paper, the terms “controller” and “MA” are used interchangeably, as commonly found in controlling and MA literature (Ahrens and Chapman, 2000; Loo et al., 2011; Verstegen et al., 2007).

Regardless of the job title, the definition provided above puts emphasis on the supporting role of the controller and MA as an information provider, being responsible for the procurement and distribution of information. Traditionally, the data sources included in the controller’s and MA’s data analysis tasks are restricted to structured data sources such as data generated by ERP systems, spreadsheet programmes and other internal applications (Brands and Holtzblatt, 2015). In times of rapidly growing data volumes, it is considered insufficient to purely rely on historical data for guiding decisions, but rather to make use of future-oriented business analytics to identify and understand market trends and customer behaviour, developing new products and improving strategical considerations (Bhimani and Willcocks, 2014). With the rise of big data, structured and unstructured data from both inside and outside the company can be included into analyses to create forecasts and market trends, which, in turn, creates an added value (Brands and Holtzblatt, 2015). The positive impact of data-driven decision-making on firm performance has already been confirmed by Brynjolfsson et al. (2011) and Brynjolfsson and McElheran (2016), who state that firms using data and business analytics for decision-making show higher outputs and productivity.

Given these recent developments, business analytics and IT skills are expected to gain importance as one of the new major skills areas of the controller’s and MA’s skills profile, being a “must have” capability for both professions to gain benefits from business analytics (Bhimani and Willcocks, 2014; Karenfort, 2017; Payne, 2014). As opposed to the traditional definition of the controlling and MA profession given above, in more recent role definitions a comprehensive set of IT and business analytics skills is integrated. For example, the Institute of Management Accountants (IMA) (2018a) has recently updated its competence framework in June 2018. The revised competence framework includes six domains of core skills that MA professionals need to maintain their position. Beyond the common core skills such as reporting and control, strategic management or leadership, one major skills area includes technology and analytics skills, described as “the competences required to manage technology and analyze data to enhance organizational success” (IMA, 2018b). Technology and analytics skills that are expected from MAs comprise the following four main tasks with different levels of maturity (IMA, 2018b):

  1. Information systems: The ability to effectively use information systems (IS) and IT, design systems structure and data warehouses and to evaluate, recommend and implement the appropriate ERP system.

  2. Data governance: The ability to manage data and to design and implement data governance systems to ensure the “availability, utility, integrity and security of data”.

  3. Data analytics: The ability to make use of quantitative and qualitative techniques such as multiple query, scripted or interpreted languages (e.g. SQL, Python, R), advanced statistical tools for exploratory data analysis (e.g. cluster analysis, time-series analysis, Monte Carlo analysis) or for the development of predictive models.

  4. Data visualization: The ability to visualize data to effectively and adequately interpret and communicate the results of complex analyses.

In a similar manner, numerous publications from academic and practical controlling literature have highlighted this recent shift in the skills profile of the controller and MA. Apart from the obligatory professional skills, such as a basic qualification and professional experience and specialized controlling and accounting skills, a comprehensive set of business analytics and IT skills are expected to be a major part of the controller’s and MA’s skills profile in the near future.

More specifically, the required business analytics skills encompass the knowledge of the existence and availability of quantitative and statistical methods (Mödritscher and Wall, 2017; Seufert and Treitz, 2017), as well as the ability to acquire, prepare, integrate, analyze and visualize both internal and external data with the primary purpose to identify and extract patterns and interrelations between variables (Angerer et al., 2017; Behringer, 2018; Mehanna, 2015; Seufert and Treitz, 2017; Stratigakis and Kallen, 2017). It is, therefore, essential for the controller to have basic skills in system and data architecture (Horváth and Michel, 2017) and advanced statistical, programming and modelling skills (Egle and Keimer, 2017; Mödritscher and Wall, 2017). Advanced mathematical and statistical skills are required for the controller to apply the various statistical analysis methods (Internationaler Controller Verein, 2016; Karenfort, 2017; Payne, 2014; Schäffer and Weber, 2016; Weber, 2015) and to apply statistical methods to generate exploratory hypotheses from available data (Mödritscher and Wall, 2017). Besides, skills in programming and scripting language are useful to generate an added value from the analyzed data (Egle and Keimer, 2017), while knowledge of data modelling is helpful to make forecasts (Horváth and Michel, 2017; Kieninger et al., 2015; Pietrzak and Wnuk-Pel, 2015). Overall, the role of the controller and MA is predicted to shift from the traditional role of the information provider towards a data scientist with strong systematic and mathematical–statistical competences (Karenfort, 2017).

Apart from the business analytics skills as described above, general IT skills have also gained importance within the controller’s and MA’s skills profile. For example, the controller is expected to be able to communicate with computers and machines and to use IT applications such as ERP, MS Office and other computer programs in his/her daily work (Brands and Holtzblatt, 2015; Egle and Keimer, 2017; Horváth and Michel, 2017; Kieninger et al., 2015; Rasch et al., 2015). Besides, it has been stated that it is necessary for the controller to have a digital mindset, a basic digital competence that is required when using ICT and digital media and the understanding of digital business (Kirchberg and Müller, 2016; Seufert et al., 2017).

3. Analysis of member profiles on business social networks

Given our research objectives defined in Section 1, member profiles on business social networks such as LinkedIn or XING constitute a valuable source of data for various reasons. The examination of particular competence supplies requires a high number of individual-level data that must contain information about the professional’s skills, competences and abilities similar to those offered by a traditional curriculum vitae (CV). As traditional CVs are not publicly available due to data privacy concerns, member profiles on business social networks are a more feasible option with respect to data accessibility and data availability. Similar to a CV, a member profile published on a business social network usually contains relevant information about the professional’s job positions, educational background and their skills, competences and interests (Gorbacheva et al., 2016; Sievers et al., 2015). In publishing these personal-related data, members of business social networks aim at acquiring and maintaining professional contacts or seeking for new jobs, whereas companies can use them as a marketplace to recruit business professionals or develop their employer branding (Heidemann et al., 2012; Sievers et al., 2015).

