Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation

Passlick, Jens; Grützner, Lukas; Schulz, Michael; Breitner, Michael H. Article — Published Version Self-service business intelligence and analytics application scenarios: A taxonomy for differentiation Information Systems and e-Business Management Provided in Cooperation with: Springer Nature Suggested Citation: Passlick, Jens; Grützner, Lukas; Schulz, Michael; Breitner, Michael H. (2023) : Selfservice business intelligence and analytics application scenarios: A taxonomy for differentiation, Information Systems and e-Business Management, ISSN 1617-9854, Springer, Berlin, Heidelberg, Vol. 21, Iss. 1, pp. 159-191, https://doi.org/10.1007/s10257-022-00574-3 This Version is available at: https://hdl.handle.net/10419/309011 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Information Systems and e-Business Management (2023) 21:159–191 https://doi.org/10.1007/s10257-022-00574-3 1 3 ORIGINAL ARTICLE Self‑service business intelligence andanalytics application scenarios: Ataxonomy fordifferentiation JensPasslick1 · LukasGrützner1· MichaelSchulz2· MichaelH.Breitner1 Received: 5 September 2021 / Revised: 8 July 2022 / Accepted: 22 September 2022 / Published online: 5 February 2023 © The Author(s) 2023 Abstract Self-service business intelligence and analytics (SSBIA) empowers non-IT users to create reports and analyses independently. SSBIA methods and processes are discussed in the context of an increasing number of application scenarios. However, previous research on SSBIA has made distinctions among these scenarios only to a limited extent. These scenarios include a wide variety of activities ranging from simple data retrieval to the application of complex algorithms and methods of analysis. The question of which dimensions are suitable for differentiating SSBIA application scenarios remains unanswered. In this article, we develop a taxonomy to distinguish among SSBIA applications more effectively by analyzing the relevant scientific literature and current SSBIA tools as well as by conducting a case study in a company. Both researchers and practitioners can use this taxonomy to describe and analyze SSBIA scenarios in further detail. In this way, the opportunities and challenges associated with SSBIA application can be identified more clearly. In addition, we conduct a cluster analysis based on the SSBIA tools thus analyzed. We identify three archetypes that describe typical SSBIA tools. These archetypes identify the application scenarios that are addressed most frequently by SSBIA tool providers. We conclude by highlighting the limitations of this research and suggesting an agenda for future research. Keywords Self-service· Business intelligence· SSBIA application scenarios· Taxonomy· Software archetypes 1 Introduction The success of companies often depends on the right decisions being made at the right time. This dependence can apply to both strategic and operational decisions. In this context, the goal of modern companies is to make more decisions based on * Jens Passlick passlic[email protected]er.de Extended author information available on the last page of the article 160 J.Passlick et al. 1 3 facts and figures instead of making purely subjective decisions (Bani-Hani et al. 2019). Information has become an essential asset for companies, necessitating the use of business intelligence (BI) systems for future development to remain competitive (Tavera Romero etal. 2021). This development leads to higher demands on a BI environment, which should provide the information that is necessary for decision-making (Michalczyk et al. 2020). However, easy and flexible access to data is a major problem in conventional BI architectures, as classical BI structures are often too rigid and slow (Imhoff and White 2011; Bani-Hani etal. 2019). Changes to reports and the creation of new analyses are largely the responsibility of the IT department. Enabling business departments to produce reports and analyses on their own may be a solution to this problem (Bani-Hani etal. 2018a). The ability of business departments to create reports and analyses by themselves is often summarized under the term self-service business intelligence (Alpar and Schulz 2016). Requirements in the context of analytics are also added more frequently, which is why the term should now be taken to include self-service business intelligence and analytics (SSBIA) applications (Chen etal. 2012; Michalczyk etal. 2020). For example, business departments increasingly want to identify patterns and outliers in very diverse data. Therefore, it would be helpful if such data analyses could be carried out flexibly and immediately by the business department itself (Passlick etal. 2020). In recent years, software manufacturers have tried to offer increasingly simple and, most importantly, target group-oriented SSBIA tools (Eckerson 2019). The importance of SSBIA has also been demonstrated by surveys such as the “Data, BI & Analytics Trend Monitor” from the Business Application Research Center (BARC 2022). This survey is based on the opinions of 2400 industry practitioners and shows that the implementation of SSBIA has consistently ranked among the top five most important trends over the past 5years in the area of data management and BI (BARC 2022). Alpar and Schulz (2016, p. 151) describe the goal of SSBI as to “… empower casual users to perform custom analytics and to derive actionable information from large amounts of multifaceted data without having to involve BI specialists. Power users, on the other hand, can accomplish their tasks with SSBI more easily and quickly than before”. Various aspects of an SSBIA approach have been discussed previously. Different perspectives, user roles, experiences, and self-service levels have been investigated in the context of SSBIA research (Michalczyk etal. 2020). In particular, the diverse levels of self-service illustrate how different SSBIA application scenarios can be (Alpar and Schulz 2016). Alpar and Schulz (2016) distinguish these levels based on only two dimensions: self-reliance and system support. In other publications, additional dimensions are