Jamshid A. Vayghan

Service-oriented technology and management: Perspectives on research and practice for the coming decade

Service-oriented technologies and management have gained attention in the past few years, promising a way to create the basis for agility so that companies can deliver new, more flexible business processes that harness the value of the services approach from a customers perspective. Service-oriented approaches are used for developing software applications and software-as-a-service that can be sourced as virtual hardware resources, including on-demand and utility computing. The driving forces come from the software engineering community and the e-business community. Service-oriented architecture promotes the loose coupling of software components so that interoperability across programming languages and platforms, and dynamic choreography of business processes can be achieved. Nevertheless, one of todays most pervasive and perplexing challenges for senior managers deals with how and when to make a commitment to the new practices. The purpose of this article is to shed light on multiple issues associated with service-oriented technologies and management by examining several interrelated questions: why is it appropriate now to study the related business problems from the point of view of services research? What new conceptual frameworks and theoretical perspectives are appropriate for studying service-oriented technologies and management? What value will a service science and business process modeling offer to the firms that adopt them? And, how can these approaches be implemented so as to address the major challenges that organizations face with technology, information and strategy? We contribute new knowledge in this area by tying the economics and information technology strategy perspectives to the semantic and design science perspectives for a broader audience. Usually the more technical perspective is offered on a standalone basis, and confined to the systems space - even when the discussion is about business processes. This article also offers insights on these issues from the multiple perspectives of industry and academic thought leaders.

The internal information of IBM

J. A. Vayghan S. M. Garfinkle C. Walenta D. C. Healy Z. Valentin The ability to utilize data as an enterprise asset is central to every enterprise transformation initiative. This ability is critical for reusing data consistently throughout the enterprise and deriving actionable knowledge from it. Accurate and high-quality data must consistently propagate meaning and value throughout the enterprise and comply with the policies and processes of the enterprise. For a variety of reasons, large enterprises manage data at a local level (e.g., for each department and location), resulting in information ‘‘silos’’ where data is redundantly stored, managed, and processed, each with its own policies and processes, leading to inconsistency. IBM has begun a transformation process to establish a program for the management of its critical data, beginning with the creation of an enterprise data strategy that is aligned with IBM business strategy. In this paper, we describe the progress, to date, of the IBM transformation process. We focus on the activities of the IBM Enterprise Business Information Center of Excellence (EBI CoE), which is responsible for establishing, implementing, and deploying the enterprise data architecture program. The EBI CoE uses leading-edge information management technology and services from IBM and its partners to address enterprise data challenges. We present lessons learned and best practices derived from this ongoing internal transformation process that can be useful for enterprises facing similar data challenges as they transform their operations and business models.

Editorial: Business and data analytics: New innovations for the management of e-commerce

The success of many different kinds of e-business operations depends on data and information, and how they are used to optimize operations, drive sales and marketing, and grow the business. The ability to manage and safeguard data as a strategic asset, transform it into actionable information, and use it as a strategic differentiator is a key contributor to the success of any business operation. What makes this an even more interesting challenge is the speed at which data have been growing in recent years, due to social networking, the Internet, mobile telephony and all kinds of new technologies that create and capture data. The popular press, McKinsey Consulting, IBM Research and many other organizations are now referring to this development with the phrase ‘‘big data’’. The broad recognition in industry is that e-commerce operations must be designed to take advantage of the data that have become available, as a basis for improving customer service, achieving firm awareness and making markets effective places for economic exchange. As a result, traditional data management, data engineering, and data analytics techniques do not seem to work well with the historically large amounts of data that many e-commerce operations face today. Indeed, these day electronic commerce is all about data! Despite the critical role that data play in the success or failure of e-commerce firms, there has not been enough research on effective ways to leverage it to create meaningful information for management and strategy in e-commerce. This special issue is intended to highlight the need for more systematic research and investigation in this important area. The research should include many different types of data, including: quantitative and qualitative data; text, audio and video data; stocks of existing archived data and flows of contemporaneous data streams; and transaction-based, opinion-related and temporally-changing data. We also need to conduct more research to understand the entire life cycle involving data in organizations, including the highly digital environments of e-commerce firms, and also the operations of other firms in more traditional industries. Our emphasis should be on understanding the processes of data generation, data acquisition, data transformation and data integration. We also need to consider data cost and quality, data retention, data analytics and visualization, and data infrastructure and governance. In a word, industry needs many more innovative ways to manage and use big data to support ‘‘smart’’ e-commerce. With these challenges in mind – and with the added impetus of the flood of big data news flowing from the many business press sources, in 2008 we initiated discussions about constructing a special issue on the topic of ‘‘Business and Data Analytics’’. For some years already, understanding the outcomes based on working with large data sets was a part of our professional agenda – at the University of Minnesota and Arizona State University, and also at IBM