Previous research has already relied on member profiles from business social networks as a data source for studying competence-related topics of interest. Gorbacheva et al. (2016) take a Latent Semantic Analysis (LSA) text mining approach to examine competence supplies and the role of gender in LinkedIn member profiles of business process management (BPM) professionals. Lohmann and Zur Muehlen (2015) conduct a content analysis of BPM practitioner LinkedIn profiles to compare the skills sets found with those demanded in job advertisements. Based on user profiles from the platform about.me, which allows users to link multiple online identities from diverse social networks such as LinkedIn, Twitter and Facebook, Chelaru et al. (2014) are focussed on investigating differences in communication and connection practices between subgroups of professions. The most recent attempt to make use of member profiles data in a business context has been made by Gloor et al. (2018), who extracted the network structure of start-up entrepreneurs’ XING profiles to investigate the usefulness of virtual ties for competitive advantage and business success. To the best of our knowledge, no attempts have been made to examine the role of business analytics competence and IT skills supplies based on member profiles.

3.1 Data collection

For data collection purposes, we refer to member profiles on the business social network XING to focus on examining the role of business analytics competences and IT skills in the skills profiles of controlling professionals. With 14.4 million active members, XING is the leading business social network in the DACH region including Germany, Austria and Switzerland (Handelsblatt, 2018), offering a huge pool of professionals from across various industries, organizations and professions. Besides, the collection of data in XING has proven to be feasible due to the comfortable search functions offered by its search engine.

We intentionally decide to extract member profiles of DAX and MDAX companies, organizations that are among the TOP 100 largest companies in Germany (CIO, 2018) and a randomly selected sample of member profiles from diverse companies for two main reasons. First, large enterprises such as DAX30 companies tend to be faster and more successful in adopting ICT whereas the level of ICT adoption in SMEs tends to be rather low due to resource constraints (Arendt, 2008; Thong, 2001). Hence, it can be assumed that business analytics competences and IT skills are likely to be more evident in the skills profiles of employees of large enterprises rather than in those of small- and medium-sized enterprises (SMEs). In comparing the competence supplies in member profiles of different company categories, we aim to examine the moderating effect of company size across the four groups on the competence supplies of the employees. Second, as we intend to enrich the extracted member profiles data by adding company-level information such as number of employees, revenue and other key figures, we must ensure that these data are publicly available. Companies listed in the prime standard segment such as DAX or MDAX companies must comply with disclosure requirements, which include the publication of annual and quarterly reports on their websites (Deutsche Börse Group, 2004). This, in turn, facilitates the availability and accessibility of company-level data.

To fully exploit the benefits of XING, we use a premium member account, which enables us to obtain up to 300 search results per search query. The search terms applied for the search queries include the term “controller” within the current position field and the company name of the current employer. The names of the DAX and MDAX companies are obtained from the list of the DAX and MDAX companies (boerse.de, 2018). Beyond these two search options, no further limitations or filters have been applied. Based on the results of each search query, the semi-structured data as shown in Table I were automatically extracted from the member profiles and stored in Excel spreadsheets as .csv files prior to conducting data preparation and data analysis.

Due to restrictions in the allowed number of data extraction per day, the process of data collection covers five weeks during July and August 2018. Overall, a total number of 3,358 member profiles could be extracted during this time period.

3.2 Data preparation

Based on the extracted data, the subsequent data preparation process consists of three major steps, which help us to cleanse the data and enrich them by adding further information prior to the data analysis step.

  1. Data cleansing: The collected member profiles are checked for duplicates and fake profiles to ensure that only meaningful data are included into the subsequent analysis. As a consequence, 13 profiles with obvious fake names such as “Maxi Milian”, “Ad Bert” or “Gustav Limo” have been excluded. In a further step, we decided to delete a total number of 1,014 profiles without any content in the fields “haves”, “qualifications” or “wants”, as it does not make sense to analyze data sets without content. After the data cleansing step, a total number of 2,331 member profiles remains.

  2. Adding individual-level data: Each member profile is enriched by adding individual-level data such as gender, academic degree classification and the associated discipline. To identify the gender of each member profile, we refer to the first names and pictures offered in the profiles and, where necessary, manually conduct a Google search to determine the correct gender. Besides, we assign the self-reported academic degrees in different structured categories such as “Doctor”, “Master”, “Bachelor” or “Diploma”, as in most cases the members use unstructured text to describe their academic degree. Where possible, we also identify the associated discipline of each member in defined categories (e.g. “economics”, “engineering” and “IS”). The additional individual-level data enable us to explore the effect of gender or academic background of the controlling professionals on the supply of their business analytics and IT skills.

  3. Adding company-level data: The final data preparation step consists of the addition of relevant company-level data such as the number of employees and the financial key figures reported in the last available annual report on the companies’ websites. The key figures of interest include the annual revenue, the net income and the value of assets. In the case that the company is not listed as a DAX or MDAX company, these data are not available on the websites. Instead, we search for the last annual report on the central repository of the German Federal Ministry of Justice and Consumer Protection, which offers a free accessible platform for annual publications and financial statements of German companies (Bundesanzeiger Verlag, 2018). By adding company-level data, we aim to examine the role of company size and other key figures on the competence supplies.

Beyond the data cleansing and data enrichment steps, the collected member profiles are merged into one single Excel spreadsheet to facilitate the data import for the data analysis step, which is described in the following section.