addressed to differentiate the levels of self-service further, such as the user roles included or the experiences of the users (Passlick etal. 2017, 2020; Weiler etal. 2019). The necessary data management, which varies in terms of complexity, can be used to make strict distinctions among different SSBIA application scenarios (Imhoff and White 2011). These aspects highlight the research gap regarding the necessity of obtaining a more detailed understanding of the dimensions of SSBIA. The lack of a detailed SSBIA classification and an identification of different application scenarios leads to certain problems. For example, SSBIA application developers must identify the SSBIA level for which 161 1 3 Self‑service business intelligence andanalytics application… they intend to create applications precisely to address the relevant requirements in the best possible way (Johansson etal. 2015). The value of SSBIA for a company is also extremely dependent on the SSBIA application scenarios in question. Previous research has not necessarily taken the stronger differentiation that is made possible by more target group-oriented SSBIA tools into account. In addition, the question of which application scenarios currently exist in practice remains unanswered. To provide a detailed description of these application scenarios, we propose our research question (RQ): What dimensions and characteristics distinguish SSBIA application scenarios and what scenarios currently exist? Based on these dimensions and characteristics, various SSBIA applications can be described and investigated more effectively. First, we discuss the literature on SSBIA levels. Subsequently, we develop our taxonomy by using an iterative procedure following the suggestions of Nickerson etal. (2013). For this purpose, we use not only our findings based on previous publications but also our analysis of SSBIA tools and a case study. Thereafter, we deduce our final taxonomy. We continue to investigate the question of which SSBIA applications are supported by SSBIA tools currently on the market. Using our taxonomy, we classify these tools and conduct a cluster analysis. Based on the clusters thus discovered, archetypes can be formed that allow conclusions to be drawn regarding the SSBIA application scenarios that are increasingly being addressed by SSBIA tool providers. Finally, we discuss our results and findings, their implications, the resulting recommendations, and the limitations of this research; we also highlight further research opportunities. 2 Knowledge regardingSSBIA dimensions Throughout the remainder of this article, we refer to the definition of SSBIA by Alpar and Schulz (2016, p. 151) as the empowerment of “casual users to perform custom analytics and to derive actionable information from large amounts of multifaceted data”. In this context, analytics includes the use of advanced algorithms and analytic models for diagnostic, predictive, or prescriptive purposes (Moore 2017). However, the important factor in this context is that end users can take advantage of these applications despite the fact that their job descriptions do not primarily involve statistical or analytical activities (Moore 2017). This empowerment can be the key to allowing organizations to become data-driven organizations (Mullarkey etal. 2019). Research has focused on different aspects of SSBIA. Imhoff and White (2011) conduct a survey to identify the relevant challenges and opportunities from a practical perspective. These challenges and opportunities can be summarized in terms of the ease of use of the software, the accessibility of the data, data management, and easy deployment (Imhoff and White 2011). Johansson etal. (2015) differentiate SSBIA from traditional BI using the PACT framework. The PACT framework comprises the dimensions People, Activity, Context, and Technology (Benyon 2014). A frequently quoted article by Alpar and Schulz (2016) offers a first overview 162 J.Passlick et al. 1 3 of SSBIA. Alpar and Schulz (2016) describe various levels of SSBIA. They differentiate these levels based on the dimensions “system support” and “self-reliance” (Alpar and Schulz 2016). Figure1 shows the levels addressed and the dimensions by which they are differentiated. These three levels can easily be differentiated based on these two dimensions. The question of whether further dimensions are necessary to differentiate SSBIA applications in other contexts remains open. Ogushi and Schulz (2016) conduct a literature analysis to identify the dimensions of technology, data, presentation, and social features. Bani-Hani etal. (2019) analyze business employees’ independence and the value that is cocreated in this context. Similar to the findings of Alpar and Schulz (2016), they identify three constellations of SSBIA that create value. These constellations differ based on how independent from the IT department business users are able to work. However, the steps these authors identify are slightly different. They differ based on whether business users are responsible for interpretation (level C), analysis and visualization (level B), or data preparation and gathering (level A) (Bani-Hani etal. 2019). Based on a literature analysis, Lennerholt and van Laere (2019) analyze the challenges of introducing SSBIA. These authors identify the accessibility and usability of data as well as data quality as major groups associated with the challenges of introducing SSBIA (Lennerholt and van Laere 2019). Thus, they identify completely different dimensions than those identified by Alpar and Schulz (2016). Michalczyk etal. (2020) analyze the SSBIA research conducted to date. These authors categorize the literature according to different levels of selfservice. For this purpose, they use the levels identified by Alpar and Schulz (2016). They also use the dimensions of perspective, user role, and experience for such differentiation. In this context, other dimensions are also used. The importance of SSBIA efforts with respect to addressing various user types is evident in various works by Eckerson (2012, 2014, 2019). These user types are related to different analytical tools. Fig. 1 Differentiation