Data quality applied to an academic business intelligence solution: Lesson learned

2017 ◽  
Author(s):  
Marshall Fernandez ◽  
Abraham Davila ◽  
Paula Angeleri
Keyword(s):  
Data Quality ◽  
Business Intelligence

Strategy, Action Plan, and Approaches for Business Intelligence in Banking and Mining

2019 ◽  
pp. 305-325
Author(s):  
Hafeez Niazi
Keyword(s):  
Data Quality ◽  
Data Visualization ◽  
Business Intelligence ◽  
Success Factors ◽  
Action Plan ◽  
Study Analysis ◽  
Key Factors ◽  
Case Study Analysis ◽  
Critical Success

This case study will analyze the critical success factors and key matters related to the deployment of BI deployment in different organizations. Different organizations have different approaches to making BI available for different business users, divisions, and departments. Data visualization is also one of the important factors which will provide user better reflection of data rather than make them confuse about organization data with too much information in the reports and dashboards. Data quality and diverse standards, which make BI famous in the different organizations, are also analyzed during the investigation of both organizations used in this case study. The case study analysis also shows how BI maturity, governance, and framework are key factors involved in the successful deployment of the BI in different organizations.

Big Data Quality for Data Mining in Business Intelligence Applications

2021 ◽  
pp. 64-91
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Quality ◽  
Business Intelligence ◽  
Future Research ◽  
Quality Factors ◽  
Data Volume ◽  
Mining Algorithms ◽  
Data Mining Application

Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of transition from raw data to standardized datasets. These have created a need for expanding the traditional data quality (DQ) factors into BDQ (big data quality) factors besides the need for new BDQ assessment and measurement frameworks for data mining and BI applications. However, very limited advancement has been made in research and industry in the topic of BDQ and their relevance and criticality for data mining and BI applications. Data quality in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in business intelligence applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI system has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of BDQ definitions and measurement for data mining for BI, analyzes the gaps therein, and provides a direction for future research and practice in this area.

Aspects of Data Quality That Cause Impact on Business Intelligence Systems

2013 ◽  
Author(s):  
Patricia Alves de Freitas ◽  
Everson Andrade dos Reis ◽  
Wanderson Senra Michel ◽  
Mauro Edson Gronovicz ◽  
Marcio Alexandre de Macedo Rodrigues
Keyword(s):  
Data Quality ◽  
Business Intelligence ◽  
Business Intelligence Systems

Depicting Data Quality Issues in Business Intelligence Environment through a Metadata Framework

2016 ◽  
Vol 7 (2) ◽  
pp. 20-31
Author(s):  
Te-Wei Wang ◽  
Yuriy Verbitskiy ◽  
William Yeoh
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Business Intelligence ◽  
Quality Data ◽  
Data Quality Management ◽  
Metadata Model ◽  
Modern Business ◽  
Quality Framework ◽  
Promising Solution ◽  
Quality Dimensions

Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize their analysis by proposing a BI data quality framework.

Improving Data Quality in Intelligent eCRM Applications

2018 ◽  
pp. 1616-1626 ◽  
Author(s):  
Bashar Shahir Ahmed ◽  
Fadi Amroush ◽  
Mohammed Ben Maati
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Business Process ◽  
Business Intelligence ◽  
Business Processes ◽  
Global Market ◽  
Integrated Systems ◽  
Business Intelligence Systems ◽  
Tools And Techniques

Today most of the businesses are in continuous search of sophisticated tools and techniques to progressively grow their business. And therefore, the use of intelligence systems has found its pace in the global market. The intelligence systems has mostly effected the E-CRM as it is the most critical and central part for the growth of the business. The E-CRM approaches have enhanced drastically with an integration of the business intelligence systems and organizations are now diligently striving for excellence by gaining benefit from these integrated systems. However, there are many organizations which lag behind in escalating their progress and growth as they have not yet understand how to improve the data quality by using business intelligence systems and therefore used it for decision making. Hence, the following research is conducted to study the implementation trends of Intelligence E-CRM in business process and how the business intelligence systems could help in improvising the data quality and the business processes.

Business Intelligence for Healthcare

2016 ◽  
pp. 2146-2170
Author(s):  
Jack S. Cook ◽  
Pamela A. Neely
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Business Intelligence ◽  
Information Quality ◽  
Management Process ◽  
Insurance Company ◽  
Process Data ◽  
Study Approach ◽  
Intelligence System ◽  
Revenue Cycle

Using an interpretive case study approach, this chapter describes the data quality problems in two companies: (1) a Multi-Facility Healthcare Medical Group (MHMG), and (2) a Regional Health Insurance Company (RHIS). These two interpretive cases examine two different processes of the healthcare supply chain and their integration with a business intelligence system. Specifically, the issues examined are MHMG's revenue cycle management and RHIS's provider enrollment and credentialing process. A Data and Information Quality (DIQ) assessment of the revenue cycle management process demonstrates how a framework, referred to as PGOT, can identify improvement opportunities within any information-intensive environment. Based on the assessment of the revenue cycle management process, data quality problems associated with the key processes and their implications for the healthcare organization are described. This chapter provides recommendations for DIQ best practices and illustrates these best practices within this real world context of healthcare.

Depicting Data Quality Issues in Business Intelligence Environment Through a Metadata Framework

2019 ◽  
pp. 291-304
Author(s):  
Te-Wei Wang ◽  
Yuriy Verbitskiy ◽  
William Yeoh
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Business Intelligence ◽  
Quality Data ◽  
Data Quality Management ◽  
Metadata Model ◽  
Modern Business ◽  
Quality Framework ◽  
Promising Solution ◽  
Quality Dimensions

Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize the analysis by proposing a BI data quality framework.

Data Quality for Data Mining in Business Intelligence Applications

2015 ◽  
pp. 34-59
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Data Mining ◽  
Data Quality ◽  
Business Intelligence ◽  
Objective Measures ◽  
Future Research ◽  
Subjective Measures ◽  
Research And Practice ◽  
Modeling Approaches ◽  
Mining Algorithms

Data Quality (DQ) in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in Business Intelligence (BI) applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI System has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of DQ definition and measurement for data mining for BI, analyzes the gaps therein, besides reviewing proposed solutions and providing a direction for future research and practice in this area.

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