3.3 Data analysis and synthesis

To identify patterns, and thus, to make sense out of the content from the member profiles, we decide to conduct a content analysis, which constitutes a popular and acknowledged method in IS research (Coners and Matthies, 2014). The method of content analysis allows “a systematic, replicable technique for compressing many words of text into fewer content categories based on explicit rules of coding” (Stemler, 2001). As such, this method is considered useful in dealing with large volumes of data for examining trends and patterns in documents (Coners and Matthies, 2014). However, the manual coding process for the analysis of unstructured documents, as often applied in the social sciences (Coners and Matthies, 2014; Gorbacheva et al., 2016), would seem inappropriate for the purposes of our research, as it would require substantial efforts to manually code 2,331 member profiles. Hence, for data analysis and synthesis purposes, we aim to make use of the recent advances in data analytics software tools that have made the analysis of structured and unstructured contents more feasible. More specifically, we refer to IBM WEX Content Analytics as one of the leading software tools for big data text analytics (Evelson, 2016). Originally developed for the use in a business setting, WEX has proven to be a suitable tool for conducting content analyses in a scientific context, e.g. for automatically crawling, gathering and extracting data from various company sources (Shapira et al., 2016), for analyzing a high number of job advertisements for IT professionals (Bensberg and Buscher, 2016) or for an automated literature search and literature analysis (Bensberg et al., 2018). The content analytics function as offered by WEX is aimed at enhancing the visibility and understanding from the content and context of structured and unstructured information by detecting patterns, trends and deviations over time based on natural language processing (Zhu et al., 2014, p. 3).

Structured contents from our data sample include, among others, the name, gender or company fields from the member profiles. Unstructured data are contents in the fields “haves”, “interests”, “qualifications” or “wants” extracted from the member profiles (Zhu et al., 2014, pp. 12-13). WEX integrates functions of an enterprise search engine, content discovery and data mining in one tool to make sense of unstructured data and combine the results with the insights from structured data (Zhu et al., 2014, p. 14). In doing so, content analytics can help to reduce the manual workload of the literature search and text analysis during the research process. Notwithstanding the benefits that WEX offers for data analytics purposes, its academic use is limited to the mentioned examples above. By applying WEX for the data analysis and data synthesis step, we also aim to encourage researchers and practitioners to integrate and advance big data analytics as a method of inquiry. The use of data science methods such as big data analytics can help researchers to gain more timely and accurate results in further attempts to examine old questions in new ways and to explore new research questions that cannot be examined in the past due to data access or data analysis constraints (George et al., 2016; Tonidandel et al., 2018). The steps and activities conducted for the data analysis process using WEX Version 11.0.2.0 are summarised and illustrated in Figure 1.

First, the data to be analyzed must be administered by creating a new content analytics collection and importing the member profiles as .csv file in the administration console of WEX. Once the basic structures and configurations are defined, the content analytics collection can be built based on the imported data. Second, within the data configuration steps, we conduct an initial content analysis in WEX’s Content Analytics Miner prior to making iterative adjustments in the administration console. This includes a redesign of the index fields and facets structure to define what we aim to make sense out of the content to be analyzed. More specifically, we configure rule-based categories in WEX, which enable us to automatically identify business analytics competences and IT skills from the unstructured text within the haves and qualifications from each member profile. Therefore, we rely on the skills requirements identified through our literature review as described in Section 2.2. Finally, the results can be analyzed by using various views and analytics functions in the Content Analytics Miner.

4. Results

In this section, we provide a detailed analysis of the results including a brief delineation of the descriptive statistics on the study sample and the empirical results of our data analytics approach.

4.1 Descriptive statistics

As can be seen from the descriptive statistics in Table II, our study sample consists of 2,331 member profiles from 141 German companies. Among them, member profiles from the 28 DAX companies account for over one-half of the whole sample, followed by the number of profiles from the 41 TOP 100 companies (27.1 per cent) and MDAX companies (18.1 per cent). Member profiles from the “diverse” company category stem from 32 companies, but only account for 4.5 per cent of all profiles. The descriptive statistics shown in Table II also reveal that male member profiles are dominant in the study sample, with a total share of 57.1 per cent, whereas female member profiles only account for a share of 42.9 per cent of the whole sample.

According to recent statistics (Statista, 2018), 52.6 per cent of XING users are male, compared to 47.4 per cent who are female. Thus, it can be concluded that female member profiles among controlling professionals are slightly underrepresented in XING compared to those of all XING members. Beyond company classification and gender considerations, another interesting picture is offered when regarding the distribution of the member profiles according to academic degree and discipline. Although it was not able to identify the academic background of the XING users in all cases due to the lack of information, the statistics shown in Table III are based on a high number of profiles, and thus, can be considered valid.

According to academic degree, 28.6 per cent of the XING members possess the typical German “diploma” degree, followed by the master’s (15.6 per cent) and bachelor’s degree (11.4 per cent). Interestingly, the share of female members who have a bachelor’s degree is nearly equal to the share of male members, while for the diploma and master’s degree and the doctorate/PhD degree the share of female members turns out to be significantly underrepresented. According to discipline, the major share of German controlling professionals on XING have an economical background (39.1 per cent). Besides, professionals from the engineering, IS, mathematics, law and computer science disciplines are also among the member profiles from the study sample. Another interesting finding relates to the gender distribution across disciplines, showing a significant lower share of female members from the engineering, ISs and computer science discipline. This remained male domination in the STEM disciplines (Science, Technology, Engineering and Mathematics) is consistent to the findings from previous research (Baird, 2018; Beyer, 2014; Wang and Degol, 2017).

4.2 Business analytics competences and information technology skills

The first results from the data analytics procedure performed in WEX are summarized in Table IV. We analyze the data within the “facet pairs” view in the Content Analytics Miner, which enables us to examine different facets of the member profiles to detect patterns and interrelations between variables. For example, we examine the frequency of occurrence (FOC) on business analytics competences and IT skills within the member profiles according to gender or to academic background to get a deeper understanding of how these individual-level variables might influence the supply of the analytics competences. On a company level, we are able to examine the impact of revenue, number of employees, total assets or the company classification (DAX, MDAX, etc.) on the competence supplies of the controlling professionals. Due to space considerations, we intentionally refrain from presenting screenshots out of WEX, but make use of the data export function in WEX prior to summarizing and presenting the results.