of SSBIA levels according to Alpar and Schulz (2016, p. 152) 163 1 3 Self‑service business intelligence andanalytics application… In the maturity model developed by Halper (2017), other SSBIA dimensions are once again described. These dimensions are known as organization, data management, infrastructure, analytics, and governance (Halper 2017). However, this model does not classify individual SSBIA application scenarios but rather describes the maturity of the entire organization with regard to SSBIA, e.g., the extent to which an SSBIA culture prevails in the company; however, this factor is not directly relevant to our objectives. It can be seen that SSBIA has previous been discussed from different perspectives. Various dimensions have been identified, thus highlighting the different requirements for an SSBIA environment that emerge depending on the application in question. However, a clear approach to the task of differentiating these application scenarios has not yet been developed. The work of Alpar and Schulz (2016) provides preliminary insights, but the literature described above shows that many different perspectives on SSBIA can be adopted with respect to the possible applications that can be differentiated. Furthermore, an increasing number of companies are also addressing this topic and discussing the use of SSBIA in different areas (Gartner 2018). Our research addresses this research gap and the associated needs. 3 Development ofthetaxonomy 3.1 Research design andmethodology “A fundamental problem in many disciplines is the classification of objects of interest into taxonomies” (Nickerson et al. 2013, p. 336). Classification systems such as taxonomies, which are often referred to as typologies, help by structuring and organizing knowledge. Taxonomies uncover and classify objects based on common characteristics and explain their correlations to each other, which allows researchers to understand and analyze complex fields (Glass and Vessey 1995; Varshney etal. 2015; Nickerson etal. 2013; Miller and Roth 1994). Our goal is to create more structure with regard to the wide range of SSBIA application scenarios. Thus, the development of a taxonomy is suitable to improve our ability to differentiate among various SSBIA application scenarios. The design of our taxonomy is based on the methodology for the development of a taxonomy created by Nickerson etal. (2013), as this methodology provides a structured and scientifically sound process for the development of taxonomies. This methodology is an iterative process based on both existing theoretical foundations (conceptualization) and empirical evidence (empiricism). The dimensions thus obtained consist of mutually exclusive and collectively exhaustive characteristics. “Mutually exclusive” means that no object has two characteristics within one dimension, while “collectively exhaustive” means that each object has at least one characteristic in each dimension (Nickerson etal. 2013). Taken together, these two attributes of the taxonomy ensure that each object has exactly one single characteristic in each individual dimension. Starting with an analysis of the literature on SSBIA elements, the dimensions of the taxonomy are derived conceptually. Subsequently, related characteristics are identified by examining SSBIA tools empirically. After each iterative step, multiple ending conditions 164 J.Passlick et al. 1 3 are checked. If the ending conditions do not entirely apply, a further iterative step is necessary. The ending conditions applied in this process were taken from Nickerson etal. (2013) (see Appendix 8.1). We also conducted a case study. The development of a taxonomy is derived from artifact development in design science research (Hevner etal. 2004; Nickerson etal. 2013). In design science research, evaluation and/or demonstration is an essential component of the research process. A framework for the evaluation of taxonomies has been developed by Szopinski etal. (2019). According to this framework, there are different ways in which a taxonomy can be evaluated. We follow this framework and evaluate our taxonomy using an “illustrative scenario” in Sect.5 (Szopinski etal. 2019, p. 13). Our procedure is illustrated in Fig.2. 3.2 Conceptual‑to‑empirical taxonomy development In accordance with the suggestions of Nickerson etal. (2013), we base our metacharacteristic on the purpose of the taxonomy in line with our RQ. Therefore, we define our meta-characteristic as follows: the definition of SSBIA dimensions that can help differentiate among SSBIA application scenarios. We specify that the requirements of data science applications are considered to be SSBIA only if they can be realized in the context of analysis applications (Bani-Hani etal. 2019; Eckerson 2019). If the analyses are implemented completely in a programming language, e.g., in Python or R, we consider them to represent an IT implementation and thus no longer an SSBIA scenario. However, the partial use of programming language in analytical applications can constitute an SSBIA scenario, for example, if small snippets of code are used for the specific visualization of data. According to this definition, e.g., the work of a “citizen data scientist” belongs among SSBIA applications (Mullarkey etal. 2019). The first iteration employs the conceptual-empirical approach of the process model (Nickerson etal. 2013). Possible dimensions that do not match the metafeatures are discarded. Given the rapidly increasing number of potentially relevant scientific publications, not all of which add value to a literature review, it is important to identify the most relevant papers (vom Brocke et al. 2015). To Fig. 2 Performed steps in the process of taxonomy development 165 1 3 Self‑service business intelligence andanalytics application… identify the relevant literature, we followed the literature review guidelines suggested by Webster and Watson (2002) and vom Brocke etal. (2015). The purpose of this study is to uncover the differentiating dimensions and characteristics of SSBIA. Consequently, based on this framework, many areas of SSBIA could be considered, leading to our broad characteristic