Introduction to the special issue of the Journal of Systems and Information Technology on Business Intelligence

Introduction to the special issue of the Journal of Systems and Information Technology on Business Intelligence

Article Type: Guest editorial From: Journal of Systems and Information Technology, Volume 17, Issue 3

With the rapid advancement of both business techniques and technologies in recent years, knowledge has become an important strategic asset that can determine the success or failure of an organisation. Indeed, research and practice have shown that rapid and effective conversion of data and information into working knowledge is a major contributor to competitive advantage. One very effective dimension to the need for creation, aggregation and sharing of knowledge is the pursuit of Business Intelligence (BI). Making sound business decisions based on accurate and current information and knowledge requires more than simple intuition, and BI has become indispensable to organisational success in the global economy. BI is currently one of the fastest growing areas of Information Technology (IT), along with developments in “green” computing, social networking, data visualisation, mobile BI, predictive analytics and big data. The aim of this special issue is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications.

As with other such concepts, the meaning and interpretation of BI have shifted over the years. At its simplest, BI refers to the ability to use information to gain a competitive edge. This includes data on education, skills and the past performance of employees to help companies identify the critical talent within the organisation and ensure its development and retention (#B11). To expand understanding of the concept, #B18 listed four key components or sub-systems of BI: Data Management, Advanced Analytics, Business Performance Management and Information Delivery. BI is often referred to as the techniques, technologies, systems, practices, methodologies and applications that analyse critical business data to help an organisation better understand its business and market and make timely business decisions (#B8). There are clear overlaps here with the concept of competitive intelligence, an activity emerging in the early 1990s and aimed at producing actionable intelligence and insights into the behaviour of competitors (#B24), uncovering the strategies behind their actions and, at the highest level, anticipating strategic and tactical moves.

It was not so long ago that the option of pursuing BI was one confined to only the largest organisations. However, with dramatic falls in both hardware and storage costs, and the availability of more sophisticated and cheaper software systems, the technology has moved into the mainstream. Furthermore, whereas previously data and information were tied up in different systems that were unable to talk to each other, systems such as finance, human resources or customer management, today these systems are connected and companies are able to use data to obtain a complete picture of their operations, or as it is popularly perceived “a single version of the truth”. This is made possible through a number of key technologies, including open source computing, data mining and the cloud. In 2012, surveys of Chief Information Officers were reporting a strong preference for spending on BI projects, something described at the time as a “mega trend” shifting the emphasis in investment towards collaboration and analytics, in what were information projects rather than process projects (#B3). Top BI technology priorities reported for 2013 included: dashboards, end user “self-service”, advanced visualisation and data warehousing. Smaller organisations were more likely to place a priority upon mobile BI and cloud, while large organisations favoured data warehousing and data mining (#B13).

Such advances in technology notwithstanding, there is little room for complacency about the organisational penetration of BI with, for example, perceived success rates for 2013 remaining largely the same as for a year earlier (#B13). Furthermore, while it is clear that technological advances have been a transformative force, they inevitably bring with them the risk of potential disruption and indeed, of building yet more data and application silos (#B4). Hence, it is still necessary to remember the fundamental importance of the human dimension when it comes to the acquisition and application of these technologies. Indeed, this point was made nearly a decade ago (#B28) when, in reference to the reported 50 per cent failure of all BI projects, executives were being urged to focus on the intangible (and abundantly human) reasons underlying such failures. These included not only the inability of organisations to effectively manage people, processes, and change, but also to understand how much and how well they were using the BI solutions already at their disposal. There is also the issue of behavioural change, which is perhaps the most difficult aspect of successful BI project implementation, and which requires a combination of incentives and rewards for people (in recognition of their achievements in using BI systems) and penalties to deter unhelpful behaviours and ensure a proper focus on using BI applications (#B28). In other words, the obstacles to a successful BI implementation are not solely technological in nature; in fact, they are often located within the organisation itself (#B6; #B13). More recently, this emphasis on the human dimension has extended not only to include training for staff in the proper use of BI applications, but also calls for the education of an entire new profession of big data scientists operating as multi-functional problem solvers communicating between different departments (#B19; #B21). This arguably has become ever more critical with the emergence of different versions of BI and the current phenomenon of big data (#B12).

