Showing papers on "Business analytics published in 2011"

Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance?

Abstract: We examine whether firms that emphasize decision making based on data and business analytics (“data driven decision making” or DDD) show higher performance. Using detailed survey data on the business practices and information technology investments of 179 large publicly traded firms, we find that firms that adopt DDD have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage. Furthermore, the relationship between DDD and performance also appears in other performance measures such as asset utilization, return on equity and market value. Using instrumental variables methods, we find evidence that the effect of DDD on the productivity do not appear to be due to reverse causality. Our results provide some of the first large scale data on the direct connection between data-driven decision making and firm performance.

The Role of Organizational Absorptive Capacity in Strategic Use of Business Intelligence to Support Integrated Management Control Systems

Abstract: This study examines the influence of organizational controls related to knowledge management and resource development on assimilation (i.e., strategic integration and use) of business intelligence (BI) systems. BI systems use analytics and performance management concepts to leverage enterprise system databases and provide core management control system (MCS) capability. Our results indicate that organizational absorptive capacity (i.e., the ability to gather, absorb, and strategically leverage new external information) is critical to establishing appropriate technology infrastructure and to assimilating BI systems for organizational benefit. Further, findings show that while top management plays a significant role in effective deployment of BI systems, their impact is indirect and a function of operational managers’ absorptive capacity. In particular, this indirect effect suggests that leveraging BI systems is driven from the bottom up as opposed to the top down. This differentiates BI from othe...

Analyzing the business model concept — a comprehensive classification of literature

Abstract: The business model concept is characterized by numerous fields of application which are promising in business practice. Consequently, research on business models has attracted increasing attention in the scientific world. However, for a successful utilization, the widely-criticized lack of theoretical consensus in this field of research has to be overcome. Thus, this paper conducted a comprehensive and up-to-date literature analysis examining 30 relevant literature sources focusing mainly on business model research. To achieve this, the analysis was based on a classification framework containing 17 evaluation criteria. Hereby, a systematic and objective penetration of the research area could be achieved. Moreover, existing research gaps as well as the most important fields to be addressed in future research could be revealed.

The value of learning analytics to networked learning on a personal learning environment

Abstract: Some might argue that the analytics tools at our disposal are currently mainly used for boring purposes, such as improving processes and making money. In this paper we will try to define learning analytics and their purpose for learning and education. We will ponder on the best possible fit of particular types of research methods and their analysis. Methodological concerns related to the analysis of Big Data collected on online networks as well as ethical and privacy concerns will also be highlighted and a case study of the use of learning analytics in a Massive Open Online Course explored.

Solving problems with visual analytics

Abstract: Visual analytics is an emerging research discipline aiming at making the best possible use of huge information loads in a wide variety of applications by appropriately combining the strengths of intelligent automatic data analysis with the visual perception and analysis capabilities of the human user. The major goal of visual analytics is the integration of these disciplines into visual analytics to acquire well-established and agreed upon concepts and theories, combining scientific breakthroughs in a single discipline to have a potential impact on visual analytics and vice versa. In a session at FET’11, the leaders of the thematic working groups of the recently finalised FET Open coordination action VisMaster CA presented the scientific challenges that were identified in the visual analytics research roadmap, and the connection between the various disciplines and the broader vision of visual analytics. This article contains excerpts from this research roadmap to motivate further research in this direction within FET.

Agile Analytics: A Value-Driven Approach to Business Intelligence and Data Warehousing

Abstract: Using Agile methods, you can bring far greater innovation, value, and quality to any data warehousing (DW), business intelligence (BI), or analytics project. However, conventional Agile methods must be carefully adapted to address the unique characteristics of DW/BI projects. In Agile Analytics, Agile pioneer Ken Collier shows how to do just that. Collier introduces platform-agnostic Agile solutions for integrating infrastructures consisting of diverse operational, legacy, and specialty systems that mix commercial and custom code. Using working examples, he shows how to manage analytics development teams with widely diverse skill sets and how to support enormous and fast-growing data volumes. Colliers techniques offer optimal value whether your projects involve back-end data management, front-end business analysis, or both. Part I focuses on Agile project management techniques and delivery team coordination, introducing core practices that shape the way your Agile DW/BI project community can collaborate toward success Part II presents technical methods for enabling continuous delivery of business value at production-quality levels, including evolving superior designs; test-driven DW development; version control; and project automation Collier brings together proven solutions you can apply right nowwhether youre an IT decision-maker, data warehouse professional, database administrator, business intelligence specialist, or database developer. With his help, you can mitigate project risk, improve business alignment, achieve better resultsand have fun along the way.