Among the business analytics competences found in the member profiles of the controlling professionals, “business intelligence” constitutes the most frequently reported term. According to Chen et al. (2012), business intelligence and analytics (BI&A) are unified terms that can interchangeably be used to describe the emerging field around the analytics trend that has risen since the 1990s. After the emergence of “business analytics” in the late 2000s, in more recent years the term “big data” has been introduced to describe a related field (Chen et al., 2012), with other terms such as “data analytics”, “data mining” or “predictive analytics” being used as synonyms for different facets of BI&A. Beyond these definitions, terms that describe the different tasks in data preparation, data analytics and data application include “data science”, “statistics”, “modelling” and “programming” (Lorenz et al., 2015), which are also evident in the member profiles. As can be seen from Table IV, more recently introduced terms such as “big data”, “business analytics”, “data science”, “data analytics”, “data mining” or “predictive analytics” only occur in a few competence profiles, while “statistics” and “modelling” are evident in a higher number of profiles. However, regarding an average FOC of only 0.14 per member profile, it can be stated that business analytics competences are not widespread in the competence profiles of the controlling professionals.

A completely different picture emerges when regarding the results concerning the supply of IT skills. With an average FOC of 0.96 per member profile, IT skills are supplied by most controlling professionals. The offered IT skills include, among others, skills in handling with ERP software such as SAP and Dynamics, CRM applications, Office applications such as Excel, Word and PowerPoint, as well as skills in SQL and Data Warehouse (DWH).

On examining the total FOC on business analytics competences and IT skills in the member profiles according to gender, a gap between male and female member profiles becomes apparent (Figure 2).

Despite the fact that 57.1 per cent of the total study sample are male and 42.9 per cent are female, the share of male users that have reported business analytics competences is 69.1 per cent, while for the female users this figure is only 30.9 per cent. As far as the supply of IT skills are concerned, the figures are more balanced, but show a similar tendency. With a share of 62.2 per cent, male users report to have IT skills, while for female users this figure is 37.8 per cent. These findings are also underlined by the correlations reported in Table IV. The correlations are automatically calculated by WEX and represent the strength of connection between different facets (variables). Mathematically, this value is calculated by estimating the ratio of two density values (Zhu et al., 2014, p. 192):

  1. the density of the given set of member profiles that includes the business analytics competences (e.g. “business intelligence”, “data science”); and

  2. the density of the entire set of the member profiles in the whole collection.

Thus, the correlation value reported in Table IV demonstrates how highly connected two variables are to each other (Zhu et al., 2014, p. 193).

Based on the values reported in Table IV and illustrated in Figure 2, it can be concluded that male users tend to report analytics competences more frequently while female users do this less frequently. As already emphasized in previous studies (Gorbacheva et al., 2016), this observed gender gap does not automatically imply that male controlling professionals indeed possess more business analytics competences and IT skills than their female counterparts. Instead, gender-related issues such as lower confidence and self-efficacy might be accountable for these differences (Gorbacheva et al., 2016). A possible explanation for these gender differences can also be found in the theory of impression management, which describes the process by which individuals attempt to manage and control the impressions others form of them, depending on their motivation (Gardner and Martinko, 1988; Leary and Kowalski, 1990). A recent study conducted on the employment of impression management tactics in organizational settings concluded that there exist substantial gender differences between male and female employees in attempting to conform to gender-role-based expectations (Guadagno and Cialdini, 2007). Among others, Guadagno and Cialdini (2007) report that male employees engage in self-promotion or self-enhancement by boasting or emphasizing their best characteristics more than their female counterparts do. Hence, it can be expected that they show the same behaviour in business social network context by over-emphasizing their skills and competences as well.

Apart from the gender differences, a potential moderating effect can be observed when regarding the average FOC according to the academic degree of the employees as shown in Figure 3.

At a first glance, the results indicate that the business analytics competence supplies depend on the academic degree in the sense that the higher the academic degree, the more competence supplies can be expected from the competence profile. However, the relatively low FOC for the diploma and magister degree reveal that this association might be misleading, as the level of the German diploma and magister degree is equal to the master’s degree. At this point it must be mentioned that the diploma degree has been replaced by the bachelor’s and master’s degree since the implementation of Bologna (BMBF, 2018). Hence, the majority of employees who hold a diploma and magister degree are older and have more years of professional experience than bachelor’s and master’s graduates do. Thus, it can be assumed that a mix between academic background, age and professional experience might account for the differing business analytics competence supplies of the employees. This assumption is supported by the figures presenting the average FOC on IT skills in Figure 3. The results reveal that controlling professionals with a bachelor’s and master’s degree show the highest supply of IT skills, followed by professionals with a diploma, magister and doctorate/PhD degree. However, due to the lack of information, we cannot examine the association between age and competence supplies to verify and validate this assumption. Instead, this is an important issue to be investigated in future research.

Another interesting finding emerges when regarding the FOC on business analytics competences and IT skills according to company classification. Guided by previous research on the role of resource constraints on ICT adoption (Arendt, 2008; Thong, 2001), we have initially expected that large enterprises such as DAX30 companies tend to be faster and more successful in adopting ICT, and thus, data analytics competences and IT skills are more evident in the competence profiles of controlling professionals of DAX companies rather than in those of the other company classifications. The results shown in Table V and Figure 4 reveal that the opposite is the case, as member profiles from DAX employees show the lowest average FOC on analytics competences and IT skills (0.11 and 0.84), followed by those from the TOP 100 companies (0.15 and 1.03), MDAX companies (0.19 and 1.13) and others (0.21 and 1.13).

Given these results, the question arises what might account for these surprising observations considering the variability across different company classifications. At first sight, the assumption is near that there exist biases that are rooted in the classifications made to group the companies, as the TOP 100 companies cluster also comprises large enterprises with similar characteristics such as DAX companies with respect to number of employees, annual revenue or value of assets. Hence, we also examine the FOC on business analytics competences and IT skills according to other classifications to explore the impact of these characteristics on the results.