search string “Self-Service Business Intelligence” OR “Self-Service Analytics” OR “Self-Ser- vice Business Analytics”. Using this search string, the literature search engines and databases ScienceDirect, AiSeL, and Google Scholar were searched systematically, resulting in 46 relevant papers. We conducted a forward (3 papers), backward (4 papers), and a related article search (1 paper), to find additional literature based on the key papers previously identified. The key papers are Alpar and Schulz (2016), Bani-Hani etal. (2017, 2019), Burke etal. (2016), Eckerson (2009, 2012, 2014, 2019), Halper (2017), Imhoff and White (2011), and Lennerholt etal. (2018). These steps allowed us to complete our list, resulting in a total of 54 relevant papers. In addition, we searched the publication lists of the authors Bani- Hani and Eckerson for additional relevant articles. An overview of the overall and final results of these search methods is provided in the table in Appendix 8.2. Based on the SSBI architecture developed by Passlick etal. (2017), which indicates the relationships between new self-service elements and traditional BI components, we used the five themes of “Data Modeling”, “Data Presentation and Analysis”, “Users”, “Data Governance”, and “Architectural Elements” as our first criteria to sort and classify the literature identified. “Data Modeling” describes the tools, components, and techniques that are necessary to transform the data so that it can be analyzed in subsequent steps. The “Data Presentation and Analysis” topic focuses on these further analyses. Tools and techniques that present and visualize data are outlined. The “Users” topic specifies the user groups that can be found in an SSBIA environment. “Data governance” summarizes the guidelines governing, e.g., data quality or data protection. Under the theme of “Architecture Elements”, we summarize the components that support SSBIA from a technical or organizational perspective. To obtain a scientifically valid basis for classifying SSBIA applications, we identified the initial dimensions based on the literature review presented in Table 1. Drawing on the aforementioned SSBIA architecture developed by Passlick etal. (2017), we structured the dimensions of the taxonomy in line with the literature review, resulting in a preliminary taxonomy including a total of eight dimensions. A description of the dimensions follows in Sect.4, and definitions of the different dimensions can be found in Appendix 8.3. As shown in Appendix 8.1, several end conditions were not met due to the purely conceptualempirical approach previously employed. 3.3 Empirical‑to‑conceptual taxonomy development Subsequently, we employed an empirical-to-conceptual approach. For this purpose, we analyzed SSBIA tools. To identify possible tools, we used Gartner’s Magic Quadrant Report (2019), the “BI Products List” drawn from the website 166 J.Passlick et al. 1 3 Table 1 Occurrence of perspectives on SSBIA in the literature Data modeling Data presentation and analysis User Data governance Architecture elements Abelló etal. (2013) x x x x Alpar and Schulz (2016) x x x Bani-Hani etal. (2017) x x x Bani-Hani etal. (2018a) x x x Bani-Hani etal. (2018b) x x x Bani-Hani etal. (2019) x x Berthold etal. (2010) x x x x Böhringer etal (2009) x x x Burke etal. (2016) x x x Burnay etal. (2014) x Clarke etal. (2016) x Convertino and Echenique (2017) x Corral etal. (2015) x x Daradkeh (2019) x x De Mauro etal. (2018) x Eckerson (2009) x x x Eckerson (2011) x x x x Eckerson (2012) x x x Eckerson (2014) x Eckerson (2019) x x x Goeken etal. (2014) x x Halper (2017) x x x x Horvath etal. (2014) x x Howson (2015) x x x Imhoff and White (2011) x x x x Johannessen and Fuglseth (2016) x x x Johansson etal. (2015) x x Kobielus etal. (2009) x x Kosambia (2008) x Kretzer etal. (2015a) x Kretzer etal. (2015b) x x Lennerholt and van Laere (2019) x x x Lennerholt etal. (2018) x x x Li etal. (2017) x x Liu etal. (2012) x x x Mayer etal. (2014) x x Meyers (2014) x x x Michalczyk etal. (2020) x x x Morton etal. (2014) x x x x Naish (2013) x 173 1 3 Self‑service business intelligence andanalytics application… 4.2 Analysis oftheexamined data set To evaluate the taxonomy, all SSBIA tools examined were assigned to their respective characteristics. Figure 3 shows the frequency with which the characteristics were assigned to particular dimensions. In terms of the user roles dimension, it is apparent that the vast majority of SSBIA tools address both information consumers and information producers. The information collaborator type is not addressed by any tool as the sole characteristic. Eleven percent of the tools focus only on information producers. Basic + standard is addressed by more than half of the SSBIA tools with respect to the user skills dimension. Thirty percent of the tools additionally include the advanced skill. Few tools (13%) do not address the lowest level of skills (basic). In the dimension of BI analytics activities, approximately half of the tools are designed for report creation and data visualization. Thirty-eight percent of the tools offer additional advanced analytics capabilities. At least the integration of existing data sources is supported by all SSBIA tools in terms of the dimension requirements for data management. However, approximately 90% of the tools also support further activities. We also identified full support for data cleansing and enhancing in 47% of the tools. Only approximately 11% of the tools do not support any form of collaboration in development. The majority (72%) of the tools support the individualization of other people’s reports. A small percentage (together approx. 17%) of the tools also offer comment or rating functions. Most tools support access via mobile devices (51%). Approximately 21% even enable natural language chat, while 28% offer information access only via desktop. With regard to the final dimension investigated, i.e., the nature of the analysis, the ad hoc characteristic dominates, accounting for 53% of the tools, while 28% of the tools try to function as all-rounders. Significantly fewer (17%) tools address standard reporting, and 2% of the tools do not have any reporting function because they focus on data preparation. 