Talent and analytics: new approaches, higher ROI

Abstract: Purpose – More and more, the leaders of business functions are turning for competitive insights to the massive data they can now capture. But to date, human resources departments have lagged behind the efforts of marketing, IT, CRM and other functions. The purpose of this article is to show how executives can start using data to measure and improve HR's contributions to business performance.Design/methodology/approach – The article identifies six analytical tools that HR can use to connect HR efforts to business performance. Survey results underscore the value of an analytical approach while revealing that many HR departments are heavily focused on internal measures rather than business outcomes. Each analytical tool is exemplified through case studies. A model is presented to suggest how executives can get started by focusing on five key areas.Findings – Leading companies are using six analytical tools to improve the connection between HR investments and business returns: employee databases; segmentation...

Business Analytics in Supply Chains - The Contingent Effect of Business Process Maturity

Abstract: The paper analyzes the effect of the use of business analytics on supply chain performance. It investigates the changing information processing needs at different supply chain process maturity levels. The effects of analytics in each Supply Chain Operations Reference areas (Plan, Source, Make and Deliver) are analyzed with various statistical techniques. A worldwide sample of 788 companies from different industries is used. The results indicate the changing impact of business analytics use on performance, meaning that companies on different maturity levels should focus on different areas. The theoretical and practical implications of these findings are thoroughly discussed.

Integrating business analytics into strategic planning for better performance

Abstract: Purpose – Over the past few years, developments in business analytics have provided strategic planners with promising instruments for dealing with turbulent environments This study aims to reveal whether or not the application of business analytics in strategic planning contributes to better company performance, and to formulate recommendations on how to integrate business analytics in companies' performance management systemsDesign/methodology/approach – Based on a survey conducted with 89 respondents from high‐technology firms, a group comparison between firms with strong performance and those with weak performance reveals significant differences between the two groups' strategic planning processes and application of business analyticsFindings – The empirical survey's results show that better‐performing companies are characterized by a more sophisticated analytical planning process Lower‐performing firms acknowledge this competitive advantage Based on these findings, the authors develop recommendat

On Testing Business Models

Abstract: This study explores decisions related to formal empirical tests of business models and interpretations and uses of those tests. Business models describe managers' rationales as to how their organizations will achieve success. This study documents a test of one company's business model under seemingly favorable conditions for such a test—a successful single-product firm following a consistent strategy over a long period of time with stable management and publicly traded stock. Although the findings provide only weak support for the hypothesized business model, the confidence of the company's top managers in their business model remained high. Further analyses reveal that the managers' response to the test results is consistent with that expected of Bayesian-rational agents. Our analyses provide the basis for development of a framework for understanding the expected value of testing business models in various circumstances. This framework might explain apparent contradictions between previous studi...

Key Success Factors in Business Intelligence

Abstract: Business Intelligence can bring critical capabilities to an organization, but the implementation of such capabilities is often plagued with problems. Why is it that certain projects fail, while others succeed? The aim of this article is to identify the factors that are present in successful Business Intelligence projects and to organize them into a framework of critical success factors. A survey was conducted during the spring of 2011 to collect primary data on Business Intelligence projects. Findings confirm that Business Intelligence projects are wrestling with both technological and non-technological problems, but the non-technological problems are found to be harder to solve as well as more time consuming than their counterparts. The study also shows that critical success factors for Business Intelligence projects are different from success factors for Information Systems projects in general. Business Intelligences projects have critical success factors that are unique to the subject matter. Major differences can be found primarily among non-technological factors, such as the presence of a specific business need and a clear vision to guide the project. Success depends on types of project funding, the business value provided by each iteration in the project and the alignment of the project to a strategic vision for Business Intelligence at large. Furthermore, the study provides a framework for critical success factors that, explains sixty-one percent of variability of success for projects. Areas which should be given special attention include making sure that the Business Intelligence solution is built with the end users in mind, that the Business Intelligence solution is closely tied to the company’s strategic vision and that the project is properly scoped and prioritized to concentrate on the best opportunities first.

