Towards a Classification Framework of Business Intelligence Value Research Sunet Eybers1, Jan H. Kroeze2, Ian Strydom3 1
School of Computing, University of South Africa (UNISA), Florida, Johannesburg, South Africa
[email protected] 2 School of Computing, University of South Africa (UNISA), Florida, Johannesburg, South Africa
[email protected] 3 School of Computing, University of South Africa (UNISA), Florida, Johannesburg, South Africa
[email protected]
Abstract. Business Intelligence and Analytics (BI&A) and the topic of ‘big data’ have sparked renewed interest in the discipline of Business Intelligence (BI) and the challenges associated with the implementation thereof. Although the benefits of the powerful analytical capability of BI&A are obvious, not all organisations have reached the maturity of dealing with big data. For some organisations, the focus remains on implementing ‘traditional’ BI capabilities based on structured data (also known as BI&A 1.0) such as data warehousing. For this reason, the value of such implementations remains a burning issue, especially in tough economic times. As a result, the authors embarked on a journey of investigating the current published academic material focusing on the value added by BI projects to organisations. Various focus areas have been identified and presented in a conceptual classification framework. This provisional framework makes an important academic contribution as it provides the reader with various approaches to identify, evaluate and justify the value of such projects either prior to, during or after projects; monitor the potential value in the course of the project; identify gaps in current research and identify future research opportunities. Keywords. Business intelligence, business value, success, maturity.
1
Introduction
The interest in Business Intelligence and BI related projects seemed to have increased substantially, both under practitioners and academic scholars. Despite challenging financial times, BI implementations remain one of the highest priorities for organisations [14]. Also the number of BI related papers published in peer reviewed journals has steadily increased over the past decade [23]. The latest trend towards the growing analytical capability required by huge datasets, labelled as ‘big data’, contributed to renewed interest in the application and capability of Business Intelligence and Analytics (also known as BI&A) [7]. However, whilst the benefits reaped by organisations implementing BI&A 2.0 and BI&A 3.0 are obvious, not all organisations are currently implementing this type of capability, and some are still stuck in BI&A 1.0. For this reason, the benefits of BI&A 1.0 cannot be discarded and stakeholders demand feedback in terms of the value of such implementations. Also, even when BI&A 2.0 and 3.0 are implemented, benefits are often lagging. For this reason, it is sometimes imperative to establish business benefits ex-ante (before), during or post-ante (after) implementations. It therefore seems surprising that a literature review by [23] found that research pertaining to the benefits offered by BI implementations seems to have been neglected when compared to other focus areas. However, it remains an important topic of interest for various reasons. Firstly, it is important to identify and understand BI competencies in decision environments contributing to successful BI implementations [22]. Secondly, the need has arisen to develop tools necessary for analysing, predicting and managing success in BI organisations due to huge capital investments [1]. Thirdly, it remains challenging to justify BI investments due to indirect, intangible benefits across organisations [15]. Fourthly, the research pertaining to BI value measurement seems insufficient [20], [34]. Fifthly, various authors recognize the need for the development of accurate and reliable measurement methods for measuring the business value of BI implementations [2], [28]. Lastly, there is a need to investigate the relationship between costs and benefits of BI solutions [11].
The objective of this paper is to consolidate current BI value research contributions and subsequently propose a provisional classification framework for BI value research. The primary research question can therefore be articulated as: What academic research has been conducted and published over the last decade focusing on the business value achieved in organisations as a result of a business intelligence implementation? The proposed framework will make numerous contributions. Firstly, the framework will present the reader with a summary of existing academic research focusing on the topic of BI value. Secondly, gaps in the current research are identified for future research opportunities. Lastly, the extent is exposed to which BI value research is investigated. Future research efforts can therefore build on the current structure.
