Business analytics and business value: A comparative case study

With growing adoption of business analytics, it is important for investing firms to understand how business value is created from investments. Studies in IT domain have highlighted how higher investment in technology may not bring more returns, rather how IT as an organizational capability acts as a key mediator in value creation. This research extends the model to business analytics, to identify elements of analytics technology assets and business analytics capability and to understand the mechanism of business value creation using multiple case studies. We capture how analytics resources contribute to business performance by developing operational and organizational performance measures.     

Business intelligence and analytics use,innovation ambidexterity,and firm performance: A dynamic capabilities perspective

To survive in a dynamic and hyper-competitive business environment, firms are compelled to simultaneously introduce incremental and radical innovations. While it is recognised that business intelligence and analytics (BI&A) can support innovation and provide organisational value, the literature provides a limited understanding of its impact on balancing different innovation activities and ensuring performance gains. In this study, we examine the relationship between BI&A use, innovation ambidexterity, and firm performance by relying on the process theory of IS value creation as well as the dynamic capabilities perspective. We test our model using data collected from medium- and large-sized firms in Slovenia, applying partial least squares modelling. The results support the notion that BI&A use is positively associated with successful balancing between explorative and exploitative innovation activities, which in turn enhances firm performance. Our results also indicate that innovation ambidexterity is enhanced in two ways: indirectly through interaction with the firm’s absorptive capacity, and directly by increasing the possibilities of faster experimentation with offerings of products or services and improved predictability of the value of new products or services.     

Business analytics maturity of firms: an examination of the relationships between managerial perception of IT,business analytics maturity and success

This study develops a measure for business analytics (BA) maturity and empirically examines the relationships between managerial perception of IT, BA maturity and BA success. The findings suggest that (1) BA maturity can be measured via BA integration & management support, process-level benefits of BA and technology & data analytics capabilities, (2) BA maturity positively affects organizations’ overall BA success, and (3) managerial perception of IT positively influence organizations’ achievement of BA maturity.     

Big Data Initiatives: Minimizing Risk and Maximizing Value with Governance

Abstract

Big Data Analytics can be a fantastic business opportunity for many organizations. Already organizations are using advanced analytics to streamline production processes, optimize back office activities, market more effectively, and better satisfy customer demand. That said, it goes without saying (as recent headlines can attest) that sometimes enhanced analytics capabilities can introduce risks such as erosion of privacy, overly-intrusive knowledge about customers, etc.

Given this dichotomy, making the decision about when, whether, how much, and how to invest in big data analytics initiatives can be a challenge. Invest too soon and you may obviate existing investments or disrupt business activities; invest too late and you may find that competitors gain advantages that make the market landscape asymmetric.

This article outlines how and why applying “tried and true” governance principles can help make this decision easier. For those that have formalized governance structures in place, how they might inform the decision an organization makes in this regard – and for those that don’t have a formalized governance program – how they might co-opt some of those principles to help make this decision more approachable.     

Process innovation as creative problem solving: An experimental study of textual descriptions and diagrams

The use of process models to support business analysts’ idea-generation tasks has been a long-standing topic of interest in process improvement. We examine how two types of representations of organizational processes – textual and diagrammatic – assist analysts in developing innovative solutions to process-redesign tasks. The results of our study clarify the types of process-redesign ideas generated by analysts who work with text versus those who work with models. We find that the volume and originality of process-redesign ideas do not differ significantly but that appropriateness of ideas varies. We discuss the implications of these findings for research and practice in process improvement.     

Business Analytics-Based Enterprise Information Systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.     