Data is dead... without what-if models

Abstract: Current database technology has raised the art of scalable descriptive analytics to a very high level. Unfortunately, what enterprises really need is prescriptive analytics to identify optimal business, policy, investment, and engineering decisions in the face of uncertainty. Such analytics, in turn, rest on deep predictive analytics that go beyond mere statistical forecasting and are imbued with an understanding of the fundamental mechanisms that govern a system's behavior, allowing what-if analyses. The database community needs to put what-if models and data on equal footing, developing systems that use both data and models to make sense of rich, real-world complexity and to support real-world decision-making. This model-and-data orientation requires significant extensions of many database technologies, such as data integration, query optimization and processing, and collaborative analytics. In this paper, we argue that data without what-if modeling may be the database community's past, but data with what-if modeling must be its future.

Strength in Numbers: How does data-driven decision-making affect firm performance?

Abstract: We examine whether performance is higher in firms that emphasize decisionmaking based on data and business analytics (which we term a data-driven decisionmaking approach or DDD). Using detailed survey data on the business practices and information technology investments of 179 large publicly traded firms, we find that firms that adopt DDD have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage. Using instrumental variables methods, we find evidence that these effects do not appear to be due to reverse causality. Furthermore, the relationship between DDD and performance also appears in other performance measures such as asset utilization, return on equity and market value. Our results provide some of the first large scale data on the direct connection between data-driven decisionmaking and firm performance. Acknowledgements: We thank Andrew McAfee, Roger Robert, Johnson Sikes and participants at the Workshop for Information Systems and Economics and participants at the 9 th Annual Industrial Organization Conference for useful comments and the MIT Center for Digital Business for generous

Software analytics as a learning case in practice: approaches and experiences

Abstract: Software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services. In this position paper, we advocate that when applying analytic technologies in practice of software analytics, one should (1) incorporate a broad spectrum of domain knowledge and expertise, e.g., management, machine learning, large-scale data processing and computing, and information visualization; and (2) investigate how practitioners take actions on the produced information, and provide effective support for such information-based action taking. Our position is based on our experiences of successful technology transfer on software analytics at Microsoft Research Asia.

Analytics Ecosystem Transformation: A Force for Business Model Innovation

Abstract: Analytics technologies that mine large amount of structured and unstructured data to gain insights are becoming increasingly important to businesses. In particular, the growing availability of enterprise proprietary data, coupled with publically aggregated or acquired data allows analytics to gain insights not only about the enterprise itself, but also cross companies, industry, and cross industries. The impact of such analysis is that it is transforming business processes and driving strategic business decision making and business model transformations, all of which overshadow more traditional low level, siloed, tactical optimizations. Such analytics trends are driving shifts in the overall analytics ecosystem that includes data providers and aggregators, analytics technology and service providers, clients in different industries, partners, and other related communities, e.g., visualization providers, academia, open source development communities. In particular, we have observed the emergence of two new service entities in the overall ecosystem: § New forms of data services that aggregate and provide accesses to a wide range of public and private data by partnering with data providers, aggregators, and clients are emerging. We call such services "Data as a Service (DaaS)" in this paper. DaaS can leverage commonly managed Cloud and Web based infrastructure and tools as well as hosted and Web delivery models to offer rich set of data processing, management, and access services, in addition to in house implementations. § On top of DaaS, one can create high value analytics services that can boost productivity and create value for all. Such services may include Business Intelligence reporting, text analytics, and advanced analytics such as predictive modeling, all made in composable forms to allow for direct consumption, integration and customizations. We call such services "Analytics as a Service (AaaS)". DaaS and AaaS help to maximize value for the overall ecosystem by eliminating common costs and delivering high value data and analytics services. Their emergence is transforming the overall analytics ecosystem and forcing significant cost structure and productivity model shifts, i.e., where to cut cost and where to make money -- two key metrics to a business model. As a result, they are driving the emergence of new business models across the overall analytics ecosystem. In this paper, we will analyze the major analytics ecosystem trends. We show that our analysis suggests that there is an analytics ecosystem transformation undergoing. The new ecosystem will increasingly leverage new forms of data and analytics services and roles, e.g., DaaS and AaaS, to maximize value for the overall ecosystem. Such ecosystem changes drive shifts in enterprise cost structures and productivity and value creation models and creates a force for business model innovation. We will describe an evolution of business models around the analytics ecosystem and highlight the emerging business models that are enabled by the new ecosystem, many of which have an open, collaboration, and co-developing spirit. We will also present several real-world case studies to illustrate how the new ecosystem can maximize value for all by implementing innovative business models.

Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics

Abstract: "A very rich book sprinkled with real-life examples as well as battle-tested advice.Pierre Haren, VP ILOG, IBM"James does a thorough job of explaining Decision Management Systems as enablersof a formidable business transformation.Deepak Advani, Vice President, Business Analytics Products and SPSS, IBMBuild Systems That Work Actively to Help You Maximize Growth and ProfitsMost companies rely on operational systems that are largely passive. But what if you could make your systems active participants in optimizing your business? What if your systems could act intelligently on their own? Learn, not just report? Empower users to take action instead of simply escalating their problems? Evolve without massive IT investments?Decision Management Systems can do all that and more. In this book, the fields leading expert demonstrates how to use them to drive unprecedented levels of business value. James Taylor shows how to integrate operational and analytic technologies to create systems that are more agile, more analytic, and more adaptive. Through actual case studies, youll learn how to combine technologies such as predictive analytics, optimization, and business rulesimproving customer service, reducing fraud, managing risk, increasing agility, and driving growth.Both a practical how-to guide and a framework for planning, Decision Management Systems focuses on mainstream business challenges.Coverage includesUnderstanding how Decision Management Systems can transform your businessPlanning your systems with the decision in mindIdentifying, modeling, and prioritizing the decisions you need to optimizeDesigning and implementing robust decision servicesMonitoring your ongoing decision-making and learning how to improve itProven enablers of effective Decision Management Systems: people, process, and technologyIdentifying and overcoming obstacles that can derail your Decision Management Systems initiative

Realising the strategic impact of business intelligence tools

Abstract: Purpose – The purpose of this paper is to explore the effectiveness of business intelligence (BI) tools as enablers of knowledge sharing used by employees in the organisation. This practice‐oriented article on the deployment and impact of BI tools in industry suggests a balanced scorecard (BSC) approach to performance management. More specifically, a suite of web 2.0 tools is used in the practice of BI and their impact measured with a BSC.Design/methodology/approach – The research proposition is that the effectiveness of BI is indeed strategic and relates to its corporate performance. This claim is validated using a global information technology consultancy firm's BI unit as the lead case of an immersive field study. Research engagements with four other firms provide corroborative support.Findings – The BSC approach to deriving targets and ascertaining outcomes was shown to be applicable to good practice. The converse is equally valid. That is, strategic performance management requires the use of BI in or...

Toward a Goal-Oriented, Business Intelligence Decision-Making Framework

Abstract: Decision making is a crucial yet challenging task in enterprise management. In many organizations, decisions are still made based on experience and intuition rather than on facts and rigorous approaches, often because of lack of data, unknown relationships between data and goals, conflicting goals, and poorly understood risks. This paper presents a goal-oriented, iterative conceptual framework for decision making that allows enterprises to begin development of their decision model with limited data, discover required data to build their model, capture stakeholders goals, and model risks and their impact. Such models enable the aggregation of Key Performance Indicators and their integration to goal models that display good cognitive fit. Managers can monitor the impact of decisions on organization goals and improve decision models. The approach is illustrated through a retail business real-life example.