2
Research Methodology
A literature review approach was used to investigate the extent to which BI value has been researched (this is similar to an approach followed by [5]). An extensive literature search was performed targeting various peer-reviewed academic resources including (a) the current Association for Information Systems (AIS) senior scholars’ basket of top IS journals available on the AIS website; as well as (b) proceedings from academic conferences on the topic of Information Systems (IS) (as part of the AIS accredited conferences). The senior scholars’ basket was selected due to the focus on the top IS journals in the field. This selection approach is similar to the approach followed by renowned authors such as [7] and [23]. Also, conference proceedings comprise the latest research information pertaining to the field of BI. Table 1. AIS senior scholars' basket of top IS journals (top 8) European Journal of Information Systems Information Systems Journal Information Systems Research Journal of the AIS Journal of Information Technology Journal of Management Information Systems (MIS) Journal of Strategic Information Systems MIS Quarterly
The following search criteria were used in the search process against all fields (including abstract, keywords and title): “business intelligence” AND “value”; “business intelligence” and “evaluation”; “business intelligence” and “success”; “business intelligence” AND “worth”; “business intelligence” AND “performance management”; “business intelligence” AND “impact”. All aspects of the BI concept was considered, including BI as technology (such as data warehousing, OLAP, decision support systems), product (data, information, knowledge, decisions) as well as process (extract, transform and load processes) [43]. No further demarcation of BI was considered, despite the BI evolution classification into BI&A 1.0 (data warehousing), BI&A 2.0 (analytical web applications) and BI&A 3.0 (mobile technology) of Chen et al. [7]. The search was further restricted to include results from the past decade, from 2000 to 2013. Although BI is perceived as a young discipline (less than 25 years old) [32] the search was not expanded to include the entire period. The main reason for the decision is the fact that BI, prior to 2000, was often referred to using encapsulating terminology such as decision support systems (DSS) and management information systems (MIS) [3], [16]. Although introduced in 1989 by Howard Dressner [52] BI, as a discipline, did not receive sufficient academic focus prior to the turn of the century. This is evident when a general search is conducted for the keyword “business intelligence” when using any renowned academic search engine. For example, the PROQUEST (ABI/INFORM Global) search engine was used to perform a complete search (with no date restrictions). The search returned 41 records for the period 1990 to 1999; 522 records for the period 2000 to 2009 and a total of 491 records for 2010 to 2013 (search conducted on 1 September 2013). The results set returned using a restriction for the
period 1990 to 1999 was therefore excluded because of the majority of the articles published in the latter periods. Even if the period prior to 1990 was included, it would probably not have impacted much on the literature results set. The initial literature search resulted in a temporary set of 449 journal articles and 1524 conference papers and proceedings. The articles were further evaluated for relevancy using the article abstract. The preliminary literature pool was compiled. A backward literature search was conducted to identify additional possible resources after which the final literature pool was finalized. A backward literature search entails the evaluation of the reference list contained in the articles returned as part of the literature pool to identify possible related articles on the topic. A list of the final literature pool utilized for the construction of the framework is displayed in Table 2.
Fig. 1. Literature review process
A meta-synthesis approach was used to integrate, evaluate and interpret the content of the final qualitative literature pool. This approach was used to identify the main unit of investigation within the studies and identify common key elements as well as the academic contribution made. For example, studies investigating success factors of BI implementations using critical success factors were classified accordingly.
3
BI Value Research: Proposed Framework
As a result of numerous researches investigating the value of IT related implementations many models, frameworks and taxonomies that prove the wealth of implementations have seen the light. Some of these results have been empirically verified through extensive testing, whilst some remain conceptual based on theoretically founded theories without extensive testing. However, all these studies strive towards achieving the same objective – to prove the business value to organisations as a result of the investment. A similar scenario is evident in the BI research area. BI value literature can be broadly classified into two categories. The first category reused existing IT value models, frameworks and taxonomies applied to a BI environment [38]. The second category developed bespoke models specifically tailored for the BI environment [54], [56]. Both categories were considered. The first section in the proposed framework classifies research focused on pre-conditions for maximum business value. These studies focus on organisational or process maturity, organisational or process readiness and success models. In instances where these conditions exist BI business benefits are expected. The second section focuses on organisational level investigations as well as process level investigations and subsequent interrelationships. It is postulated that the value realized on process level will have a direct impact on the organisational level as well as the interrelationship between process and organisational level capabilities.
Critical success factors as well as project success failure is classified as the last category in the framework. These critical success factors should be tracked in order to realize business value. Also, project success studies explore the reason for project success or failure and subsequent benefits. This section might also contain organisational or process level investigations. The focus areas briefly discussed above are depicted in Fig. 2 and form the basis of the proposed framework. Each focus area is discussed in more detail.