It is clear, moreover, that these potentially game-changing advances, especially developments in predictive analytics, have the potential to do more than simply augment the current BI environment. It is also clear that such potential is appreciated in the business community at large. Hence, Forrester’s 2014 global data and analytics survey found that 31 per cent of the respondents already had predictive analytics platforms and applications in production (compared to 21 per cent in 2012), with another 24 per cent planning for such systems. The report also found that leading BI vendors were either building predictive capabilities organically or acquiring or integrating third-party predictive software. They were also integrating open source R, a free programming tool that allows companies to examine and present big data sets, and the free Hadoop software that enables ordinary personal computers to analyse huge quantities of data that previously required a supercomputer. Along with a welcome for the advent of integrated BI capabilities (predictive and text analytics as well as text analytics, natural language processing and geospatial features) came the gentle reminder that what vendors often claim for their products does not always eventuate when it comes to actual capabilities (#B3).

A potentially disruptive characteristic of such advances is the fact that they are placing new data discovery and advanced analytics tools into the hands of customers and doing so in such a seamless and transparent fashion that they are not even aware that they are interacting with a BI tool. These customers can range from other businesses (e.g. analysing their payroll data), to consumers (e.g. interacting with a bank or payment card statement) or employees (e.g. having BI content available from within a customer relationship management system). Increasingly, this entails a change in BI strategies to enable quick access to data beyond traditional on-premises systems such as Software as a Service (SaaS) and cloud-based BI. Significantly, access to these potentially low-cost and easy-to-deploy alternatives reflects a shift in control from IT departments to users at all levels of the enterprise. Equally important is the quality of data, which is ultimately determined not by its providers but by those who must use them for analytical and decision-making purposes (#B3).

It is clear that the focus of BI and related activities is not just of technological or organisational interest, but they also touch upon issues that are truly strategic in nature. This is emphasised in #B6 where, with reference to project failures and low adoption rates for information systems, they identified the absence of clear strategies along with inappropriate organisational structures and cultures. To succeed, such a strategy must reflect an effective alignment between enterprise objectives, business strategy, investments and BI to embody the right combination of people, processes and technology that enables stakeholders with better decision-making capabilities and helps the enterprise to achieve desired goals (#B23). Once these strategic decisions have been taken, managers can direct their attention to the particular configurations of technology that today make up a BI portfolio that has not only expanded to include a considerably enhanced analytics component, but also can be described in terms of different versions of BI and Analytics (#B8).

The papers in this special issue range over a variety of perspectives and applications of BI. The first of these, that by Harrison et al., while taking an avowedly technological approach, helps to set the scene nicely, particularly for readers new to the details of BI systems. They offer a high level view of the role played by technology in the exploitation of internal BI. Hence, the focus is largely on BI architectures and components based on current trends and operational issues associated with internal BI implementation and an assessment of the contribution made by BI to obtaining a competitive advantage for organisations. The authors make a good case for internal BI, describing it as a means of using advanced technological tools to assess and analyse data and information about an organisation to better understand its market position and to monitor its organisational competitiveness (#B27; #B30). They finish with a number of convincing conclusions starting with the argument that BI is not a “one-size-fits-all” approach but something that has to be tailored to the circumstances of individual organisations. Technology is the “backbone” of BI systems, allowing organisations access to high quality, targeted information by minimising the processes involved.

The second paper, by Arefin et al., is based on an empirical study of the impact of BI on organisational effectiveness. It is doubly interesting in that this study was conducted in an emerging country, namely Bangladesh. Although the authors have focused on the technological perspective of BI, this is moderated by a broadening of this perception to include recent developments in analytics such as big data. They acknowledge the relative dearth of research into the relationship between “softer” organisational factors and BI, which is a particularly serious matter in that the primary objective of BI is to support organisational decision-making. Here, organisational factors are viewed as non-IT resources and those complementing them (#B31) are investigated under the four categories of organisational strategy, structure, culture and process. In terms of their relationship to BI, the paper poses three research questions: what is the relationship between organisational factors and BI system effectiveness; what is the relationship between BI system and organisational effectiveness; and does a BI system moderate the relationship between organisational factors and organisational effectiveness. These basic questions are later teased out in the form of 13 hypotheses. A quantitative survey involving almost 600 managers in nearly 400 organisational units in Bangladesh was carried out. The research findings indicate that it is important to recognise the influence of organisational factors on the effectiveness of BI systems. Furthermore, the relationship between organisational factors and organisational effectiveness is partially mediated by the effectiveness of BI systems.