As can be seen from Figure 5, a comparison of the FOC on business analytics competences according to the number of employees confirm the findings shown in Table V. Member profiles from companies with more than 100,000 employees show the lowest average FOC on business analytics competences and IT skills, whereas the highest value is achieved in member profiles of companies with 1,000-10,000 employees. The results according to the revenue or value of assets show similar results, indicating that business analytics competences and IT skills can be found less frequently in very large companies rather than large or middle-sized companies.

One possible explanation can be derived from the findings of previous research in controlling and MA literature, which indicate an increased specialization of the controllers’ and MA’s tasks depending on company size (Becker and Ulrich, 2009). Hence, we can assume that very large companies that have adopted big data might prefer to hire specialists such as data scientists instead of upskilling the company’s controllers for conducting data analytics tasks, while in less large companies the tasks of the controllers might have been broadened more often to encompass data analytics tasks. Thus, in DAX companies the data analytics tasks are conducted by data scientists, while in MDAX companies these tasks are the controller’s duty. Another interesting finding relates to the observation that the average FOC on analytics competences tend to decline in member profiles of companies with less than 1,000 employees. This indicates that in smaller companies the theory of resource constraints might be more applicable (Arendt, 2008; Thong, 2001), and thus, employees occupied in these companies do not need to possess data analytics skills.

5. Explanatory model

The quantitative results presented above raise several questions about why differences in the competence supplies occur in the member profiles of controlling professionals with respect to company size, gender and other characteristics. We aim to provide an enhanced understanding towards the rationales behind the observed findings by proposing a theoretically founded explanatory model that integrates empirically validated findings from extant research across various disciplines. As shown in Figure 6, the explanatory model consists of two sub-models that explain the emergence of the skills requirements on business analytics competences (demand side) while the supply side of the business analytics competences is described in the third sub-model.

The first part of the explanatory model delineates the forces that account for the necessity of business analytics competences as one main part of the controller’s and MA’s competence profile to deal with the adoption of new IT and IS such as big data. Guided by the resource-based view (RBV) of the firm (Barney, 2001; Wernerfelt, 1984), previous research from the information systems discipline has highlighted the role of company size as one major antecedent for the implementation success of both IT and IS. More specifically, it has been argued that resource constraints constitute an inhibiting factor to the successful IT and IS adoption in SMEs, as limited financial, time and knowledge resources prevent them from extensive investments in IS and IT compared to larger companies (Arendt, 2008; Caldeira and Ward, 2003; Thong, 2001). By exclusively focussing on investigating member profiles of controlling professionals occupied in foremost large companies, we assume that the adoption rate of IT in general and big data in particular is higher in these organizations, and thus, business analytics competences and IT skills are more likely to be supplied by the employees of large enterprises rather than of those occupied in SMEs. However, the adoption of IT in general and big data in particular does not automatically imply that companies need controllers or MAs with business analytics competences and advanced IT skills. As indicated by the results of this study, controlling professionals from the largest companies in terms of number of employees, revenue or value of assets report the lowest competence supplies in business analytics and IT skills. The second part of the sub-model based on the well-known principles of organizational theory delivers the rationale for this surprising finding.

Guided by Adam Smith’s (1817) idea towards the division of labor, Mintzberg (1990, p. 67) describes job specialization as one major design parameter to define the structure of an organization. Job specialization refers to the degree to which organizational tasks are broken down into individual jobs for productivity reasons (Mintzberg, 1990, p. 70). Extant research has empirically validated the impact of company size on the degree of specialization within companies, indicating that the degree of job specialization is positively correlated with an increase in company size (Kieser and Kubicek, 1992, pp. 301-303). Empirical results from controlling and MA literature have confirmed this finding by demonstrating an increased specialization of the controllers’ and MA’s tasks depending on company size (Becker and Ulrich, 2009). Thus, company size and organizational structure are determinants to the job profile that account for the controller’s skills requirements. The arrow in both directions between resources and skills requirements represent the link between the first sub-models, as the employees’ skills and competences constitute one of the major resources for a successful adoption of new IT. In line with organizational theory, the results of this study reveal that employees from the largest companies report the less competence supplies in business analytics and IT skills. We assume that, due to the high degree of job specialization, business analytics and IT-intensive tasks do not belong to the controller’s job profile in these cases, but rather are integrated into another job profile such as those of the data scientist or data analyst. In the case that the degree of job specialization is medium or rather low, business analytics competences and IT skills tend to be more necessary for the controller’s skills requirements, as supported by the results of this study. Overall, it can be concluded that the extent to which business analytics competences and IT skills are required in a controller’s job profile is determined by the level of IT and big data adoption, as well as the degree of job specialization, which, in turn, both depends on company size.

Apart from the demand side as illustrated by the first two sub-models, the supply side is focussed on explaining the competence supply, and thus, reflects the perspective of the employees. As presented in Section 4, we have found several possible moderators that account for the variability of the competence supplies. Explanations for these results can be found in the literature on computer self-efficacy from across diverse disciplines such as the IS, educational and psychology research field. Technology and computer self-efficacy have been subject in a plenty number of studies from the past, with empirically validated results showing that significant gender differences exist in the sense that men tend to report greater computer self-efficacy than do women (Cassidy and Eachus, 2002; Durndell et al., 2000; Durndell and Haag, 2002; He and Freeman, 2010; Vekiri and Chronaki, 2008).

Computer self-efficacy is based on the more general construct of self-efficacy as a key element of Bandura’s (1977) social cognitive theory (He and Freeman, 2010). Originating from the social psychology domain, social cognitive theory puts emphasis on the role of self-efficacy as the individuals’ belief that one has the capability to take actions to reach desired outcomes (Bandura, 1977). In line with the findings of this study, age and job experience has been found to be significant antecedents of computer self-efficacy in former studies (He and Freeman, 2010; Reed et al., 2005). Thus, computer self-efficacy is assumed to be one major determinant of the supply of business analytics competences.

In summary, the controller’s skills gap can be uncovered by comparing the skills requirements defined on the demand side with the competence supplies on the supply side. The arrow in both directions between the variables “skills requirements” and “competence supplies” represent a balancing relationship.