Fig. 2. A framework of BI value research
3.1
Preconditions for Realized Value
Numerous studies focus on the pre-conditions required to realize BI value as a result of implementations. Some studies focus on organisational and/or process level maturity, the readiness of an organisation and/or processes for a BI implementation or contributing factors of project success. These studies postulate that for an organisation to maximize benefits of BI implementations, a particular maturity level, readiness level or conditions for success should be present. Similar to the main challenge when defining BI, studies investigating the maturity and/or readiness for BI implementations focus on individual aspects of BI as a discipline, namely technology [24], [53], process or product [11] of which technology (data warehousing) is more prevalent. Although valuable to organisations a complete maturity or readiness model should focus on all aspects of BI. BI Maturity Models Maturity models assist organisations in identifying the maturity of the organisation on either process or organisational level. The approach proposes an analysis to identify the current maturity level of the organisation. The current level is then compared to a future desirable maturity level. Subsequent activities are identified to proceed to the next level within the maturity model to achieve the corresponding benefits of the succeeding level [11]. The objective of a maturity model is twofold. Firstly, the focus is on the improvement of corporate data management [19]. Secondly, gaps in the current implementation can be identified when benchmarked against the next desirable level in the maturity level. A higher level in the maturity model implies more benefits [12], [53]. A number of maturity models were identified in the course of the literature review process. Whilst some models focused on the maturity of BI [11-12], [19], [42], [27], [36], others focused on data warehouse maturity [53]. There are many advantages of using BI maturity models in organisations. These models prescribe a structured approach to introducing or enhancing the current or new BI capability within organisations, contributing to the realization of maximum business value. Also, the existence of certain characteristics (pertaining to a particular maturity level), can contribute to the predictability of the success of a BI implementation.
Dinter [11], on the contrary, identified a number of disadvantages of maturity models. The vast number of maturity models available might pose a challenge to users in the selection of the appropriate model. Also, the research development methods of these models are not always disclosed, questioning the validity and reliability of the instrument. As a result, these models are not always empirically tested. Maturity models focus on sub-sections of BI such as data warehousing or data quality, similar to perspectives of the proposed BI framework in this paper (namely readiness assessments). The applicability to BI as holistic implementation might be questionable. The various lower levels of maturity models are not always disclosed, making it challenging when implementing these models. Finally, maturity models include a subjective component containing an element of individual preference. BI Readiness Assessments Readiness assessments investigate the organisations’ inclination for achieving success prior to a BI implementation. It is a business centric assessment and includes investigations into certain organisational, process and technical level characteristics to establish readiness [54]. The main objective of a readiness assessment is to minimize risk when implementing a BI solution. One such example is the assessment of the organisation’s ability to provide BI systems with data. The lack of data will increase the risk of project failure. Advantages of using readiness assessments focus on the period prior to BI implementations. If the susceptibility of the organisation towards the BI implementation can be established, corrective action can be implemented, therefore mitigating the risk of project failure. In addition, if the ideal incubation period is established prior to the BI implementation, the implementation should be uneventful. Unfortunately, these readiness assessments are often created focusing on the subcomponents of BI such as data warehouses [54], [24]. BI Success Models In the context of BI, Shollo and Kautz (2010) describes BI success as the positive benefits obtained as a result of the implementation [44]. The organisation’s and subsequent stakeholders’ definitions of success depend on the anticipated benefits. The application of success models in the context of BI varies substantially, depending on the objective of the method used. Some success models aim to understand the elements affecting or contributing to successful implementations including critical success factors (CSF) [1], [20], [37], [33], [31]. On the other hand, some studies focus on understanding, assessing and scrutinizing the success of BI implementations either by using instruments [38] or BI success models [26], [56]. Some studies include the influence of contextual factors on the success of BI implementations [13] such as BI capabilities [22]. Data warehouse success has also been a topic of investigation by authors such as [55], [43], [21]. The advantage of using success models is obvious. The early identification of characteristics necessary for the successful BI implementation can minimize project failure risk. Unfortunately, similar to maturity models, success models might be subjective, containing an element of individual preference. Also, some of the models proposed focus on sub-components of BI such as data warehousing [26], [43].
3.2
Value Investigations at the Organisational Level of Analysis
Studies focusing on the organisational level of analysis assess the organisational impact of IT on organisational performance [17]. The bigger the positive impact the more business value is realized. In the context of BI, [6] supported the same view, focusing on identifying both individual and organisational competencies necessary to realize benefits of BI implementations. These competencies should be embedded in Business Intelligence Systems (BIS) in order to maximize business value [18]. In an attempt to ensure the realisation of BI benefits, [30] proposes a six sigma approach as part of the BI project management process. The six sigma approach is a methodology focusing on the quality of outputs. The approach is typically applied by organisations striving towards a zero defect deviation of products. In the application proposed by [30], the author propose certain objectives (or so-called “Critical to Quality” (CTQ) goals) to be achieved, for example quality of data. These are similar to organisational wide critical success factors (CSF). If these factors are positive, project benefits will be realized.