Method, system and program for data delivering using chatbot

Abstract: A computer-implemented method, system and program for interactive data delivering are described. A method for the interactive data delivering provides an effective way for retrieving, analyzing, processing and presenting business analytics data to a user in a natural, conversational way. The method may comprise receiving a request from the user to provide the analytics data in the natural language format, converting the command in the natural language format into one or more Application Programming Interface (API) calls, retrieving generic data associated with the request of the user based on the API calls, generating a semantic model associated with the generic data and the user request, processing the retrieved generic data to generate analytics data, with the processing being based on the semantic model, communicating the analytics data to a chatbot, and converting, under control of the chatbot, the analytics data into a natural language format for delivering to the user.

A maturity model of enterprise business intelligence

Abstract: The implementation of an enterprise-level business intelligence initiative is a large-scale and complex undertaking, involving significant expenditure and multiple stakeholders over a lengthy period. It is therefore imperative to have systematic guidelines for business intelligence stakeholders in referring business intelligence maturity levels. Draw upon the prudent concepts of the Capability Maturity Model, this research proposes a multi-dimensional maturity model with distinct maturity levels for managing enterprise business intelligence initiatives. The maturity model, named Enterprise Business Intelligence Maturiy (EBIM), consists of five core maturity levels and four key dimensions, namely information quality, master data management, warehousing architecture, and analytics. It can be used to assist enterprises in benchmarking their business intelligence maturity level and identifying the critical areas to attain higher level of maturity.

In-memory Databases in Business Information Systems

Abstract: In-memory databases are developed to keep the entire data in main memory. Compared to traditional database systems, read access is now much faster since no I/O access to a hard drive is required. In terms of write access, mechanisms are available which provide data persistence and thus secure transactions. In-memory databases have been available for a while and have proven to be suitable for particular use cases. With increasing storage density of DRAM modules, hardware systems capable of storing very large amounts of data have become affordable. In this context the question arises whether in-memory databases are suitable for business information system applications. Hasso Plattner, who developed the HANA in-memory database, is a trailblazer for this approach. He sees a lot of potential for novel concepts concerning the development of business information systems. One example is to conduct transactions and analytics in parallel and on the same database, i.e. a division into operational database systems and data warehouse systems is no longer necessary (Plattner and Zeier 2011). However, there are also voices against this approach. Larry Ellison described the idea of business information systems based on in-memory database as “wacko,” without actually making a case for his statement (cf. Bube 2010). Stonebraker (2011) sees a future for inmemory databases for business information systems but considers the division of OLTP and OLAP applications as reasonable. Therefore, this discussion deals with the question of whether in-memory databases as a basic data management technology can sustainably influence the conception and development of business information system or will remain a niche application. The contributors were invited to address the following research questions (among others): What are the potentials of in-memory databases for business information systems? What are the consequences for OLTP and OLAP applications? Will there be novel application concepts for business information systems? The following researchers accepted the invitation (in alphabetic order): Dr. Benjamin Fabian and Prof. Dr. Oliver Günther, Humboldt-Universität zu Berlin Prof. Dr. Donald Kossmann, ETH Zürich Dr. Jens Lechtenbörger and Prof. Dr. Gottfried Vossen, Münster University Prof. Dr. Wolfgang Lehner, TU Dresden Prof. Dr. Robert Winter, St. Gallen University Dr. Alexander Zeier with Jens Krüger and Jürgen Müller, Potsdam University Lechtenbörger and Vossen discuss the development and the state of the art of inmemory and column-store technology. In their evaluation they stress the potentials of in-memory technology for energy management (cf. Loos et al. 2011) and Cloud Computing. Zeier et al. argue that the main advantage of modern business information systems is their ability to integrate transactional and analytical processing. They see a general trend towards this mixed processing mode (referred to as OLXP). Inmemory technology supports this integration and will render the architectural separation of transactional systems and management information systems unnecessary in the future. The new database technology also greatly facilitates the integration of simulation and optimization techniques into business information systems. Lehner assumes that the revolutionary development of system technology will have a great impact on future structuring, modeling, and programming techniques for business information systems. One consequence will be a general shift from control-flow-driven to data-flowdriven architectures. It is also likely that the requirement for ubiquitously available data will be abandoned and a “needto-know” principle will establish itself in certain areas. Kossman identifies two phases in which in-memory technology will influence business information systems. The first phase is a simplification phase which is caused by a separation of data and application layers of information systems. In a second phase, however, complexity will increase since the optimization of memory hierarchies, such as the interplay between memory and cache, will also have consequences for application developers. Fabian and Günther stress that inmemory databases have already proven

