Computing, Information Systems & Development Informatics Vol. 4 No. 1 March, 2013 PREPRINT – CISDI Vol 4 No 1 – 2013 57 A FRAMEWORK FOR DATA QUALITY MANAGEMENT IN NIGERIAN HIGHER INSTITUTIONS Egbokhare, F.A., Akpon-Ebiyomare, D.E and Chiemeke, S.C. Department of Computer Science University of Benin Benin City, Nigeria Correspondence: fegbokhare@yahoo.com Abstract This empirical research developed a questionnaire instrument as a tool for gathering data targeted at identifying critical success factors of data quality in tertiary institutions Information Systems databases (based on reviewed literature and findings reported in Akpon-Ebiyomare et al (2012)). Factors motivating the study include stakeholder perceived data quality success factors in Nogerian tertiary Institution databases. Based on the findings from data gathered from the research, a generalized framework for Data quality management in Nigerian Higher Institutions is proposed. The framework provides evaluation and improvement components that can be used to interact with the other components to ensure data integrity and hence quality data success at all times. Keywords: Framework, Data Management, Critical Factors, Integrity, Evaluation and Nigerian Higher Institutions. 1. INTRODUCTION/BACKGROUND OF STUDY Data is one of the most critical assets of any organization because the quality of data has a strong influence on the decision making processes. While wrong organizational decisions may not all be 100% attributed to data quality issues, Strong et al (1997) noted that the percentage contributed by poor data quality is quite high. As data quality awareness and requirements increased, researchers began to focus on data quality frameworks (Tayi andBallou,2008; Wang et al. 2006); data quality assessment (Wand and Wang 2006; English 2009); data quality management (Wang 2009; Fletcher, 2004) and data quality dimensions (Wang et al 2006; Pipino 2012). Several factors influence the quality of data in organizations (Akpon-Ebiyomare et al, 2012; Redman, 2006; Tayi and Ballou, 2008). Ballou et al 2002 identified four dimensions that are most pertinent to data values: Accuracy, Timeliness, Consistency and Completeness. Olson (2003) and Wang et al(2006)view accuracy and correctness as the most important dimensions because if data is not accurate, then the other dimensions are of little importance. Other approaches for assessing quality of data attempted to manage data in terms of definition, content and presentation, (English 2009). Poor data quality can have adverse effects on organizations, for example Olson (2003) reported that Poor data quality management costs more than $1.4 billion annually in 599 surveyed companies and up to 88% of data-related projects fail, largely due to issues with Data Quality. Wang et al (2001) discovered that 70% of manufacturing orders are assessed as being of poor data quality while Data Quality issues accounted for nearly $600M losses for US companies in 2001. Redman (2006)estimated that poor data quality results in 8% to 12% loss of revenue in a typical enterprise, and informally estimated poor data quality to be responsible for 40% to 60% of expenses in service organizations. Strong et al, (2004) observed that between 50% and 80% of computerized U.S. criminal records are estimated to be inaccurate, incomplete and ambiguous. Because of the imperfect nature of data therefore, the need for organizations to design frameworks for continuous improvement of data quality cannot be over emphasized. Wang (2009) proposed frameworks for the assessment of data quality. Wang’s framework takes into account the fact that there are different types of data and different consumers and users. The framework also recognizes that data is used for different applications. As such, the needs and quality requirements are different for the different data customers and applications. Akpon-Ebiyomare et al (2012) studied critical success factors influencing data quality in Nigerian higher Institutions. The research used the University of Benin as a single case study and obtained twenty-one (21) data quality factors out of which thirteen (13) were rated critical. The twenty-one factors obtained were exposed to other stakeholders/custodians of data at the International Conference of the Nigerian Computer Society at Abuja, Nigeria in 2011. The result obtained is used to propose a framework for data quality management in Higher Institutions in Nigeria. 2. MATERIALS AND METHOD In order to identify critical success factors of data quality in tertiary institutions IS database, a survey instrument was developed. The instrument (questionnaire) was developed based on reviewed literature and the findings reported in Akpon-Ebiyomare et al (2012), with specific reference to Tertiary Institutions Information Systems. The questionnaire was fine-tuned to effectively address all the aspects of stakeholder perceived data quality success factors.