EditorialBusiness and data analytics: New innovations for the management of e-commerce

The success of many different kinds of e-business operations depends on data and information, and how they are used to optimize operations, drive sales and marketing, and grow the business. The ability to manage and safeguard data as a strategic asset, transform it into actionable information, and use it as a strategic differentiator is a key contributor to the success of any business operation. What makes this an even more interesting challenge is the speed at which data have been growing in recent years, due to social networking, the Internet, mobile telephony and all kinds of new technologies that create and capture data. The popular press, McKinsey Consulting, IBM Research and many other organizations are now referring to this development with the phrase ‘‘big data’’. The broad recognition in industry is that e-commerce operations must be designed to take advantage of the data that have become available, as a basis for improving customer service, achieving firm awareness and making markets effective places for economic exchange. As a result, traditional data management, data engineering, and data analytics techniques do not seem to work well with the historically large amounts of data that many e-commerce operations face today. Indeed, these day electronic commerce is all about data! Despite the critical role that data play in the success or failure of e-commerce firms, there has not been enough research on effective ways to leverage it to create meaningful information for management and strategy in e-commerce. This special issue is intended to highlight the need for more systematic research and investigation in this important area. The research should include many different types of data, including: quantitative and qualitative data; text, audio and video data; stocks of existing archived data and flows of contemporaneous data streams; and transaction-based, opinion-related and temporally-changing data. We also need to conduct more research to understand the entire life cycle involving data in organizations, including the highly digital environments of e-commerce firms, and also the operations of other firms in more traditional industries. Our emphasis should be on understanding the processes of data generation, data acquisition, data transformation and data integration. We also need to consider data cost and quality, data retention, data analytics and visualization, and data infrastructure and governance. In a word, industry needs many more innovative ways to manage and use big data to support ‘‘smart’’ e-commerce. With these challenges in mind – and with the added impetus of the flood of big data news flowing from the many business press sources, in 2008 we initiated discussions about constructing a special issue on the topic of ‘‘Business and Data Analytics’’. For some years already, understanding the outcomes based on working with large data sets was a part of our professional agenda – at the University of Minnesota and Arizona State University, and also at IBM

Analytical Customer Relationship Management

The Internet has emerged as a low-cost, low-latency, and high-bandwidth customer communication channel. Its interactive nature provides an organization the ability to enter into a close, personalized dialog with individual customers. The simultaneous maturation of data management technologies like data warehousing and data mining have created the ideal environment for making customer relationship management (CRM) a much more systematic effort than it has been in the past. In this article, we describe how data analytics can be used to make various CRM functions like customer segmentation, communication targeting, retention, and loyalty much more effective. We briefly describe the key technologies needed to implement analytical CRM, and the organizational issues that must be carefully handled to make CRM a reality. The goal is to illustrate problems that exist with current CRM efforts, and how using data analytics techniques can address them. Keywords: customer segmentation; customer profiles; data analytics; data warehouse; data mining; Lines of Business (LOBs)