4.3 Cluster analysis The assignment of the examined SSBIA tools to the characteristics of our taxonomy described previously was used as the foundation of our cluster analysis. By means of this cluster analysis, we identified typical SSBIA tools that are offered on the market. These typical forms are also known as archetypes. The analysis indicates that the developed taxonomy can differentiate the tools well. In addition, we can obtain insights into the SSBIA application scenarios that are observed by SSBIA tool vendors because they focus their tools on these application scenarios. To conduct the cluster analysis, we first applied the Ward (1963) algorithm to the collected data set. The Ward (1963) algorithm has the advantage of being a hierarchical partitioning algorithm. In contrast to the k-means algorithm, it is unnecessary to specify the number of clusters to be formed in advance. On the other hand, the clusters formed using k-means are often better. For this reason, a combination of hierarchical and nonhierarchical algorithms is recommended (Balijepally etal. 2011). To apply the Ward (1963) algorithm, we used the Sokal and Michener (1958) matching coefficient to calculate distances. After execution, the result can be 174 J.Passlick et al. 1 3 visualized using a dendrogram. This dendrogram is shown in Fig.4, which displays the SSBIA tools that we analyzed. All tools are connected by different branches. In this context, if a connection is long, it indicates great differences in the assigned characteristics. The height of the branching gives an impression of how many different groups are included in the data set as well as the strength of that inclusion. At a height of approximately 3, we can see three groups, which could be a suitable cluster number. These three groups are also marked by thicker boxes. However, four clusters would also be conceivable because this junction is at a similar height. The fourth branch is at a height of approximately 2.8, so we continue by using three and four as possible cluster amounts. Two groups would also be conceivable, but if three or four groups already lead to plausible results, these three or four groups are preferable, since the archetypes thus become more differentiated. The data set in this constellation is too small to accommodate more than four groups. Although several measures can calculate the optimal number of clusters, several studies have shown that these measures lead to such different results that a qualitative assessment is more appropriate for our study (Gimpel etal. 2018; Janssen etal. 2020). Accordingly, we analyzed the distribution of the characteristics when separated into three and four groups in further detail. If the study were to be divided into four groups, no plausible groups could be identified. No clear differences could be identified between two of the four groups. We thus concluded that a division into three groups leads to plausible results. The distribution in the case of a separation into three groups is shown in Table3. We have assigned each group a label that reflects its essential characteristics. Thereby we refer to the groups or archetypes as all-rounder with advanced analytics (A), simple ad hoc application scenarios (B), and tools used by information producers (C). In the all-rounder with advanced analytics archetype, all user skills are typically covered by the analyzed SSBIA tools. All analytics and data management activities are typically possible. Most tools support the individualization of other people’s reports. Frequently, mobile BI applications can be realized, but many tools of this archetype already include a natural language chat. All kinds of analyses are supported. Fig. 4 Clustering using the Ward (1963) algorithm as visualized by a dendrogram 175 1 3 Self‑service business intelligence andanalytics application… In the archetype simple ad hoc application scenarios, the basic + standard user skill is supported. In rare application scenarios, other skills are also supported. No advanced analytics functions are offered, and data management is limited to the integration of new data sources. The individualization of other people’s reports is supported as a form of collaboration, and analyses can usually be accessed via mobile devices. The tools associated with this archetype very often focus on ad hoc analyses. The user role information producer is the focus of the archetype tools used by information producers. Standard + advanced is often addressed as a user skill. The skill basic is therefore rarely included. Either no analytical activities or advanced analytics are enabled. Most such tools support data management with data cleansing and enhancing. Collaboration is frequently not supported. Access to all tools is possible only via desktop. Above all, standard reporting is addressed in this archetype. Table4 summarizes the archetypes thus found. 5 Discussion, implications, andrecommendations Based on our literature review, our analysis of SSBIA tools, and our case study, we developed a taxonomy that describes different application scenarios of SSBIA. This taxonomy offers a detailed answer to our RQ, which inquires into the dimensions and characteristics that distinguish SSBIA applications. The taxonomy features seven dimensions that are relevant to the task of differentiating SSBIA application scenarios and is evaluated in accordance with the suggestions of Szopinski etal. (2019) by reference to an illustrative scenario. Based on the dimensions thus uncovered, we expanded the initial differentiation that consists of the two dimensions “self-reliance” and “system support” (Alpar and Schulz 2016). For example, the developed taxonomy concretizes the dimension “self-reliance”. The dimensions user skill, BI analytics activities, and requirement for data management can be viewed as an elaboration of “self-reliance”, which has implications for both practice and research. Future research can now identify more clearly the particular application scenario of SSBIA in question when investigating aspects of SSBIA. Under certain