Showing papers on "Business analytics published in 2010"

The Impact of Business Analytics on Supply Chain Performance

Abstract: The paper investigates the relationship between analytical capabilities in the plan, source, make and deliver area of the supply chain and its performance using information system support and business process orientation as moderators. Structural equation modeling employs a sample of 310 companies from different industries from the USA, Europe, Canada, Brazil and China. The findings suggest the existence of a statistically significant relationship between analytical capabilities and performance. The moderation effect of information systems support is considerably stronger than the effect of business process orientation. The results provide a better understanding of the areas where the impact of business analytics may be the strongest.

The impact of business analytics on supply chain performance

Abstract: The paper investigates the relationship between analytical capabilities in the plan, source, make and deliver area of the supply chain and its performance using information system support and business process orientation as moderators. Structural equation modeling employs a sample of 310 companies from different industries from the USA, Europe, Canada, Brazil and China. The findings suggest the existence of a statistically significant relationship between analytical capabilities and performance. The moderation effect of information systems support is considerably stronger than the effect of business process orientation. The results provide a better understanding of the areas where the impact of business analytics may be the strongest.

The business model: theoretical roots, recent developments, and future research

Abstract: The paper provides a broad and multifaceted review of the received literature on business models, in which we attempt to explore the origin of the construct and to examine the business model concept through multiple disciplinary and subject-matter lenses. The review reveals that scholars do not agree on what a business model is, and that the literature is developing largely in silos, according to the phenomena of interest to the respective researchers. However, we also found some emerging common ground among students of business models. Specifically, i) the business model is emerging as a new unit of analysis; ii) business models emphasize a system-level, holistic approach towards explaining how firms do business; iii) organizational activities play an important role in the various conceptualizations of business models that have been proposed, and iv) business models seek not only to explain the ways in which value is captured but also how it is created. These emerging themes could serve as important catalysts towards a more unified study of business models.

The Analytics Movement: Implications for Operations Research

Abstract: The movement toward the increased use of analytics in organizations has generated much discussion by academics and professionals about the impacts and opportunities that analytics offers. Although operations research (OR) has been a driving force in applying quantitative and analytical models for organizational decision making, it is less clear how we as OR practitioners can take advantage of the surging interest in analytics to promote the OR profession and expand its reach. In this paper, we discuss the drivers of the analytics movement, an example of an analytics project, and the opportunities and implications for OR, i.e., the problem scope, models and methods, implementation issues, organizational role, professional skills, and education.

Business Analytics for Managers: Taking Business Intelligence Beyond Reporting

Abstract: What differentiates good organizations from bad? The good ones are those that take advantage of the data they already have and use the feedback that business intelligence gives them to improve their processes. SharePoint is now the delivery platform of choice for Microsoft's business intelligence pr...

Conceptual Model of Business Value of Business Intelligence Systems

Abstract: With advances in the business intelligence area, there is an increasing interest for the introduction of business intelligence systems into organizations. Although the opinion about business intelligence and its creation of business value is generally accepted, economic justification of investments into business intelligence systems is not always clear. Measuring the business value of business intelligence in practice is often not carried out due to the lack of measurement methods and resources. Even though the perceived benefits from business intelligence systems, in terms of better information quality or achievement of information quality improvement goals, are far from being neglected, these are only indirect business benefits or the business value of such systems. The true business value of business intelligence systems hides in improved business processes and thus in improved business performance. The aim of the paper is to propose a conceptual model to assess business value of business intelligence systems that was developed on extensive literature review, in-depth interviews, and case study analysis for researching business intelligence systems’ absorbability capabilities or key factors facilitating usage of quality information provided by such systems respectively.