Based on the quantitative results presented in Sections 4 and 5, we finally summarize the answers to the research questions as posed in Section 1 in Table VI.

6. Discussion

6.1 Implications for research and practice

Overall, the findings of this study are expected to be of value for research and practice for various reasons. In comparing the controller’s recent skills requirements with the current competence profiles of controlling professionals, we have observed an apparent skills gap. Although business analytics is recently considered a major part of the controller’s and MA’s skills profile (IMA, 2018b), the results of this study indicate that the current competence profiles of controlling professionals do not comply with the requirements of the controller of the future, as business analytics is supplied in only 14 per cent of all current competence profiles. Obviously, there appears to be a discrepancy between desire and reality. However, we must caution against this conclusion. Instead, the answer to the question whether a skills gap exists must be given individually by taking into account several contextual variables such as the adoption level of IT and big data and the degree of job specialization, as illustrated by the explanatory model. Thus, the implications for organizations that may arise from this finding will depend on the strategic direction that companies intend to take with respect to the adoption of data analytics techniques and their organizational structure.

In business media and academic literature, a great deal of controversy exists about the question whether the ideal picture of the controller with business analytics skills is only wishful thinking or will manage to become reality within the upcoming years. While some consulting firms and practitioners claim that programming, statistical and modelling skills do fall within conventional controlling and MA skills (Egle and Keimer, 2017; Mödritscher and Wall, 2017), others state that the pleas for big data and analytics may be unrealistic if finance departments have still not managed to keep basic systems running (Payne, 2014). Regardless of this debate, companies must decide whether it is necessary to get their controlling professionals upskilled or to hire specialists such as data scientists for the new tasks when adopting data analytics. In this respect, the results of our study indicate that the supply of business analytics competences and IT skills in controlling professional’s profiles tends to decrease with company size. Thus, the assumption is near that in large organizations such as DAX companies, controllers do not need business analytics competences and advanced IT skills as these skills constitute a major part of a data scientist’s job profile rather than of a controller’s or MA’s job profile. Future research could focus on investigating whether the association between the contextual variables on the supply of business analytics competences and IT skills exists and how the competence profiles of the controller and data scientist can be defined more sharply in different organizational contexts.

In the case that the controller’s tasks will indeed be enlarged by the new data analytics tasks, companies must attempt to close the skills gap. Organizations across diverse industries will be forced to make significant investments in formal education and training to remain competitive. Based on the results of a survey of 500 financial executives and managers, the Institute of Management Accountants and Robert Half (Krumwiede, 2016) emphasize the need for skills in the field of strategic data-driven analyses using business analytics processes. The results reveal that many companies are experiencing significant skills gaps in the business analytics expertise of their MAs while facing several difficulties in finding qualified personnel with the required skills (Krumwiede, 2016). It is an acknowledged fact that companies need to provide opportunities for employees to develop appropriate skills sets for business analytics through formal trainings (Brands and Holtzblatt, 2015). Thus, skills that are required for the future controller and MA need to be integrated into formal job trainings for employees within organizations and taught at a tertiary level to adequately prepare the graduates for the new challenges. The development of new curricula and their effective integration into educational settings constitutes a further avenue of research.

From the perspective of the employees, there is one main implication to be mentioned. First, the differences in the supply of business analytics competences and IT skills reveal that female members tend to underemphasize their competences compared to their male counterparts. These differences, in turn, can possibly account for the differential success in their job career in terms of potential job offers, advancements and salaries. Thus, female members are well-advised to act more confidently by highlighting their skills and competences in their profiles in an authentic way. Finally, another major purpose of our study is to demonstrate the usefulness of big data analytics for conducting rigorous and efficient research. By applying the big data content analytics tool IBM WEX Content Analytics for the data analysis step, we aim to encourage researchers and practitioners to integrate data analytics tools into their research process and thus to make use of recent advances in big data and business analytics.

6.2 Limitations

As with any exploratory research of this kind, there exist limitations that must be taken into account. First, the data sample integrated into this study is restricted to member profiles of controlling professionals from the German-speaking area. Besides, we have intentionally focussed on examining the skills profiles of controlling professionals from foremost large companies such as DAX and TOP 100 firms due to the accessibility and availability of company-level data. This limitation prevents us from drawing conclusions on a wider scale. Instead, the results gained from the member profiles only reflect the skills levels of employees from large companies and does not include insights into the skills levels of the employees from SMEs or other countries. Further research is needed to extend and validate the findings of this research with findings based on data from other geographical areas and SMEs. Likewise, the investigation of member profiles of MA professionals from the English-speaking area such as the USA and UK would be helpful in identifying similarities or differences in the supplies of business analytics competences.

A second limitation relates to the quality of the examined data. Although we have conducted a data cleansing step to exclude fake member profiles and profiles with no relevant content, we cannot guarantee that the information concerning the self-reported skills and competences provided in the member profiles are accurate and true. Past research from the psychology field has highlighted impression management as a major motive for individuals to actively participating in social networking sites (Krämer and Winter, 2008). The involvement in online social networks enables users to create online self-presentations that can be tailored in accordance to their desired image in an attempt to influence the perceptions that the audience forms of them (Krämer and Winter, 2008; Paliszkiewicz and Mądra-Sawicka, 2016). This behaviour is guided by the theory of impression management, which describes the process by which individuals attempt to manage and control the impressions others form of them, depending on their motivation (Gardner and Martinko, 1988; Leary and Kowalski, 1990).