Shifting ML value creation mechanisms: A process model of ML value creation

Advancements in artificial intelligence (AI) technologies are rapidly changing the competitive landscape. In the search for an appropriate strategic response, firms are currently engaging in a large variety of AI projects. However, recent studies suggest that many companies are falling short in creating tangible business value through AI. As the current scientific body of knowledge lacks empirically-grounded research studies for explaining this phenomenon, we conducted an exploratory interview study focusing on 56 applications of machine learning (ML) in 29 different companies. Through an inductive qualitative analysis, we uncover three broad types and five subtypes of ML value creation mechanisms, identify necessary but not sufficient conditions for successfully leveraging them, and observe that organizations, in their efforts to create value, dynamically shift from one ML value creation mechanism to another by reconfiguring their ML applications (i.e., the shifting practice). We synthesize these findings into a process model of ML value creation, which illustrates how organizations engage in (resource) orchestration by shifting between ML value creation mechanisms as their capabilities evolve and business conditions change. Our model provides an alternative explanation for the current high failure rate of ML projects.     

Data analytics and firm performance: An empirical study in an online B2C platform

Data analytics has become an increasingly eye-catching practice in both the academic and the business communities. The importance of data analytics has also prompted growing literature to focus on the design of data analytics. However, the boundary conditions for data analytics in creating value have been largely overlooked in the literature. The objective of this article therefore is to examine the business value of data analytics usage and explore how such value differs in different market conditions. We rely on an online B2C platform as our empirical setting and obtain several important insights. First, both demand-side and supply-side data analytics usage has a positive effect on the performance of merchants. Second, when merchants’ product variety is high, the influence of usage toward demand-side data on performance is strengthened, whereas such impact is weakened for supply-side data analytics. Third, when competitive intensity is high, the performance implication of demand-side data analytics usage is strengthened, whereas such impact is not strengthened for supply-side data analytics. This study contributes by advancing the overall understanding of business value of data analytics.     

基于本体的数据挖掘技术应用于商务智能中的实际应用

首先阐述了挖掘技术与商务智能的含义,指出了数据挖掘技术应用在商务智能中的意义,结合新时期我国各大企业的发展实际,对基于本体的数据挖掘技术应用在商务智能中的实际情况进行了分析,旨在利用数据挖掘技术,发挥出企业商务智能系统的优势,提高企业核心竞争力,促进企业长远发展。     

Big Data Analytics Services for Enhancing Business Intelligence

This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.     

Analysis and improvement of business process models using spreadsheets

Software in general is thoroughly analyzed before it is released to its users. Business processes often are not – at least not as thoroughly as it could be – before they are released to their users, e.g., employees or software agents. This paper ascribes this practice to the lack of suitable instruments for business process analysts, who design the processes, and aims to provide them with the necessary instruments to allow them to also analyze their processes. We use the spreadsheet paradigm to represent business process analysis tasks, such as writing metrics and assertions, running performance analysis and verification tasks, and reporting on the outcomes, and implement a spreadsheet-based tool for business process analysis. The results of two independent user studies demonstrate the viability of the approach.     

Integrated value chains and their implications from a business and technology standpoint

Unlike previous decades where enterprises prized independence, the next decade will be one of business alliances and competing, end-to-end value chains. Enterprise value chains comprised of powerful business alliance partners will exceedingly compete as single entities for customers. Such extended corporations reach out not only with business relationships; they must integrate their business processes and information systems. In this paper, we review the business and technological requirements of modern extended organizations and explain how adaptive business objects and controlled interoperability are the key enabling technologies to the challenge of integrated value chains.     

A design-time data-centric maturity model for assessing resilience in multi-party business processes

Nowadays, every business organization operates in ecosystems and cooperation is mandatory. If, on the one hand, this increases the opportunities for the involved organizations, on the other hand, every business partner is a potential source of failures with impacts on the entire ecosystem. To avoid that these failures, which are local to one of the organizations, would block the whole cooperation, resilience is a feature that multi-party business processes currently support at run-time, to cope with unplanned situations caused by those failures.In this work, we consider awareness of resilience in multi-party business processes during design-time, by focusing on the role of available – as an alternative to unreliable – data as a resource for increasing resiliency, as data exchange usually drives the cooperation among the parties. In fact, a proper analysis of involved data allows the process designer to identify (possible) failures, their impact, and thus improve the process model at the outset. A maturity model for resilience awareness is proposed, based on a modeling notation extending OMG CMMN — Case Management Model and Notation, and it is organized in different resiliency levels, which allow designers (i) to model at an increasing degree of detail how data and milestones should be defined in order to have resilient by-design process models and (ii) to quantify the distance between a process model and the complete achievement of a resiliency level.     

Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

The Big Data era has descended on many communities, from governments and e-commerce to health organizations. Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper 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. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice.     

Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis

ABSTRACT

Business intelligence (BI) technologies have received much attention from both academics and practitioners, and the emerging field of business analytics (BA) is beginning to generate academic research. However, the impact of BI and the relative importance of BA on corporate performance management (CPM) have not yet been investigated. To address this gap, we modeled a CPM framework based on the Integrative model of IT business value and on information processing theory. Data were collected from a global survey of senior managers in 337 companies. Findings suggest that the more effective the BI implementation, the more effective the CPM-related planning and analytic practices. BI effectiveness is strongly related to BA, planning and to measurement. In contrast, BA effectiveness is strongly related to planning but less so to measurement. The study suggests that although both BI and BA contribute to corporate management practices, the information needs are different based on the level of uncertainty versus ambiguity characteristic of the management practice.     

A process-oriented methodology for evaluating the impact of IT: A proposal and an application in healthcare

In order to improve the performance of business processes often Information Technologies (ITs) are introduced. However, business processes are known to be complex and distributed among multiple business entities. As a result, the impact of new IT on an entire business process is typically hard to assess as quantitative methods for evaluation are missing. Therefore, in this paper, we propose a process-oriented methodology for evaluating the impact of IT on a business process ahead of its implementation. In our method, process mining and discrete event simulation are key ingredients. Based on automatically stored data, process mining allows for obtaining detailed knowledge on a business process, e.g., it can be discovered how a business process is actually executed. Using discrete event simulation, a model can be built which accurately mimicks the discovered process and which can subsequently be used for exploring and evaluating various redesign of the same process.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

Harnessing Artificial Intelligence to Deliver Real-Time Intelligence and Business Process Improvements

As demand for data scientists in audit/Governance, risk management and compliance (GRC), and industry in general, outpaces supply, data science in a box—packaged analytics powered by artificial intelligence (AI) and guided machine learning—can bridge the gap to bring analytics to every major enterprise. Packaged analytics harness the power of AI and machine learning technologies to help operations, finance executives, and GRC professionals do their jobs better; optimize business processes; and deliver actionable insights for better decision making. This article will explore real-world case studies of how companies have used packaged analytics to achieve process improvements, better oversight over financial spend, and significant return on investment. It is a guide to internal auditors and their GRC counterparts on what is available and suggests they can partner or use the products independently and significantly contribute to their companies.     

The business model portfolio as a strategic tool for value creation and business performance

The number of organisations that operate multiple business models continues to increase. However, operating multiple business models can be complex, as they often need to be harmonised within a broader portfolio due to their interdependencies. This complexity is exacerbated by the increasing role of digital technology and data – enabling new opportunities but also coming with related paradoxes. This paper examines the growing body of literature on business model portfolios revealing that they are evolving into a strategic tool for value creation and business performance. While there are concomitant value opportunities arising from complementarities and synergies, there are also paradoxes emerging from tensions that need to be considered. Employing a developmental literature review, we present a synthesis of recent empirical case studies to gain insight into the management of business model portfolios. Firstly, we identify different strategic intents: diversifying, sensing, and complementing. Secondly, we distil different themes for value opportunities and paradoxes and categorise them according to a business model framework. Thirdly, we identify and discuss the role of digital technologies and data for business model portfolios. Overall, we contribute to an emerging stream of studies on multiple business models in relation to strategy, innovation and technology. By adopting a holistic perspective on the management of business model portfolios, we explore strategic intent, value opportunities and paradoxes, and discuss how portfolios can play a role in strategic management and planning.