Multitask Agency, Modular Architecture, and Task Disaggregation in SaaS

Abstract: We examine contract choices in the provision of "software-as-a-service" (SaaS), which is a business innovation that transforms information technology (IT) resources into a continuously provided service. We draw upon agency theory and modularity theory to propose that one of the central challenges in service disaggregation is that of knowledge interdependencies across client and provider organizations. The resulting lack of verifiability of certain tasks results in a multitask agency problem. Our key research questions involve (1) the suitability of high-versus low-powered incentives in SaaS contracts when the outsourced tasks involve business analytics that are difficult to verify, and (2) how such contract choices are affected by the modularity of interfaces between the client and the provider. Analysis of data collected from 154 providers of SaaS offering a range of IT services supports our contention that when contracting for business analytics characterized by knowledge interdependencies across clients and providers, incentives should be "low powered." Modularity in the interfaces of the service provider increases the desirability of high-powered incentives in such situations. Our results are robust after accounting for endogeneity issues arising from unobserved matching between service providers and the nature of IT services outsourced by clients. With the increasing importance of information systems in services, this paper suggests that arm's-length relationships and high-powered incentives may be ineffective in incentivizing providers to perform on complex business analytic tasks, unless accompanied by the modularization of interfaces.

Business Analytics and Competitive Advantage: A Review and a Research Agenda

Abstract: Business analytics has the potential to deliver performance gains and competitive advantage. However, a theoretically grounded model identifying the factors and processes involved in realizing those performance gains has not been clearly articulated in the literature. This paper draws on the literature on dynamic capabilities to develop such a theoretical framework. It identifies the critical roles of organizational routines and organization-wide capabilities for identifying, resourcing and implementing business analytics-based competitive actions in delivering performance gains and competitive advantage. A theoretical framework and propositions for future research are developed.

Towards an Understanding of Business Intelligence

Abstract: Heading – abstract) Given the wide recognition of business intelligence (BI) over the last 20 years, we performed a literature review on the concept from a managerial perspective. We analysed 103 articles related to BI in the period 1990 to 2010. We found that BI is defined as a process, a product, and as a set of technologies, or a combination of these, which involves data, information, knowledge, decision making, related processes and technologies that support them. Our findings show that the literature focuses mostly on data and information, and less on knowledge and decision making. Moreover, in relation to the processes there is a substantial amount of literature about gathering and storing data and information, but less about analysing and using information and knowledge, and almost nothing about acting (making decisions) based on intelligence. The research literature has mainly focused on technologies and neglecting the role of the decision maker. We conclude by synthesizing a unified definition of BI and identifying possible future research streams.

The New HR Analytics: Predicting the Economic Value of Your Company's Human Capital Investments

Abstract: CONTENTS PREFACE xi CONTRIBUTORS xvii PART ONE: INTRODUCTION TO PREDICTIVE ANALYTICS 1 CHAPTER ONE Disruptive Technology: The Power to Predict 3 CHAPTER TWO Toward Analytics and Prediction 8 Why Analytics Is Important 17 Measuring What Is Important, by Luis Maria Cravino Strategic Human Capital Measures: Using Leading HCM to Implement Strategy, by Stephen Gates and Pascal Langevin From Business Analytics to Rational Action, by Kirk Smith PART TWO: THE HCM:21(r) MODEL 45 CHAPTER THREE Scan the Market, Manage the Risk 47 How to Improve HR Processes 56 The Intersection of People and Profits: The Employee Value Proposition, by Joni Thomas Doolin, Michael Harms, and Shyam Patel More Than Compensation: Attracting, Motivating, and Retaining Employees, Now and in the Future, by Ryan M. Johnson "Best in Brazil": Human Capital and Business Management for Sustainability, by Rugenia Pomi CHAPTER FOUR The New Face of Workforce Planning 85 How to Put Capability Planning into Practice 94 Scenario Planning: Preparing for Uncertainty, by James P. Ware Quality Employee Engagement Measurement: The CEO's Essential Hucametric to Manage the Future, by Kenneth Scarlett Truly Paying for Performance, by Erik Berggren The Slippery Staircase: Recognizing the Telltale Signs of Employee Disengagement and Turnover, by F. Leigh Branham CHAPTER FIVE Collapsing the Silos 141 How They Are Applying It 153 Roberta Versus the Inventory Control System: A Case Study in Human Capital Return on Investment, by Kirk Hallowell The Treasure Trove You Already Own, by Robert Coon Waking the Sleeping Giant in Workforce Intelligence, by Lisa Disselkamp CHAPTER SIX Turning Data into Business Intelligence 182 How to Interpret the Data 192 Predictive Analytics for Human Capital Management, by Nico Peruzzi Using Human Capital Data for Performance Management During Economic Uncertainty, by Kent Barnett and Jeffrey Berk Using HR Metrics to Make a Difference, by Lee Elliott, Daniel Elliott, and Louis R. Forbringer PART THREE: THE MODEL IN PRACTICE 215 CHAPTER SEVEN Impacting Productivity and the Bottom Line: Ingram Content Group, by Wayne M. Keegan 217 CHAPTER EIGHT Leveraging Human Capital Analytics for Site Selection: Monster and Enterprise Rent-A-Car, by Jesse Harriott, Jeffrey Quinn, and Marie Artim 224 CHAPTER NINE Predictive Management at Descon Engineering, by Umair Majid and Ahmed Tahir 240 CHAPTER TEN Working a Mission-Critical Problem in a Federal Agency, by Jac Fitz-enz 259 CHAPTER ELEVEN UnitedHealth Group Leverages Predictive Analytics for Enhanced Staffing and Retention, by Judy Sweeney 265 PART FOUR: LOOKING FORWARD 271 CHAPTER TWELVE Look What's Coming Tomorrow 273 Views of the Future: Human Capital Analytics 276 APPENDIX: THE HCM:21(r) MODEL: SUMMARY AND SAMPLES 301 INDEX 333 ABOUT THE AUTHOR 000