The value of Competitive Business Intelligence System (CBIS) to Stimulate Competitiveness in Global Market

Abstract: Business Intelligence is the mixture of the gathering, cleaning and integrating data from various sources, and introducing results in a mode that can enhance business decisions making.BIS provide sufficient fundamentals for comparison process. Thus, nowadays, organizations desire to assess and evaluate their assets into Business Intelligence systems, which involve an accurate evaluation to the business value and distinguish it from other organizations using comparable systems. This paper describes and measures the fact that competitive advantage can be gained through Business Intelligence. It evaluates the impact of key factors of typical BIS on improving business performance to survive in competitive market.

Creating Value From Business Analytics Systems: The Impact Of Strategy

Abstract: Business analytics systems can potentially contribute to firm performance and create competitive advantage. However, these benefits do not always follow from investment in business analytics technology. This paper argues that dynamic capabilities, enabled by business analytics technology, lead to value-creating actions and ultimately to improved firm performance. We develop a theoretical model that explains how organizational strategy relates to both business analytics technology and organizational structure, and impacts value-creating actions. We use the theoretical model to explain the implementation of a CRM system for one type of strategy.

Business history in family business studies: from neglect to cooperation?

Abstract: Purpose – This paper seeks to provide a brief overview of what is business history as an academic discipline, with some reflection about its evolutionary patterns and heuristic value in other fields, as for instance, management studies. A peculiar and increasingly practised subfield of business history is that of family business studies, which is thus a promising crossroads and meeting point for both business historians, practitioners and scholars in management studies.Design/methodology/approach – Through an extensive analysis of the literature on family business studies in business history, this article highlights some potential areas of collaboration and suggests some reflections about the way in which the research methods of historians can be beneficial for management scholars.Findings – Business history has in fact a high potential in providing, through its longitudinal and comparative approach, evidence for building new theories and challenging the existing ones.Originality/value – This article trie...

Business modeling for services: Current state and research perspectives

Abstract: This paper reviews business model literature from the perspective of extant business modeling approaches in order to discover research gaps and to outline perspectives which show the possible development of business model modeling. Due to the growing importance of services for many companies and the resulting transformation of product based business models to service based business models, the paper focuses on the link between business models and services. Thus the paper identifies how the business model construct can provide support for the analysis and design of service business models. The contribution ends with a brief discussion of missing service-related aspects.

Business Analytics Based on Financial Time Series

Abstract: Baniya merchants of the Mughal Empire, burgher merchants of the Swedish Empire, and chonin merchants of the Tokugawa Shogunate had the same questions on their mind as business people do today. To which townspeople should I sell my wares? Of folks that buy from me, are there any that might stop buying from me? Which groups buy which goods? Which saris should I show Ranna Devi to make as much money as I can? How much timber will people want in the coming weeks and months? The world has changed over the centuries with globalization, rapid transportation, instantaneous communication, expansive enterprises, and an explosion of data and signals along with ample computation to process them. In this new age, many continue to answer the aforementioned and other critical business questions in the old-fashioned way, i.e., based on intuition, gut instinct, and personal experience. In our globalized world, however, this is not sufficient anymore and it is essential to replace the business person's gut instinct with science. That science is business analytics. Business analytics is a broad umbrella entailing many problems and solutions, such as demand forecasting and conditioning, resource capacity planning, workforce planning, salesforce modeling and optimization, revenue forecasting, customer/product analytics, and enterprise recommender systems. In our department, we are in creasingly directing our focus on developing models and techniques to address such business problems. The goal of this article is to provide the reader with an overview of this interesting new area of research and then hone in on applications that might require the use of sophisticated signal processing methodologies and utilize financial signals as input.

Business Analytics for Managers

Abstract: Introduction.- Exploring & Discovering Data.- Data Modeling I - Basics.- Data Modeling II - Making Models More Flexible.- Data Modeling III - Making Models More Selective.- Data Modeling IV - Fine Tuning Your Model.- Introduction to the Statistical Software R.