The stakeholder theory is applied by [47] to their BI value investigation. The focus of the study was on the so-called newform organisation with its unique characteristics. The approach includes a unique value creation objective identified across all organisational boundaries. These objectives can be monitored and assessed in performance management investigations. This approach has several advantages. Firstly, it might be easier for the evaluator to relate the benefits or value identified on this level to the overall organisational goals. Secondly, a broader view of benefits might be necessary to identify the impact on organisational decision making and competitive advantage.
3.3
Value Investigations at the Process Level of Analysis
Various studies pertaining to IT value investigations focus on the value of investments generated on process level, also known as the process approach [4], [46], [49]. Process theory, in general, investigates ‘how’ the value occurs by means of inputs resulting in desirable outcomes and the interrelationships between these constructs [49]. This approach is also used by [57] to investigate the value of BI to organisations. Subsequently in the context of BI, [48] argues that an investigation on business process level is necessary to understand the business value created on organisational level. The study uses a customized model based on the process model by [49] as well as [28]. The finding is that business benefits are realized on various activities across all the processes but that they are challenging to measure due to the indirect and delayed onset of benefits. On the contrary, the impact of BI implementations is usually more visible on process level. These individual process level benefits contribute directly to the overall organisational level performance. Unfortunately, this approach requires that the evaluator have a thorough understanding of the various organisational processes. 3.4
Value at the Organisational and Process Level of Analysis and Subsequent Interrelationships
Whilst some authors argue that an investigation on business process level is necessary to understand the IT business value realized on organizational level [8], [29] other authors focus on understanding the relationship between the two constructs [13]. The focus of these studies is the mutual affiliation rather than the business value outcome. There seems to be a (positive) correlation between business process performance and organizational performance. However, the strength of the correlation varies between various industries [13]. Therefore, context should be considered when designing performance management measurement systems for the purpose of value realization. In a study conducted by [2] the Content, Context, and Process (CCP) framework [50] is utilized to analyse the effectiveness of an existing evaluation process. The study found that traditional financial measures are too narrow and that the evaluation technique should include content, process and context. This approach allows for a holistic approach to the identification of the value of BI implementations both on strategic and operational level. Unfortunately, this approach can be complex to implement. A summary of the BI value models, frameworks, tools and techniques used as basis for the framework is given in Table 2 followed by a summary of the advantages and disadvantages of each of the focus area (in Table 3). Table 2. Summary of BI value models, frameworks, tools and techniques
Classification
Contribution
Author
BI Maturity Model (biMM)
[11]
Americas SAP User Group (ASUG) BI maturity model
[19]
Data warehousing stages of growth
[53]
Preconditions for value or success: Maturity models (BIMM)
Classification
Readiness assessments Success models
Process level
Organisational level
Both process and organisational level and interrelationships
Contribution
Author
BI Maturity Model
[12]
Service-Oriented Business Intelligence Maturity Model (SOBIMM)
[42]
Theoretical BI maturity model
[27]
Data warehouse process maturity
[41]
Business Intelligence Maturity Model (BI MM)
[36]
Method for BI readiness assessment
[54]
Success factors indicating readiness
[24]
Instrument for understanding, evaluating and analysing success of BI solutions
[38]
Data warehouse success model
[54]
Critical success factors
[20]
Critical success factors
[37]
Critical success factors
[33]
Critical success factors framework
[31]
BI success model
[26]
Capability assessment framework
[22]
Framework for CSF
[1]
Model for BI success
[56]
System success factors in DW
[43]
Structural model of DW success
[21]
DeLone and McLean’s success model tested in BI environment
[51]
Business value process model
[48]
Process oriented research approach for investigating value
[57]
Process and variance model
[40]
Six sigma approach
[30]
Model of organizational competencies for BI success
[6]
Stakeholder model of BI
[47]
Success in BI-based organisations
[34]
Success factors in BI systems
[18]
Assessment framework
[45]
Theory of content, context and process (CCP) for BI (based on [48])
[2]
BI value measure instrument
[13]
Table 3. Advantages and disadvantages of BI framework focus areas
Classification
Advantage(s)
Disadvantage(s)
Structured approach to introducing or enhancing the current or new BI capability can contribute to the creation of maximum business value.
There are many maturity models available and it might be challenging selecting the appropriate model.
Preconditions for value or success: Maturity models (BIMM)
Classification
Advantage(s)
Disadvantage(s)
The existence of certain characteristics (indicate a particular maturity level) can contribute to the predictability of the success of the BI implementation.