Building re-usable dictionary repositories for real-world text mining

Abstract: Text mining, though still a nascent industry, has been growing quickly along with the awareness of the importance of unstructured data in business analytics, customer retention and extension, social media, and legal applications. There has been a recent increase in the number of commercial text mining product and service offerings, but successful or wide-spread deployments are rare, mainly due to a dependence on the expertise and skill of practitioners. Accordingly, there is a growing need for re-usable repositories for text mining. In this paper, we focus on dictionary-based text mining and its role in enabling practitioners in understanding and analyzing large text datasets. We motivate and define the problem of exploratory dictionary construction for capturing concepts of interest, and propose a framework for efficient construction, tuning, and re-use of these dictionaries across datasets. The construction framework offers a range of interaction modes to the user to quickly build concept dictionaries over large datasets. We also show how to adapt one or more dictionaries across domains and tasks, thereby enabling reuse of knowledge and effort in industrial practice. We present results and case studies on real-life CRM analytics datasets, where such repositories and tooling significantly cut down practitioner time and effort for dictionary-based text mining.

Live business intelligence for the real-time enterprise

Abstract: We present our vision of a unified data management and analytics platform, which we call "Live Business Intelligence" (LiveBI) that transforms business intelligence from the traditional back-office, report-oriented platform, to an enabler for delivering data-intensive, real-time analytics that transform business operations in the modern enterprise. The LiveBI Platform leverages data management technology and fuses it with new paradigms for analytics and application development. We present the architecture of the platform and illustrate its value in a couple of applications.

Processing online analytics with classification and association rule mining

Abstract: Business performance measurements, decision support systems (DSS) and online analytical processing (OLAP) have a common goal i.e., to assist decision-makers during the decision-making process. Integrating DSS and OLAP into existing business performance measurements hopes to improve the accuracy of analysis and provide in-depth, multi-angle view of data. This paper describes a decision support system containing our methodology, Weighted and Layered workflow evaluation (WaLwFA), extended to incorporate business intelligence using C4.5 and association rule algorithms. C4.5 produces more comprehensible decision trees by showing only important attributes. Furthermore, C4.5 can be transformed into IF-THEN rules. However, association rules are preferred as data can be described in rules of multiple granularities. Sorting rules based on rules' complexities permits OLAP to navigate through layers of complexities to extract rules of relevant sizes and to view data from multidimensional perspectives in each layer. Experimental results on an airline domain are presented.

Business cycle management and firm performance: Tying the empirical knot

Abstract: Purpose – Business cycles strongly influence corporate sales and profits, yet strategy research largely ignores the possibility that corporate management practices related to the business cycle influence profitability. This paper aims to offer initial empirical support for the view that high peformance firms use a variety of business cycle management (BCM) practices that low performance firms do not.Design/methodology/approach – This exploratory study examines the association of firm performance with business cycle management behaviors identified in the prescriptive literature and further developed from a set of case analyses. The empirical analysis uses a matched sample of 35 pairs of high vs low performers from the S&P 500.Findings – Discriminant and conditional logit analyses provide preliminary evidence that business cycle‐sensitive behaviors such as countercyclical hiring and investment associate positively with firm performance.Research limitations/implications – Future research should use larger da...