Previous research from across diverse disciplines such as psychology, educational research and information systems has indicated that individuals tend to overestimate their skills and competences in different contexts. For example, the accuracy of self-assessed user competence has already been subject of a study conducted by Gravill et al. (2006), providing evidence for the Dunning–Kruger effect (Dunning et al., 2003), according to which individuals fail to accurately self-assess their IT knowledge. A more recent study on the validity of subjective self-assessment of digital competence among pre-service teachers has concluded that the study participants consistently overestimate their digital competence (Maderick et al., 2016). In a similar manner, another study on the information literacy (IL) of students from across diverse disciplines has also indicated that the participants overestimated their self-reported IL skills compared to their actual skills in most cases (Mahmood, 2016). However, on examining the validity of self-presentation in member profiles on the German business network XING, a recent study concluded that individuals present themselves rather authentically than idealized on XING (Sievers et al., 2015). In line with this conclusion, we assume that the information yielded from the member profiles are authentic and true. However, a triangulation of results is beneficial to validate the results of this research, e.g. by conducting structured interviews or surveys to look behind the self-reported competences from the member profiles.

Another limitation is concerned with the content analytics approach that we have applied to examine the member profiles. By integrating WEX into the research process, we also aim to demonstrate the helpfulness of big data analytics for exploratory research purposes. However, WEX is not aimed at conducting advanced statistical analyses to empirically test hypotheses. Hence, the application of inferential statistics, e.g. to measure the association between the different variables using different kinds of test such as one-way ANOVA or Pearson’s r is beyond the scope of this paper.

7. Conclusion

In the current study, an explicit attempt was made to integrate recent advances in data analytics to explore the self-reported competence supplies of controlling professionals based on a comprehensive set of structured and unstructured data. In doing so, we provide insights into the skills gap that apparently exists between the controller with business analytic competences and the contemporary competence profile of the controlling professionals. Apart from this apparent skills gap, we have observed gender differences in the supply of business analytics competences and a moderating effect of company size on the competence supplies of employees. Guided by the RBV of the firm, organizational theory and social cognitive theory, we develop an explanatory model, which integrates the main findings of this research with empirically validated findings from extant research to gain an enhanced understanding of the observed phenomena. Overall, this study contributes to research and practice in a variety of disciplines, including IS research, accounting and management science by addressing an interdisciplinary and timely topic of interest.

Figures

Data analysis steps in IBM WEX including the required activities

Figure 1.

Data analysis steps in IBM WEX including the required activities

Comparison of the average FOC on business analytics competences and IT skills according to gender

Figure 2.

Comparison of the average FOC on business analytics competences and IT skills according to gender

Comparison of the average FOC on business analytics competences and IT skills according to academic degree

Figure 3.

Comparison of the average FOC on business analytics competences and IT skills according to academic degree

Comparison of the average FOC on business analytics competences and IT skills according to company classification

Figure 4.

Comparison of the average FOC on business analytics competences and IT skills according to company classification

Comparison of the average FOC on business analytics competences and IT skills according to number of employees

Figure 5.

Comparison of the average FOC on business analytics competences and IT skills according to number of employees

Explanatory model for the skills requirements towards business analytics

Figure 6.

Explanatory model for the skills requirements towards business analytics

Extracted data from the XING member profiles

Data category Content
Personal data First name and last name
Position Academic degree, job title and occupation type
Employer data Company name, company site and industry
Profile Haves, interests, qualifications and wants

Study sample according to company classification and gender of member profiles

Companies Member profiles Whole sample
Classification No. of companies Male Female Total
N (%) N(%) N (%) N (%)
DAX 28 19.9 675 57.5 499 42.5 1,174 50.4
MDAX 40 28.4 248 58.9 173 41.1 421 18.1
TOP 100 41 29.1 347 54.9 285 45.1 632 27.1
Diverse 32 22.7 61 58.7 43 41.3 104 4.5
Total 141 100.0 1,331 57.1 1,000 42.9 2,331 100.0

Study sample according to academic degree and discipline

Profile characteristics Male Female Whole sample
N (%) N (%) N (%)
Academic degree
Diploma 403 60.5 263 39.5 666 28.6
Master 220 60.4 144 39.6 364 15.6
Bachelor 139 52.3 127 47.7 266 11.4
Doctor/PhD 17 77.3 5 22.7 22 0.9
Magister 1 50.0 1 50.0 2 0.1
Diverse 47 68.1 22 31.9 69 3.0
Not specified 403 60.5 263 39.5 666 28.6
   
Discipline
Economics 522 57.3 389 42.7 911 39.1
Engineering 110 83.3 22 16.7 132 5.7
Information systems 16 80.0 4 20.0 20 0.9
Mathematics 9 56.3 7 43.8 16 0.7
Law 5 62.5 3 37.5 8 0.3
Computer science 5 83.3 1 16.7 6 0.3
Diverse 8 50.0 8 50.0 16 0.7
Not specified 656 53.7 566 46.3 1,222 52.4
Total 1,331 57.1 1,000 42.9 2,331 100.0

FOC on business analytics competences and IT skills according to gender

Competences and skills Male Female Total
N (%) Corr. N (%) Corr. N(%)
Business analytics
Business intelligence 127 72.2 1.04 49 27.8 0.46 176 7.6
Data science 2 40.0 0.05 3 60.0 0.20 5 0.2
Statistics 30 57.7 0.66 22 42.3 0.59 52 2.2
Modelling 31 72.1 0.83 12 27.9 0.31 43 1.8
Programming 10 76.9 0.63 3 23.1 0.07 13 0.6
Big data 10 71.4 0.58 4 28.6 0.13 14 0.6
Business analytics 6 54.5 0.28 5 45.5 0.26 11 0.5
Data analytics 5 71.4 0.32 2 28.6 0.05 7 0.3
Data mining 4 80.0 0.29 1 20.0 0.01 5 0.2
Predictive analytics 1 100.0 0.02 0 0.0 0.00 1 0.0
Total FOC 226 69.1   101 30.9   327 14.0
Total study sample 1,331 57.1   1,000 42.9   2,331 100.0
Average FOC 0.17     0.10     0.14  
IT skills
SAP 551 59.8 0.96 371 40.2 0.84 922 39.6
ERP 80 71.4 0.97 32 28.6 0.43 112 4.8
Dynamics 9 47.4 0.36 10 52.6 0.55 19 0.8
CRM 16 61.5 0.58 10 38.5 0.40 26 1.1
Office 289 58.4 0.90 206 41.6 0.83 495 21.2
Excel 230 62.3 0.94 139 37.7 0.72 369 15.8
VBA 125 72.3 1.04 48 27.7 0.45 173 7.4
SQL 65 76.5 1.01 20 23.5 0.31 85 3.6
DWH 21 77.8 0.82 6 22.2 0.15 27 1.2
Total FOC 1,386 62.2   842 37.8   2,228 95.6
Total study sample 1,331 57.1   1,000 42.9   2,331 100.0
Average FOC 1.04     0.84     0.96  

