Showing papers on "Business analytics published in 2006"

Real Time Business Intelligence for the Adaptive Enterprise

Abstract: In today's competitive environment, analysing data to predict market trends and to improve enterprise performance is an essential business activity. However, it is becoming clear that business success requires such data analysis to be carried out in real-time, and that actions in response to analysis results must also be performed in realtime in order to meet the rapid change in demand from customers and regulators alike. This paper discusses issues and problems of current business intelligence systems, and then outlines our vision of future real-time business intelligence. We present a list of emerging technologies that are being developed within the research program of British Telecommunications plc (BT), which could contribute to the realisation of real-time business intelligence, in addition to some examples of applying these technologies to improve BT’s systems and services.

Creating knowledge-driven healthcare processes with the Intelligence Continuum.

Abstract: Medical science has made revolutionary changes in the past few decades. Contemporaneously, however, healthcare has made incremental changes at best. One area within healthcare that best exemplifies this is the operating room (OR). The growing discrepancy between the revolutionary changes in medicine and the minimal changes in healthcare processes leads to inefficient and ineffective healthcare deliver and one if not the significant contributor to the exponentially increasing costs plaguing healthcare globally. Significant quantities of data and information permeate the healthcare industry, yet the healthcare industry has not maximised this data resource by fully embracing key business management processes or techniques (such as Knowledge Management (KM), data mining, Business Intelligence (BI) or Business Analytics (BA)) to capitalise on realising the full value of this data/information resource to reengineer processes. The Intelligence Continuum (IC), a Mobius strip of sophisticated tools, techniques and process provides a systematic mechanism for healthcare organisations to facilitate superior clinical practice and administrative management. In this paper, the case example of the orthopaedic OR is used to illustrate the power of the IC in effecting more efficient and effective healthcare processes to ensue and thereby enabling healthcare to make evolutionary changes.

Techniques, Process, and Enterprise Solutions of Business Intelligence

Abstract: Business intelligence (BI) has been viewed as sets of powerful tools and approaches to improving business executive decision-making, business operations, and increasing the value of the enterprise. The technology categories of BI mainly encompass data warehousing, OLAP, and data mining. This article reviews the concept of Business Intelligence and provides a survey, from a comprehensive point of view, on the BI technical framework, process, and enterprise solutions. In addition, the conclusions point out the possible reasons for the difficulties of broad deployment of enterprise BI, and the proposals of constructing a better BI system.

Dimensional Modeling: In a Business Intelligence Environment

Abstract: In this IBM Redbook we describe and demonstrate dimensional data modeling techniques and technology, specifically focused on business intelligence and data warehousing. It is to help the reader understand how to design, maintain, and use a dimensional model for data warehousing that can provide the data access and performance required for business intelligence. Business intelligence is comprised of a data warehousing infrastructure, and a query, analysis, and reporting environment. Here we focus on the data warehousing infrastructure. But only a specific element of it, the data model - which we consider the base building block of the data warehouse. Or, more precisely, the topic of data modeling and its impact on the business and business applications. The objective is not to provide a treatise on dimensional modeling techniques, but to focus at a more practical level. There is technical content for designing and maintaining such an environment, but also business content. For example, we use case studies to demonstrate how dimensional modeling can impact the business intelligence requirements for your business initiatives. In addition, we provide a detailed discussion on the query aspects of BI and data modeling. For example, we discuss query optimization and how you can determine performance of the data model prior to implementation. You need a solid base for your data warehousing infrastructure . . . . a solid data model.

Predictive business operations management

Abstract: The ability to forecast metrics and performance indicators for business operations is crucial to proactively avoid abnormal situations, and to do effective business planning. However, expertise is typically required to drive each step of the prediction process. This is impractical when there are thousands of metrics to monitor. Fortunately, for business operations management, extreme accuracy is not required. It is usually enough to know when a metric is likely to go beyond the normal range of values. This gives opportunity for automation. In this paper, we present an engine that completely automates the prediction of metrics to support a better management of business operations.