The impact of strategy and maturity on business analytics and firm performance: A review and research agenda

Abstract: Business analytics involves interpreting organizational data to improve decision-making and to optimise business processes. It has the potential to improve firm performance and increase competitive advantage. Although many case studies have been reported that describe business analytics applications and speculate about how they might contribute to firm performance, there is no clearly articulated and theoretically grounded model in the literature. This paper proposes a theoretical framework for understanding how and why business analytics technology and capabilities can lead to value-creating actions that lead to improved form performance and competitive advantage. We focus particularly on how strategy and maturity impact business analytics and firm performance. A number of propositions are developed from the framework and a research agenda for empirical evaluation and enhancement of the framework is proposed.

Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence

Abstract: Geared toward IT management and business executives seeking to excel in business intelligence initiatives, this practical guide explores creating business alignment strategies that help prioritize business requirements, build organizational and cultural strategies, increase IT efficiency, and promote user adoption. Business intelligence, together with business analytics and performance management, eliminates information overload by organizing the massive amounts of information available in the modern enterprise. Addressing the challenges of business intelligence operations, this resource supports the goal of better business decision making and identifying unrealized opportunities. Each chapter includes a checklist of recommended approaches and a strategy overview template.

Business and Market Intelligence 2.0

Abstract: to the “skills, technologies, applications, and practices used to support decision making” (http:// en.wikipedia.org/wiki/Business_intelligence). On the basis of a survey of 1,400 CEOs, the Gartner Group projected BI revenue to reach $3 billion in 2009.1 Through BI initiatives, businesses are gaining insights from the growing volumes of transaction, product, inventory, customer, competitor, and industry data generated by enterprise-wide applications such as enterprise resource planning (ERP), customer relationship management (CRM), supply-chain management (SCM), knowledge management, collaborative computing, Web analytics, and so on. The same Gartner survey also showed that BI surpassed security as the top business IT priority in 2006.1 BI has been used as an umbrella term to describe concepts and methods for improving business decision making by using fact-based support systems. BI also includes the underlying architectures, tools, databases, applications, and methodologies. BI’s major objectives are to enable interactive and easy access to diverse data, enable manipulation and transformation of these data, and give business managers and analysts the ability to conduct appropriate analyses and then act.2 BI is now widely adopted in the world of IT practice and has also become popular in information systems curricula.3 Successful BI initiatives have been reported for major industries—from healthcare and airlines to major IT and telecommunications fi rms.2 As a data-centered approach, BI relies heavily on various advanced data collection, extraction, and analysis technologies.2,3 Data warehousing is often considered the foundation of BI. Design of data marts and tools for extraction, transformation, and load (ETL) are essential for converting and integrating enterprise-specifi c data. Organizations often next adopt database query, online analytical processing (OLAP), and advanced reporting tools to explore important data characteristics. Business performance management (BPM) using scorecards and dashboards allow analysis and visualization of various employee performance metrics. In addition to these well-established business analytics functions, organizations can adopt advanced knowledge discovery using data and text mining for association rule mining, database segmentation and clustering, anomaly detection, and predictive modeling in various information systems and human resources, accounting, fi nance, and marketing applications. Since about 2004, Web intelligence, Web analytics, Web 2.0, and user-generated content have begun to usher in a new and exciting era of business research, which we could call Business Intelligence 2.0. An immense amount of company, industry, product, and customer information can be gathered from the Web and organized and visualized through various knowledge-mapping, Web portal, and multilingual retrieval techniques.4 By analyzing customer clickstream data logs, Web analytics tools such as Google Analytics provide a trail of the user’s online activities and reveal the user’s browsing and purchasing patterns. Web site design, product placement optimization, customer transaction analysis, and product recommendations can Business Intelligence (BI), a term coined in 1989, has gained much traction in the IT

Business Intelligence and Service-oriented Architecture: A Delphi Study

Abstract: Although service-oriented architecture (SOA) is becoming increasingly popular in enterprise application architectures, little is known about how SOA could support and influence the use and implementation of business intelligence (BI). We applied the Delphi method in order to identify opportunities and limitations of SOA concepts and technologies when applied to BI applications. This paper draws recommendations for BI vendors and for organizations that use or intend to use SOA to support BI.