circumstances, e.g., certain user skills or analytical activities may not be relevant to a research project. The focus of such research can now be differentiated more effectively. The question of whether certain characteristics have a stronger or weaker influence can also be described and analyzed more effectively. For example, experience with BI applications may be even more relevant if the requirements for data management are high, since many factors must be taken into account when performing complex data manipulations. Future research must take these differences into account to provide significantly better tailored SSBIA tools. Our research provides a foundation for a differentiated view of SSBIA. Our literature review in Sect.3.2 shows that there has been no increase in publications on SSBIA in recent years. This lack of research is astonishing, since SSBIA tools from several years ago are only partly comparable to contemporary tools. Practitioners can benefit from our taxonomy because it allows them to differentiate among SSBIA application scenarios more effectively. This assistance is 176 J.Passlick et al. 1 3 important for relevant discussions because it addresses issues that are critical to the successful deployment of SSBIA (Passlick etal. 2020). Additionally, to identify the application scenario processes that can be improved using SSBIA, SSBIA application scenarios must be described precisely. The choice of a suitable SSBIA tool is thus also simplified by the taxonomy. Since SSBIA tools have very different focuses, no single tool fits all SSBIA application scenarios (Eckerson 2019). In addition to this taxonomic knowledge, the analysis of this data set also has other implications. For instance, we can obtain an impression of the properties that are currently addressed by SSBIA software providers. For example, SSBIA tools Table 3 Distribution of characteristics among the archetypes Label All-rounder with advanced anal y tics Simple ad-hoc application scenarios Tools used by information p roducers n1 82 36 Group A B C User roles Consumer + Producer 94%100% Consumer + Producer + Collaborator 6% 17% Producer 83% User skills Basic + Standard 33%91% Basic + Standard + Advanced 67%4% 17% Standard + Advanced 4% 83% BI analytics activities None 50% Applying advanced analytics 78%4% 50% Report creation and data visualization 22%96% Requirements for data management Existing data sources 6% 13% New data sources17%78% Data cleansing and enhancing78%9% 100% Collaboration in development Comments 11%9% 33% Individualization of other people's reports83%78%17% No software supported collaboration9% 50% Ratings6% 4% Access type Desktop 6% 26%100% Mobile 56%61% Natural language chat 39%13% Nature of the analysis Ad-hoc 28%87% All-rounder67%17% No reporting17% Standard / scheduled 6% 13%67% Note: Due to rounding inaccuracies, the sum of a column in a dimension is not always exactly 100%. 177 1 3 Self‑service business intelligence andanalytics application… usually offer functions for both information consumers and producers. However, a small percentage (11%) of such tools pertain only to information producers who use the tools to process data with the aim of preparing it for a presentation or using it with other tools. More complex forms of collaboration, such as comments and ratings, have not yet become widespread (17%). Twenty-eight percent of SSBIA tools do not yet support mobile access to data, while 21% even support natural language chat. It is also remarkable that approximately half (53%) of SSBIA tools focus on ad hoc analyses. This finding indicates that many vendors mainly view SSBIA as offering tools for the creation of ad hoc analyses. The fact that ad hoc analyses play an important role in the analyzed SSBIA tools is also evident in the archetypes we found. In archetype B, i.e., tools for simple ad hoc application scenarios, the primary focus is ad hoc application scenarios, which are rather simple with regard to the analytics activities they involve. According to the differentiation proposed by Alpar and Schulz (2016), the nature of the analysis is not discussed in the levels of SSBIA. However, the high frequency of such characteristics indicates that SSBIA application scenarios must be differentiated based on a number of the dimensions that we provide in our taxonomy. The levels found by Alpar and Schulz Table 4 Identified SSBIA tool types Label A B C All-rounder tools also used for advanced analytics Tools used for simple ad hoc application scenarios Tools used by information producers (power users) User roles Consumer + Producer Consumer + Producer Producer User skills Basic + Standard + Advanced Basic + Standard Standard + Advanced BI analytics activities Application of advanced analytics Report creation and data visualization None + advanced analytics Requirements for data management Data cleansing and enhancement Integration of new data sources Data cleansing and enhancement Collaboration in development Individualization of other people’s reports Individualization of other people’s reports Primarily no software-supported collaboration Access type Primarily mobile, also natural language chat Mobile Desktop Nature of the analysis All-rounder Ad hoc Primary standard/scheduled Share in sample (47) 38% 49% 13% Example tool SAP Analytics Cloud GoodData Analytics Platform Paxata 178 J.Passlick et al. 1 3 (2016) are also evident in our taxonomy, but our archetypes indicate that the SSBIA application scenarios can also be differentiated in a rather different manner. Furthermore, certain distinctions among the three user types contained in the first dimension user role are necessary, namely, information consumers, information producers, and information workers, as well as among established roles in the context of general BI & analytics, such as the business user, the data scientist, or the data engineer (Eckerson 2011; Alpar and Schulz 2016; Michalczyk etal. 2021). SSBIA user roles relate exclusively to usage in a business department and offer a granular view of different SSBIA users. These users primarily engage in other main work tasks (Eckerson 2011; Alpar and Schulz 2016), whereas previously established roles in the context of general BI & analytics, e.g., the role of a data scientist, represent a dedicated job definition (Michalczyk etal. 2021). 