Business intelligence for telecommunications

Abstract: BUSINESS INTELLIGENCE: INTRODUCTION Overview. Intelligence Not Just Information. What Is Business Intelligence. Business Intelligence Heritage. Evolution of Business Intelligence. The Present. The Value of Business Intelligence. New Era of Enterprise Technology. Business Intelligence Applications. BUSINESS INTELLIGENCE: ESSENTIALS Facts, Data, Information, and Knowledge. BI Enabling Environment. Business Landscape. Changing Data Landscape. Data Quality. Business Intelligence Framework. Business Intelligence Platform. Data Architecture. BUSINESS INTELLIGENCE: STAGES Move Data. Extract, Transform, and Load (ETL). Data Warehouse. Data Marts. OLAP. Balanced Scorecard. Data Mining. Data Management. Data Usage. Enterprise Portal (EP). BUSINESS INTELLIGENCE: TYPES Multiplicity of BI Tools. Types of BI. Modern BI. Support for Personalization and Customization. Support for Wide Reach, High Throughput, and Access Across All Touch Points. The Enterprise BI. Information Workers. Reporting. IT Professionals. Critical BI for the Enterprise. BUSINESS INTELLIGENCE: SOLUTION AREAS BI Application Area. BI Applications. Market Analytics. Sales Reporting and Analytics. Sales Pipeline Reporting and Analysis. Channel Analysis. Competitor Analysis. Financial Reporting and Analysis. Compliance. HIPAA. Towards Enterprise BI. REAL-TIME BUSINESS INTELLIGENCE First Generation BI-Learning from The Past. A New Mindset-Information Now. Operational BI-A Business Imperative. Two to Tango-Business Performance Management and Real-Time Enterprise. Real-Time Initiatives. Different Flavors of RTE. Barriers to Real-Time Enterprise. TELECOMMUNICATIONS INDUSTRY: CHANGING LANDSCAPE 4 Cs of the Telecommunications Industry. Towards a Customer-Centric Business Model. At the Crossroads. The Customer Is King. Technology Can Help Telecommunications Companies. BI in the Telecommunications Industry. Strategy at Work. BI in Action. Disappointments from the Past. Barriers to BI. Overcoming Barriers. Customer Intelligence. Look Beyond the Organization. Role of Business Intelligence. Competitive Analysis. BUSINESS INTELLIGENCE: IMPACT Telecom Challenge. Performance Management. BI the Core of BPM. Understanding Business Performance Management. Designing and Implementing BPM. Three Waves of Performance Improvement. Impact of BI and BPM on the Telecommunications Industry. BUSINESS INTELLIGENCE: ISSUES AND CHALLENGES The BI Ghetto. Critical Challenges for Business Intelligence Success. BI Application Development Methodology. Creating a Cost-Effective Enterprise Friendly BI Solution. BUSINESS INTELLIGENCE: STRATEGY AND ROADMAP Planning a Business Intelligence Solution. CONCLUSIONS Usability Versus Feature Bloat. Enough Already about Metadata! Consulting. Licensing, Upgrades, and Maintenance. Resume Building.

Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance

Abstract: Solutions for all Industries,packaged applications,business applications, Web Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance For more details on what to document, check out my book Star Schema: The Complete Understanding performance often starts with summarized data on Our OLAP solutions provide historic detail to Business Analytics in the form of data from the For tips on how to make your data warehouse more useful to analytic Data Warehousing and OLAP Technology July 2, 2015 1 High performance for both systems – DBMS— tuned for To summarize Modeling data warehouses: dimensions & measures – Star schema Example of Star Schema whose count or other aggregates satisfying the condition Indexing OLAP Data: Bitmap Index

Knowledge and Process Based Decision Support in Business Intelligence System

Abstract: As a kind of data-driven decision support systems, business intelligence tools focus too much on data and have low efficiency of decision making. Companies in today are more process-oriented than in the past and process-driven decision support system is emerging to help enterprises improve the speed and effectiveness of business operations. In order to provide the business intelligence system with the ability of process-driven decision making, we introduce the concept of business process management to the current business intelligence system. We add the process model component in our business intelligence model base. With the implementation of case-based reasoning and rule-based reasoning technology, the process models can be built and managed efficiently. In this paper we also provide a strategy for knowledge management in business intelligence system.

Business intelligence and the telecommunications industry: can business intelligence lead to higher profits?

Abstract: The telecommunication industry is characterized as being volatile due to the nature of fast-moving technological changes. It is necessary therefore to accept that ordinary everyday business decisions that are taken to solve problems or identify opportunities are knowledgeable, accurate and timeous. These informed decisions are necessary in the normal course of every day business activities to ensure that the industry continues to grow and deliver effective and efficient services in terms of its mandate. What is assumed in terms of this article is that good decisions are not consistently made in the telecommunications sector, resulting in profit maximization being compromised. The question thus arises whether profitability will increase if consistent and good decisions are made in the ordinary course of doing business. This research attempted to address the question posed above and, although both approaches are addressed, more emphasis is placed on the technical aspect than on the business processes.

Enhanced business and inventory management systems

Abstract: Various methods and systems such as in a software application are presented which may include automated purchase recommendations based on amalgamated purchase constraints (4) for business inventory maintenance, notation of events through noted event entry portals (31) for business related observation recordation, automatic facilitation of regular debut of initial suggested industry tied information (50), presentation of business performance data (65) and disparate substantially redundant business performance data (67) in a business performance display (66) for a summary review of business operations, and easy selection of inventory items (83) from a recommended hierarchical categorization of inventory items which may be used to create buying reports, ad hoc reports, and the like.

Practical considerations for real-time business intelligence

Abstract: The area of real-time business intelligence is ill defined in industry. In this extended abstract we highlight the practical requirements through the use of examples across several domains.