Analysis of Data Quality Aspects in Data Warehouse Systems

Abstract: Data quality is a critical factor for the success of data warehousing projects. If data is of inadequate quality, then the knowledge workers who query the data warehouse and the decision makers who receive the information cannot trust the results. In order to obtain clean and reliable data, it is imperative to focus on data quality. While many data warehouse projects do take data quality into consideration, it is often given a delayed afterthought. Even QA after ETL is not good enough the Quality process needs to be incorporated in the ETL process itself. Data quality has to be maintained for individual records or even small bits of information to ensure accuracy of complete database. Data quality is an increasingly serious issue for organizations large and small. It is central to all data integration initiatives. Before data can be used effectively in a data warehouse, or in customer relationship management, enterprise resource planning or business analytics applications, it needs to be analyzed and cleansed. To ensure high quality data is sustained, organizations need to apply ongoing data cleansing processes and procedures, and to monitor and track data quality levels over time. Otherwise poor data quality will lead to increased costs, breakdowns in the supply chain and inferior customer relationship management. Defective data also hampers business decision making and efforts to meet regulatory compliance responsibilities. The key to successfully addressing data quality is to get business professionals centrally involved in the process. We have analyzed possible set of causes of data quality issues from exhaustive survey and discussions with data warehouse groups working in distinguishes organizations in India and abroad. We expect this paper will help modelers, designers of warehouse to analyze and implement quality warehouse and business intelligence applications.

Developing a business model engineering & experimentation tool - the quest for scalable 'lollapalooza confluence patterns'

Abstract: Every organization needs a viable business model. Strikingly, most of current literature is focused on business model design only, whereas there is almost no attention for business model validation and actual implementation of and experimentation with business models. The goal of the research as described in this paper is to develop a business model engineering tool supporting business model management as a continuous design, validation and implementation cycle. The tool is applied to an online investment research startup with a scalable business model in roll out and market phase. This paper describes the research as performed in a case study setting by focusing on the design, implementation and evaluation of the business model engineering tool. We also analyze the actual implementation and usage of the business model tool by the online investment research startup by focusing on the most critical actions related to actual business model implementation & experimentation – i.e. actions with so-called ‘lollapalooza tendencies’.

The Current State of Analytics in the Corporation: The View from Industry Leaders

Abstract: Business intelligence and analytics in general are currently experiencing a resurgence in interest from both the business and academic communities. As a response, a Business Analytics Special Interest Group (SIG) was formed at Villanova University in 2007 to better link these two communities and support the growing needs of business. As a multi-disciplinary group composed of both analytics professionals and academics, one of the first tasks was to investigate how businesses viewed analytics and how they were incorporating them in actual practice. With this in mind, an interview questionnaire was developed and senior-level executives from a diverse group of sixteen different firms were interviewed in a group context. Their responses led to the development of a new, integrated analytics curriculum and the establishment of a new Analytics Round Table. The results from this series of semi-structured interviews are presented in this paper.

Business Plus Intelligence Plus Technology Equals Business Intelligence

Abstract: In this article the authors will show how the parallel developments of information technology at the operational business level and decision support concepts progressed through the decades of the twentieth century with only minimal success at strategic application. They will posit that the twin technological developments of the world-wide-web and very inexpensive mass storage provided the environment to facilitate the convergence of business operations and decision support into the strategic application of business intelligence.

The state of visual analytics: Views on what visual analytics is and where it is going