6 Limitations anddirections forfuture research When investigating SSBIA tools, it must be kept in mind that they provide conclusions about the SSBIA forms that exist within organizations only indirectly. Aspects such as, for example, the sensitivity of the data indicate that not all such characteristics can be observed in the tools, but the literature as well as, in part, the case study indicate their existence. Namely, SSBIA application scenarios can only be deduced from the advertised functions of SSBIA tools indirectly. For example, in practice, SSBIA application scenarios in which only slight changes must be made to data or data models also emerge, but these scenarios are not mentioned by any software provider, as such small changes do not represent a functionality that must be advertised. Nevertheless, the analysis of SSBIA tools allows conclusions to be drawn regarding the use of SSBIA in companies, as software manufacturers respond to customer demand and adapt their communications accordingly. The differentiation of SSBIA users, data scientists, and citizen data scientists is not always strictly possible. This problem is also evident with regard to the definition of advanced algorithms. These algorithms can be implemented to a certain extent in the form of a self-service, e.g., a situation in which a citizen data scientist uses a k-means algorithm. However, there are also advanced algorithms that are so complex that they can likely no longer be considered a self-service. For example, the use of artificial neural networks or machine learning can be so complex in terms of their architecture, data management, interpretation, etc., that this approach cannot be considered a self-service. In such scenarios, advanced knowledge is required to construct the models as well as, most particularly, to interpret them correctly. Future research must provide a stronger distinction in this context. The fact that the characteristics of the SSBIA tools analyzed do not provide any quantitative information regarding the SSBIA application scenarios that are increasingly put into in practice in organizations must also be taken into account. We address this limitation by reference to our case study. However, the case study does not allow for the broad generalization made possible by the analysis of the tools. We conducted only a single-case study to obtain a different perspective on the analysis of the SSBIA tools. Additional companies could be examined in this context for comparison. 179 1 3 Self‑service business intelligence andanalytics application… Nevertheless, we can draw conclusions regarding practice from the combination of the findings drawn from the literature, the case study, and the analyzed tools. For example, SSBIA seems to be used frequently to conduct ad hoc analyses. Namely, many tools address this activity, and previous research has also identified flexibility and time savings as major advantages of SSBIA (Passlick etal. 2020). For ad hoc analyses, both high flexibility and fast execution are important characteristics. Findings from the analysis of the SSBIA tools only offer temporary insights. In future research, the analysis must be repeated to identify changes. The focus of SSBIA tool providers changes over time. In contrast, our taxonomy is more timeindependent since the dimensions we found are not purely based on the analyzed tools. Nevertheless, future research must determine whether additional characteristics might be added or whether certain elements of the taxonomy might become unnecessary. 7 Conclusions Our awareness and understanding of SSBIA have changed. Whereas only limited and simple SSBIA application scenarios were initially realized, the goal is now to implement almost all conceivable forms of analysis using SSBIA, even including applications for citizen data scientists. We identify the dimensions that must be considered when investigating and discussing SSBIA application scenarios. Our dimensions include users, their skills, analytical activities, necessary data management, intensity of collaboration, ways of accessing finished reports, and the different types of analysis. Furthermore, we present the different characteristics that pertain to each dimension. Based on the literature, an analysis of SSBIA tools, and a case study in a company, we developed our taxonomy. This approach allowed us to examine SSBIA application scenarios from different perspectives. The taxonomy thus developed is helpful for both research and practice, since a more sophisticated examination of SSBIA scenarios is now possible. Thus, the fact that the opportunities and challenges of SSBIA applications can be quite different depending on the scenario in question can be described and analyzed. In addition to this taxonomy, our cluster analysis also identified certain archetypes of SSBIA tools. All-round tools that are also suitable for advanced analyses, tools for simple ad hoc analyses, and tools intended for the use by the user group information producers in particular were found in the data set. Our archetypes confirm that the developers of SSBIA tools also address different SSBIA application scenarios. These archetypes indicate that when discussing SSBIA, it is necessary to identify the particular application scenario in question. 180 J.Passlick et al. 1 3 Appendix Summary offulfilled ending conditions periteration based onNickerson etal. (2013) Iteration Ending conditions 1. con.* 2. emp.* 3. emp.* 4. emp.* 5. emp.* • • • • Concise • • Robust • Comprehensive • • • • • Extendible • • • Explanatory • All objects or a representative sample of objects have been examined • • • • • No object was merged with a similar object or split into multiple objects in the last iteration • • • • At least one object is classified under every characteristics of every dimension • No new dimensions or characteristics were added in the last iteration • • No dimensions or characteristics were merged or split in the last iteration • • • • • Every dimension is unique and not repeated (i.e., there is no dimension duplication) • • • Every characteristic is unique within its dimension (i.e., there is no characteristic duplication within a dimension) • • • • Each cell (combination of characteristics) is unique and is not repeated (i.e., there is no cell duplication) *con. = conceptual; emp. = empirical. 