Model Driven Data Warehousing for Business Performance Management

Abstract: Traditional data warehouses are manually designed starting from specific requirements and anticipated data analysis needs. As a result there is frequently a disconnect between business process models, business definition of data artifacts and the data stored in the data warehouses as they are often designed manually and in isolation. Hence it has always been a challenge to keep the data warehouse in sync with the continuously changing business process models, resulting in both high maintenance costs and lost opportunities. In this paper we present our Model Driven Data Warehousing (MDDW) approach in the area of Business Performance Management (BPM). The purpose of MDDW is to bridge the gap between the business process models and the data warehouse models, thus enable the rapid adaptation to changes in the business environment. We describe our modeling framework comprising the various modeling elements and meta-models that capture both business and IT data artifacts.

Guest Editorial: Special Section on Visual Analytics

Abstract: ISUAL analytics is the science of analytical reasoning supportedbyhighlyinteractivevisualinterfaces.People use visual analytics tools and techniques to synthesize information; derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessments effectively for action. The issues stimulating this body of research provide a grandchallengeinscience:turninginformationoverloadinto the opportunity of the decade. Visual analytics requires interdisciplinary science beyond traditional scientific and information visualization to include statistics, mathematics, knowledge representation, management and discovery technologies, cognitive and perceptual sciences, decision sciences, and more. An important research agenda to develop the next generation suite of visual analytics technologies is described in the book Illuminating the Path: The Research and Development Agenda for Visual Analytics, which is available at http:// nvac.pnl.gov/agenda.stm. The papers in this special section address a number of the issues described in the visual analytics research agenda. They are grouped into five major areas: multidimensional data, graphs and networks, communication network analysis, space and time, and fundamentals. The first two papers address issues of visual analysis of multidimensional data. The first paper, “High-Dimensional

Plan for profit and achieve profit: lessons learnt from a business management simulation

Abstract: In this paper we introduce one new promising learning environment based on a business management simulation, discuss briefly how it was built, and how it has been used to cope with the business educational challenges This learning environment has been used to train more than 1500 students and managers In general, business education is research oriented, the knowledge base given to young professionals is abstract, and students lack the necessary skills to translate abstract knowledge to efficient practice It is difficult to teach practical skills with traditional teaching methods The learning environment was developed to answer to this challenge

Business Process Intelligence.

Abstract: An optical deterioration-accelerating weather and optical resistance testing device has a light source and a frame supporting a sample to be tested rotating around the light source. Thermal deterioration of the sample due to radiant heat generated by the light source is inhibited by the provision of a cold air guide enclosing a portion of the sample rotating frame on which the sample is supported. Cold air is introduced to the surfaces of the sample through the cold air guide to maintain the both sides of the sample at a constant test temperature.

Business Intelligence Reifegradmodelle

Abstract: Die derzeitige Marktsituation im Bereich Business Intelligence zeichnet sich durch eine Vielfalt unterschiedlichster Ansatze zur Analyse und Auswertung von Geschaftsprozessen aus. Der folgende Beitrag liefert einen Ansatz, um die Heterogenitat der verschiedenen, in der Praxis unter der Bezeichnung Business Intelligence zusammengefassten Losungen in Form eines Reifegradmodells zu kategorisieren und auf dieser Basis jeweils strategische Implikationen abzuleiten.

A Tool for Intelligent Customer Analytics

Abstract: Businesses collect and keep large volumes of customer data as part of their processes Analysis of this data by business users often leads to discovery of valuable patterns and trends that otherwise would go unnoticed and that can lead to prioritization of decisions on future investments The majority of tools currently available to business users are typically limited to computing summary statistics, simple visualization and reporting of data More complex tools that could offer possible explanations for observations, discover knowledge, or allow making predictions are usually aimed at an academic audience or at users who are highly trained in analytics However, it is business users with little experience in analytics who require access to tools that allow them to easily model customer behavior and build future scenarios In this paper we present a tool we developed for business users to perform advanced analysis on customer data

Business process transformation patterns & the business process transformation wizard

Abstract: In spite of many advances in business process simulation technologies, their adoption by the business analyst community has been primarily limited to specialists. We propose a Business Process Transformation Wizard as a capability to bridge this gap. This enables analysts to explore different business process transformation options using Business Process Transformation patterns and analyze their performance using quantitative technologies. In this paper, we explain the approach encapsulated in the BPT Wizard and illustrate it with an example.

FIM: A Business Information System for Intelligence

Abstract: Intelligence, now ingrained into all major law enforcement agencies, is expanding into mainstream business practices as “competitive intelligence.” It is absorbing enormous resources, and not just ...

XML and knowledge based process model reuse and management in business intelligence system

Abstract: As a kind of data-driven decision support systems, business intelligence tools focus too much on data. In order to provide the business intelligence system with the ability of process-driven decision making, we introduce the concept of business process management to the current business intelligence system. With the implementation of case-base reasoning and rule-base reasoning technology, the process models can be built and managed efficiently. In this paper we also provide a strategy for data mining experience reuse. Process models in our system are all defined based on XML.