Abstract: In the 2005 publication “Illuminating the Path” visual analytics was defined as “the science of analytical reasoning facilitated by interactive visual interfaces.” A lot of work has been done in visual analytics over the intervening five years. While visual analytics started in the United States with a focus on security, it is now a worldwide research agenda with a broad range of application domains. This is evidenced by efforts like the European VisMaster program and the upcoming Visual Analytics and Knowledge Discovery (VAKD) workshop, just to name two. There are still questions concerning where and how visual analytics fits in the large body of research and applications represented by the VisWeek community. This panel will present distinct viewpoints on what visual analytics is and its role in understanding complex information in a complex world. The goal of this panel is to engender discussion from the audience on the emergence and continued advancement of visual analytics and its role relative to fields of related research. Four distinguished panelists will provide their perspective on visual analytics focusing on what it is, what it should be, and thoughts about a development path between these two states. The purpose of the presentations is not to give a critical review of the literature but rather to give a review on the field and to provide a contextual perspective based on the panelists' years of experience and accumulated knowledge. Each panelist will have at most 15 minutes to present their position in order to establish the context for the audience. The rest of the time will be open for questions. The panel organizer will oversee the panel and ensure there are numerous questions of the panelists. On August 6, 2010 this world lost Jim Thomas. His influence and impact on our community were clear and profound with conferences, funding programs, research groups, consortia, government agencies, and industry. Through his intellect, energy, and passion Jim was able to create an international movement and shape a new discipline — visual analytics. This discussion on the future of visual analytics is dedicated to Jim and his work. Jim will be greatly missed but his influence and passion will always be felt.

Managing dynamic mixed workloads for operational business intelligence

Abstract: As data warehousing technology gains a ubiquitous presence in business today, companies are becoming increasingly reliant upon the information contained in their data warehouses to inform their operational decisions. This information, known as business intelligence (BI), traditionally has taken the form of nightly or monthly reports and batched analytical queries that are run at specific times of day. However, as the time needed for data to migrate into data warehouses has decreased, and as the amount of data stored has increased, business intelligence has come to include metrics, streaming analysis, and reports with expected delivery times that are measured in hours, minutes, or seconds. The challenge is that in order to meet the necessary response times for these operational business intelligence queries, a given warehouse must be able to support at any given time multiple types of queries, possibly with different sets of performance objectives for each type. In this paper, we discuss why these dynamic mixed workloads make workload management for operational business intelligence (BI) databases so challenging, review current and proposed attempts to address these challenges, and describe our own approach. We have carried out an extensive set of experiments, and report on a few of our results.

Enabling Real-Time Business Intelligence

Abstract: Book file PDF easily for everyone and every device. You can download and read online Enabling Real-Time Business Intelligence: Third International Workshop, BIRTE 2009, Held at the 35th International Conference on Very Large Databases, VLDB 2009, Lyon, France, August 24, 2009, Revised Selected Papers file PDF Book only if you are registered here. And also you can download or read online all Book PDF file that related with Enabling Real-Time Business Intelligence: Third International Workshop, BIRTE 2009, Held at the 35th International Conference on Very Large Databases, VLDB 2009, Lyon, France, August 24, 2009, Revised Selected Papers book. Happy reading Enabling Real-Time Business Intelligence: Third International Workshop, BIRTE 2009, Held at the 35th International Conference on Very Large Databases, VLDB 2009, Lyon, France, August 24, 2009, Revised Selected Papers Bookeveryone. Download file Free Book PDF Enabling Real-Time Business Intelligence: Third International Workshop, BIRTE 2009, Held at the 35th International Conference on Very Large Databases, VLDB 2009, Lyon, France, August 24, 2009, Revised Selected Papers at Complete PDF Library. This Book have some digital formats such us :paperbook, ebook, kindle, epub, fb2 and another formats. Here is The Complete PDF Book Library. It's free to register here to get Book file PDF Enabling Real-Time Business Intelligence: Third International Workshop, BIRTE 2009, Held at the 35th International Conference on Very Large Databases, VLDB 2009, Lyon, France, August 24, 2009, Revised Selected Papers.

Healthcare Provider Performance Analysis and Business Management System

Abstract: A healthcare provider performance analysis and business management system to provide a business-centric analysis of healthcare provider performance indicators. A comparison of the healthcare provider's business performance (as indicated by data collected from the provider for a number of business metrics) against best practices at similarly-situated healthcare providers (as represented by the “benchmarks” used for evaluation of business metrics) may allow the performance analysis system to provide feedback and best practice recommendation to the healthcare provider customer whose business performance is under evaluation. The evaluation of business metrics may proactively identify strengths and weaknesses within a customer's business organization, thereby helping executive management to identify business areas or practices that need the most attention or improvement. Customer-tailored technology solutions may be created to assure measurable and sustainable results. Because of rules governing Abstracts, this Abstract should not be used to construe the claims in this patent application.