FOC on Business analytics competences and IT skills according to company classification

Competences and skills DAX TOP 100 MDAX Diverse Total
N (%) N (%) N (%) N (%) N(%)
Business analytics
Business intelligence 70 39.8 52 29.5 42 23.9 12 6.8 176 7.6
Data science 2 40.0 2 40.0 1 20.0 0 0.0 5 0.2
Statistics 23 44.2 11 21.2 13 25.0 5 9.6 52 2.2
Modelling 15 34.9 14 32.6 11 25.6 3 7.0 43 1.8
Programming 8 61.5 2 15.4 2 15.4 1 7.7 13 0.6
Big data 4 28.6 6 42.9 4 28.6 0 0.0 14 0.6
Business analytics 3 27.3 4 36.4 3 27.3 1 9.1 11 0.5
Data analytics 2 28.6 3 42.9 2 28.6 0 0.0 7 0.3
Data mining 2 40.0 1 20.0 2 40.0 0 0.0 5 0.2
Predictive analytics 0 0.0 0 0.0 1 100.0 0 0.0 1 0.0
Total FOC 129 39.4 95 29.1 81 24.8 22 6.7 327 14.0
Total study sample 1,174 50.4 632 27.1 421 18.1 104 4.5 2,331 100.0
Average FOC 0.11   0.15   0.19   0.21   0.14  
IT skills
SAP 427 46.3 264 28.6 192 20.8 39 4.2 922 282.0
ERP 46 41.1 38 33.9 21 18.8 7 6.3 112 34.3
Dynamics 7 36.8 6 31.6 1 5.3 5 26.3 19 5.8
CRM 10 38.5 11 42.3 4 15.4 1 3.8 26 8.0
Office 220 44.4 138 27.9 113 22.8 24 4.8 495 151.4
Excel 153 41.5 102 27.6 92 24.9 22 6.0 369 112.8
VBA 84 48.6 51 29.5 29 16.8 9 5.2 173 52.9
SQL 32 37.6 26 30.6 18 21.2 9 10.6 85 26.0
DWH 7 25.9 12 44.4 7 25.9 1 3.7 27 8.3
Total FOC 986 44.3 648 29.1 477 21.4 117 5.3 2,228 95.6
Total study sample 1,174 50.4 632 27.1 421 18.1 104 4.5 2,331 100.0
Average FOC 0.84   1.03   1.13   1.13   0.96  

Main findings responding to the research questions

# Research questions and corresponding answers
RQ1 What business analytics competences and IT skills are currently supplied by controllers and to what extent are these skills and competences evident in their skills profiles?
P1a The business analytics competences found in the investigated competency profiles include expertise in business intelligence as the most frequent reported competence supply, followed by expertise in statistics, modelling and programming needed for data science tasks. Besides, big data, data analytics, data mining and predictive analytics are also among the self-reported competences
The supplied IT skills include skills in handling with ERP software such as SAP and Dynamics, CRM applications, Office applications such as Excel, Word and PowerPoint, as well as skills in SQL and Data Warehouse (DWH)
P1b Based on the quantitative results, it can be stated that business analytics competences are not widespread in the competency profiles of the current controlling professionals, as business analytics is supplied in only 14% of all member profiles. A completely different picture emerges when regarding the results concerning the supply of IT skills. With an average FOC of 0.96 per member profile, IT skills are supplied by most controlling professionals
RQ2 Does a skills gap exist between the controllers’ new skills profile and the currently supplied skills profile?
P2a In comparing the controller’s new skills requirements with the competence supplies evident in their skills profile, it can be concluded that a skills gap does exist with respect to the supply of business analytics competences ( P1b)
P2b However, it must be questioned if the new ideal picture of the controller will become reality for all controlling professionals in the future. The answer to this question must be derived individually depending from various factors that can be found within the organizational context (e.g. level of ICT adoption, degree of job specialization)
RQ3 What are possible moderators that might affect the supply of business analytics competences and IT skills?
P3a Possible moderators of business analytics competence and IT skills supplies include company size, gender, age and job experience of the employees
P3b More research efforts are needed to empirically test and validate the moderating role of these variables, as this is beyond the scope of this paper

Note

1.

One zettabyte is equivalent to a trillion gigabytes (Reinsel et al., 2018, p. 7).

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Corresponding author

Thuy Duong Oesterreich can be contacted at: toesterreich@uni-osnabrueck.de

About the authors

Thuy Duong Oesterreich is a research associate at the research group of Accounting and Information Systems, which is part of the Institute of Information Management and Information Systems Engineering at the Osnabrück University, School of Business Administration and Economics. She obtained her diploma degree in Business Administration at the University of Applied Sciences in Osnabrück in 2008 and is currently carrying on her PhD in Information Systems. Her research interests are focussed on the socio-economic implications of digitization and automation in the context of Industry 4.0.

Frank Teuteberg is a full-time Professor at the Osnabrück University in Germany. Since 2007, Frank Teuteberg has been the Head of the Department of Accounting and Information Systems, which is part of the Institute of Information Management and Information Systems Engineering at the Osnabrück University. Frank Teuteberg is a member of the German Logistics Association. He is the Founder of the research network ERTEMIS (www.ertemis.eu) and Leader of several research projects (e.g. www.ecoinnovateit.de or www.dorfgemeinschaft20.de). Furthermore, he is an Author of more than 270 papers in the field of cloud computing, industrial internet of things, sustainable supply chain management and human–computer interaction.