The third paper by Cahyadi and Prananto addresses the issue of applying design thinking to the creation of BI and Analytics dashboards, which enables the display of key organisational performance metrics in support of the core management tasks of managing, examining and controlling. Dashboards can be built into existing BI infrastructure (#B14) or emerge as a component of enterprise systems (#B22; #B25). Although the subject of dashboard design is well-covered in the literature (e.g. see #B10; #B34), much of the focus has been upon the graphical user interface and on functionality such as appearance and user characteristics (#B10; #B14). The authors argue persuasively that this somewhat simple approach to dashboard design risks overlooking the holistic and complex nature of the design process by underestimating such potential problems as data availability and reliability on the one hand and information overload on the other. What is needed they claim, is an approach that takes into account the wider organisational issues and that adopts the concept of design thinking. They used a case-based qualitative research method, and gathered data through semi-structured interviews with key design team personnel. The case study was conducted on the Finance department of a university in Melbourne, Australia, where the Tableau application was in use for the design of dashboards. Although based upon a single case study, this is a persuasive piece of work and the authors’ findings are arguably transferable to other entities responsible for the design of BI and Analytics dashboards.

The fourth paper, by Baur et al., takes the reader in a somewhat different direction. Its focus is on vendor pricing models for BI SaaS software, arguing that current practices are inequitable and that a greater balance needs to be found between vendor pricing models and customer benefits. The paper is grounded in a perceived shift in software delivery from physical installation on local hardware to access of the Internet (#B29; #B7). The authors argue that, although rendered more cost effective by developments such as the cloud, virtualisation and SaaS vendors have been slow not only to make their software offerings more affordable but also to change their value propositions to something that aligns more with customer benefits (#B15; #B5). Hence, the price of software must be more in keeping with value realisation by customers, with prices continuously adapted to the market and based on a deep knowledge of customers (#B2; #B20; #B26). Although there have been numerous analyses of pricing techniques (e.g. see #B17; #B16; #B9), little has been done specifically on the analysis of pricing techniques and their correlation with customer value realisation in relation to BI and Analytics. To this end, the authors argue that a customer-centric approach is badly needed. To analyse pricing models in the BI and Analytics SaaS industry, the authors adopted a two-phased method: an exploratory phase that included a literature review, interviews with industry experts and the design of a model, and a confirmatory phase that sought to validate the model through dialogue with representatives of five leading BI software vendors. The authors conclude that the end customer is essential to the overall success of software vendors’ strategies in BI and Analytics sales. In a market where each software pricing model has its own strengths and weaknesses, and where each customer derives value in a different way, the proposed model is helpful both in taking these facts into account and demonstrating the need for flexibility and scalability if software vendors’ offerings are to meet client expectations. Having a strategic and customer-centric perspective as provided in the proposed conceptual model on pricing practice can lead to lower customer churn, higher customer satisfaction and greater pricing flexibility.

The final paper, by Boyton et al., is in a way the most adventurous in that it tackles the difficult issue of suboptimal performance in BI projects. Pointing out that such reports are less well publicised than those detailing success stories, the authors identify the key factors associated with both failure and success in BI projects (where the key determinant is the delivery of real value to the organisation), before going on to recommend ways of addressing the problems identified. Significantly, the authors place great emphasis upon managerial rather than technical issues, with non-technical factors presenting the greatest challenge (#B33; #B1) and alignment of BI with strategic goals absolutely critical (#B32). They argue that, in addition to return on investment (ROI), each organisation must determine the measures that best define the value of their BI solution, such as user satisfaction, project management and other non-concrete measures. So far as the authors of this paper are concerned, any BI implementation with expressed major concerns in any of the foregoing categories is deemed to be sub-optimal. They conclude that, in designing or acquiring a BI solution, it must be the one that best fits the organisation’s strategic goals and operational needs; has a proven track record of delivering ROI for organisations with similar BI requirements; can be successfully implemented in the context of the other considerations raised in the paper; and requires an investment that is optimal against other available options.

To conclude, we would like to thank all of the authors for their high-quality contributions. We also wish to acknowledge the reviewers involved for their constructive and timely feedback. A further special note of thanks goes to the Editor-in-Chief, Craig Standing, for giving us the opportunity to guest-edit the special issue. Finally, we hope the readers will enjoy reading the papers in this special issue as much as we have enjoyed putting them together.

Xuemei Tian, Raymond Chiong, Bill Martin and Rosemary Stockdale

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