181 1 3 Self‑service business intelligence andanalytics application… Literature search results Used database ScienceDirect AISelGoogle Scholar Search string “Self-Service Business Intelligence” OR “Self-Service Analytics” OR “Self-Service Business Analytics” Order/ Reviewed Relevance Relevance First 12 pages Relevance First 20 pages 1. Results58 5827 1700 Examination of relevance by reading the title, abstract, and deletion of duplicated papers 2. Results46 Forward search 3 Backward search 4 Related article search 1 Final results54 articles, books, and conference papers in total Definition ofthefound characteristics Characteristic Definition Criteria for the assignment of the software User roles This Dimension describes the division of SSBIA business user types into distinctive categories based on their specific work task (Eckerson 2011; Alpar et al. 2016). Information Consumer (casual) Casual BI Users who gather information to increase personal knowledge and make business decisions. Allowed to access data but don’t have time or the needed skills for analyzing Data in a higher structured manner. (Imhoff and White 2011; Eckerson 2014) The most basic user with very limited skills.Allocated to every software as long as the focus of the software is not on high complicated tasks such as advanced analysis or data preparation. Information Producer (power) Power BI Users who gather information to increase personal knowledge and help to make tactical and strategic business decisions, who have time and the necessary skills for analyzing data and creating their own solutions. (Imhoff and White 2011; Eckerson 2014) Each of the analyzed software tools address information producers. Therefore, the characteristic is assigned it to each software tool. However, the case study shows that there are also SSBIA application scenarios without information producers, namely when the IT provides an application in which information can be consumed. Information Collaborator They are specific subject matter experts and have the necessary skills to improve Data and Reports. They also rate existing Reports and give constructive criticism. (Imhoff and White 2011) Allocated to the software if it has a strong emphasis on BI development collaboration and the possibility to write Comments on Reports. User skills This Dimension describes the different technical skills and knowledge levels of business users. These skills include statistics, coding, data management, visualization and discovery and reporting technologies (Cosic et al. 2012). The more complex the SSBIA task and the accompanied SSBIA tool, the higher the required computer and analytical skills of business users need to be (Spahn et al. 2008; Eckerson 2014). 182 J.Passlick et al. 1 3 Characteristic Definition Criteria for the assignment of the software BasicUsers have low analytical, mathematical and IT skills and don’t take part in implementation, architectural focus, or design oriented tasks. Their capabilities include “established views of data, routine queries, and regularly produced reports” (Imhoff and White 2011). (Eckerson 2014) Allocated to the software if it has a very simple and manageable user interface and the software is mainly designed for simple applications such as drill down in reports. Standard Users have moderate mathematical and analytical skills, but low IT Skills (Eckerson 2014). “They are able to do ad hoc analysis as well as create and publish reports” (Imhoff and White 2011). Allocated to the software if it has a simple user interface and the software is designed for uncomplicated creation (e.g. drag and drop) or editing of dashboard, reports, etc. Advanced Users have high analytical and mathematical skills, as well as moderate IT skills. They can include structured and unstructured Data in their self-created statistical analytics and reports, as well as predictive modeling and Data Mining (Imhoff and White 2011; Eckerson 2014). Data Scientists may also be covered if they do not fully implement the analysis in a programming language (Bani-Hani et al. 2019; Eckerson 2019). Allocated to the software if it can be used for highly advanced analyses (e.g. k-means) and/or for complex data preparation/data processing. The analyses can be created or edited by coding. BI analytics activities (based on Alpar and Schulz 2016) - Hierarchical structure BI analytics activities describes how SSBIA users use the data to be analyzed (Cosic et al. 2012). The dimension has a hierarchical structure. This means that the next level also contains the previous one. The dimension has a hierarchical structure which means that the following characteristic contains all underlying or previous characteristics. None No BI analytic activities. Complete focus on data preparation can be a reason for it. Allocated to the software if the theoretically possible applications of the software matched the characteristics definition. Having access and using reports Analyzing data by using reports. Allocated to the software if the theoretically possible applications of the software matched the characteristics definition. Report creation and data visualization Creating new reports or accessing already existing reports, as well as visualizing and presentation of Data. Allocated to the software if the theoretically possible applications of the software matched the characteristics definition. Applying advanced analytics Analyzing Data using advanced algorithms such as k-means or similar. Allocated to the software if the theoretically possible applications of the software matched the characteristics definition. Requirements for data management -Hierarchical structure This dimension describes the different demands of the respective SSBIA application scenarios with regard to data management. 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