The revolution of Internet-of-Things (IoT) is reshaping the modern food supply chains with promising business prospects. To be successful in practice, the IoT solutions should create "income-centric" values beyond the conventional "traceability-centric" values. To accomplish what we promised to users, sensor portfolios and information fusion must correspond to the new requirements introduced by this income-centric value creation. In this paper, we propose a value-centric business-technology joint design framework. Based on it the income-centric added-values including shelf life prediction, sales premium, precision agriculture, and reduction of assurance cost are identified and assessed. Then corresponding sensor portfolios are developed and implemented. Three-tier information fusion architecture is proposed as well as examples about acceleration data processing, self-learning shelf life prediction and real-time supply chain re-planning. The feasibilities of the proposed design framework and solution have been confirmed by the field trials and an implemented prototype system.

Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion

Purpose – The paper intends to find out the business justifications and requirements for incorporating business intelligence (BI) in organizations because many organizations that already have systems in place to collect data and gather information, often find themselves in a situation where they have no tools or roadmaps to put their vast data and information into use for strategic decision making.Design/methodology/approach – In this paper BI and the growing potential for implementing BI is explained. The paper also explains a checklist for implementing BI.Findings – During the last ten years, the approach to business management in the entire globe has deeply changed. Firms have understood the importance of enforcing achievement of the goals defined by their strategy through metrics‐driven management. Firms are evolving into new forms based on knowledge and networks in response to an environment characterized by indistinct organizational boundaries and fast‐paced change. New and complex changes are emerg...

Business justification with business intelligence

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations.

* Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach.

* Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices.

* A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online.

Table of Contents

Introduction
The Reason for This Book 
Intended Audiences 
Structure of This Book 
How to Use This Book 
Acknowledgements 

Chapter 1 Overview
Impact of Information and Data Quality 
About the Methodology 
Approaches to Data Quality in Projects 
Engaging Management 

Chapter 2 Key Concepts
Introduction
Framework for Information Quality (FIQ) 
Information Life Cycle 
Data Quality Dimensions 
Business Impact Techniques 
Data Categories
Data Specifications
Data Governance and Stewardship 
The Information and Data Quality Improvement Cycle 
The Ten Steps? Process
Best Practices and Guidelines

Chapter 3 The Ten Steps
1. Define Business Need and Approach 
2. Analyze Information Environment
3. Assess Data Quality
4. Assess Business Impact 
5. Identify Root Causes 
6. Develop Improvement Plans 
7. Prevent Future Data Errors 
8. Correct Current Data Errors 
9. Implement Controls 
10. Communicate Actions and Results 

Chapter 4 Structuring Your Project
Projects and The Ten Steps
Data Quality Project Roles
Project Timing 

Chapter 5 Other Techniques and Tools
Introduction
Information Life Cycle Approaches
Capture Data 
Analyze and Document Results 
Metrics 
Data Quality Tools 
The Ten Steps and Six Sigma 

Chapter 6 A Few Final Words

Appendix Quick References
Framework for Information Quality 
POSMAD Interaction Matrix Detail 
POSMAD Phases and Activities 
Data Quality Dimensions 
Business Impact Techniques 
The Ten Steps? Overview 
Definitions of Data Categories

Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information TM

http://kt.ijs.si/nada_lavrac/Publications/JBI-2007-DataMiningAndVisualization.pdf

Data mining and visualization for decision support and modeling of public health-care resources

Business intelligence as a key strategy for development organizations

Making business intelligence work: Start-to-finish guidance for managersThis book offers a true enterprise view of business intelligence IBM expert Mike Biere shows managers how to create a coherent BI plan that reflects the needs of users throughout the organization-and then implement that plan successfully Biere explains how to objectively assess the business case for BI, and identifies proven solutions for the obstacles that lead many BI projects to fail Coverage includes: Setting appropriate expectations and goals for your BI project Understanding how the key components of a complete BI solution fit together Designing effective BI solutions-including content management, handling unstructured data, and end-user segmentation Providing effective support for BI end users Introducing Corporate Performance Management (CPM): an executive's view of BI Previewing tomorrow's "next wave" in BI solutions Comprehensive checklists for planning your BI project

Business intelligence for the enterprise

A Complete Blueprint for Maximizing the Value of Business Intelligence in the Enterprise The typical enterprise recognizes the immense potential of business intelligence (BI) and its impact upon many facets within the organizationbut its not easy to transform BIs potential into real business value. In The New Era of Enterprise Business Intelligence, top BI expert Mike Biere presents a complete blueprint for creating winning BI strategies and infrastructure, and systematically maximizing the value of information throughout the enterprise. This product-independent guide brings together start-to-finish guidance and practical checklists for every senior IT executive, planner, strategist, implementer, and the actual business users themselves. Drawing on thousands of hours working with enterprise customers, Biere helps decision-makers choose from todays unprecedented spectrum of options, including the latest BI platform suites and appliances. He offers practical, in-the-trenches insights on a wide spectrum of planning and implementation issues, from segmenting and supporting users to working with unstructured data. Coverage includes Understanding the scope of todays BI solutions and how they fit into existing infrastructure Assessing new options such as SaaS and cloud-based technologies Avoiding technology biases and other project killers Developing effective RFIs/RFPs and proofs of concept Setting up competency centers and planning for skills development Crafting a better experience for all your business users Supporting the requirements of senior executives, including performance management Cost-justifying BI solutions and measuring success Working with enterprise content management, text analytics, and search Planning and constructing portals, mashups, and other user interfaces Previewing the future of BI

Mike Biere

Papers

Business intelligence for the enterprise

The New Era of Enterprise Business Intelligence: Using Analytics to Achieve a Global Competitive Advantage