Some maturity models focus on sub-sections of BI such as data warehousing or data quality [11]. The research development method of some of the models is not always disclosed [11]. The various lower levels of maturity models are not always disclosed making it challenging to implement [11]. Not all maturity models proposed are empirically tested [11]. Maturity models include a subjective component containing an element of individual preference [11].
Readiness assessments
The susceptibility of organisations towards the implementation of BI can minimize risk for failure.
Often focus on sub-components of BI such as data warehousing [54].
If the correct incubation environment exists for BI implementations, the contributed value should be more [54]. Success models
Early identification of characteristics necessary for successful implementation of BI can minimize project failure risk.
Similar to maturity models, success models might be subjective, containing an element of individual preference. Some success models focus on subcomponents of BI such as data warehousing [26], [43].
Process level
Organisational level
The impact of BI implementations is usually more visible on process level.
Business process improvement interventions should commence prior to a BI implementation to ensure maximum value.
The value experienced as a result of BI implementations on process level contributes directly to the overall organization level.
Individual processes should be well understood and documented for this approach to be successful.
It might be easier to relate the benefits or value on this level to the overall strategic goals. A broader view of the benefits of BI implementations might be necessary to identify the impact on organisational decision making and competitive advantage.
Classification
Advantage(s)
Disadvantage(s)
Both process and organisational level and interrelationships
This approach allows for a holistic approach to the identification of the value of BI implementations both on strategic and operational level
This approach can be complex.
4
Challenges and Limitations
The term BI is an encapsulating term, referring to many aspects of BI products, processes and technologies. This study focuses on all these aspects, contributing to an extremely wide scope. Perhaps the scope should be contained by focusing on only one aspect of BI, for example data warehousing. On the other hand, various studies focus on a particular aspect of BI (for example processes) without considering all aspects. The question remains if the findings of research studies with such a narrow scope are generalizable to the entire scope of BI. The objective of the study was to present a provisional framework of current research conducted. The result was a first draft of a broad classification method based on current academically published articles. Although it might appear as if the first version of this framework is merely a list of bibliographies, it serves as a starting point for a proper classification framework. The suggested framework is a work-in-progress, and the various perspectives identified as part of this framework would have to be critically compared in future work in order to increase the usability and value of the proposed framework. This, together with the additional dimensionality of the various versions of BI&A (1.0 to 3.0) might be used to compare the various approaches used according to the focus areas identified. All these will be considered during the next phase of the ongoing study. The number of BI academic published material is on the increase and the latest material would have to be considered to get a true indication of the status of BI value research. The current literature review scope should be enlarged to perhaps include other BI&A specific resources as identified in a paper by [7].
5
Conclusion
The objective of the paper was to investigate the extent to which BI value has been researched in order to present a summarized consolidated view in a framework. Although similar work has been done by [39] focusing on IT value research, little evidence could be found of a similar framework focusing on BI. Furthermore, the framework classified, categorized and synthesized BI value academic literature for the last decade. This was similar to an approach used by [5] on the topic of telework. The framework presented identifies the extent to which BI value has been researched as well as gaps with a view to future research. Firstly, there seems to be a need to investigate mobile BI (also known as BI&A 3.0) as well as BI applicable to social media which is currently in its infancy. Secondly, the investigation highlights the lack of published academic material focusing on the evaluation of the organisational level of analysis, followed by the process level of analysis and the interrelationships between these two levels. It seems as if the focus is on investigations of pre-conditions of success, such as maturity models, readiness assessments and success models. A possible explanation for the lack of organisational level focus might be attributed to the assessment of the net value of both tangible and intangible benefits of BI implementations without considering lower level (process level) characteristics. The research focus areas identified in the framework are in line with the level of the measurement research field as described by [39] in his taxonomy of IS business value research. This refers to both the organisational or process level unit of analysis. The importance of considering the level of analysis is stressed by [10] postulating that the distinction between the levels contributes to the explanation of the productivity paradox. The separation of the different levels is useful to structure research and to resolve conflicting results. This is similar to the approach used by Sidahmed [45]. It is also argued that, beyond the
distinction between the levels, the interrelationship between the levels can provide useful insights into how Information Systems (IS) generates value [9], [25]. The framework contributes to the existing pool of academic literature as it provides the reader with a high level classification framework for current BI value literature; identifies gaps in the current research (as discussed above); identifies future research areas; and contains a bibliography of academically published research according to the identified focus areas.
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