Journal of Modern Accounting and Auditing, ISSN 1548-6583 February 2012, Vol. 8, No. 2, 185-194 D DAVID PUBLISHING A Framework for Identifying and Managing Information Quality Metrics of Corporate Performance Management System Kunlaya Pattanagul Khon Kaen University, Thailand Wachara Chantatub Chulalongkorn University, Thailand Wasu Chaopanon Khon Kaen University, Thailand Corporate Performance Management (CPM) system is an information system used to collect, analyze, and visualize key performance indicators (KPIs) to support both business operations and especially strategic decisions. CPM systems display KPIs in forms of scorecard and dashboard so the executives can keep track and evaluate corporate performance. The quality of the information as shown in the KPIs is very crucial for the executives to make the right decisions. Therefore, it is important that the executives must be able to retrieve not only the KPIs but also the quality of those KPIs before using such KPIs in their strategic decisions. The objectives of this study were to determine the role of the CPM system in the organizations, current data and information quality state, problems and perspectives regarding data quality, as well as data quality maturity stage of the organizations. Survey research was used in this study; a questionnaire was sent to collect data from 477 corporations listed in the Stock Exchange of Thailand (SET) on January, 2011. Forty-nine questionnaires were returned. The results show that about half of the organizations have implemented CPM systems. Most organizations are confident in the information in CPM system, but information quality issues are commonly found. Frequent problems regarding information quality are information not up to date, information not ready by time of use, inaccuracy and incomplete. The most concerned and frequently assessed quality dimensions were security, accuracy, completeness, and validity. When asked to prioritize, the most important quality dimensions are accuracy, timeliness, completeness, security, and validity respectively. In addition, most organizations concern about data governance management and have deployed such measures. This study showed that most organizations are on level 4 on Gartner s data governance maturity stage in which data governance is concerned and managed, but still not effective. Keywords: data quality, corporate performance management (CPM) system, data quality metrics, key performance indicators (KPIs), data maturity and management Introduction Modern organization management requires data and information for strategic planning and target assignment. Not only key performance indicators (KPIs) are used to measure and monitor organizations performance, they are also required in benchmarking against targets. In addition, KPIs represent competency of the organization, thus they are important for the executives in strategic and operational planning. Since data and information are valuable assets, it is important that organizations pay attention in data Kunlaya Pattanagul, Ph.D. student, Information Studies Program, Faculty of Humanities and Social Science, Khon Kaen University. Wachara Chantatub, lecturer, Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University. Wasu Chaopanon, lecturer, Department of Computer Engineering, Faculty of Engineering, Khon Kaen University.
186 IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS management. Data management consists of 10 steps including: (1) data governance; (2) data architecture management; (3) data development; (4) data operations management; (5) data security management; (6) reference and master data management; (7) data warehousing and business intelligence management; (8) document and content management; (9) meta-data management; and (10) data quality management (Mosley, Brackett, Earley, & Henderson, 2009). Because of data and information overload, organizations require effective data management in order to increase competitiveness and excellence. Business intelligence (BI) enables organizations to collect and analyze data which is then turned into useful information used for making decision (Golfarelli, Rizzi, & Cella, 2004). Information system is an essential tool to collect, analyze and present important information for organization management in both strategic and operational levels. Data and information from an information system can be used for management, increase effectiveness as well as retain customers. Corporate Performance Management (CPM) system is part of the emerging BI trends which increases value and performance of the organization (Watson & Wixom, 2007). CPM is an information system used to monitor the corporate performance and present KPIs to top-level executives in order to aid strategic and operation decisions (Bose, 2006). To monitor corporate performance, the organizations need to identify effective KPIs which not only conform and reflect current performance, but also be able to benchmark against corporate goals. CPM system, therefore, is an important tool for corporate executives. KPIs presented in the CPM system are processed from data collected within and outside organization. Data quality, therefore, is an important factor to be emphasized. One key problem of the CPM system is data quality issue. Lack of data quality leads directly to insufficient information quality and may lead to wrong decision (Becker, Pöppelbuß, Glörfeld, & Bruhns, 2009). In addition, poor data results in negative consequences on the operational, tactical, and strategic organizational levels (Hassan, 2003). Data quality dimensions can be both objective and subjective, and can have different definition depends on perspective and usage (Shankaranarayanan & Cai, 2006). For example, data quality means completeness, accuracy, consistency and timeliness of data (Scarisbrick-Hauser & Rouse, 2007). In addition, data quality may depend on type and nature of data. Data quality dimension can be divided into four groups including quality dimensions describing the quality of management of the data such as accessibility, ease of maintenance and reliability; quality dimension describing the quality of the representation of the data such as conformance to schema, appropriate presentation and clarity; intrinsic data quality dimensions such as accuracy, uniqueness and consistency; and relative data quality dimensions such as user preferences, criticality and data source reputation. Nevertheless, four data quality dimensions have been described and commonly used, including accuracy, completeness, timeliness, and consistency (Xu, Nord, Brown, & Nord, 2002; Berti-Èquille, 2007; Batini, Cappiello, Francalanci, & Maurino, 2009). To achieve higher data quality, appropriate data governance must be implemented such as data profiling, data standardization, monitor and error correction, and error prevention (Solomon, 2005). In addition, organization must assign team or unit responsible for data quality. These components take part in improving data quality within the organization. It is, therefore, important to determine the role of the CPM system in the organizations, current data and information quality state, problems and perspectives regarding data quality as well as data quality maturity state of the organization. These results reflect current data management state and increase awareness regarding data quality issue within organizations.
IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS 187 Objectives The objective of this research was to evaluate the quality of data and information in the organizations. The perspective towards data quality in the context of the CPM system, data quality assessment and data quality maturity were also determined. Research Methodology Survey research was used in this research. The questionnaire was used for data collection. The questionnaire consists of three parts including: (1) general characteristics of the interviewees and their organization; (2) perspective towards data quality; and (3) data quality maturity stage of the organization. The questionnaire was sent to collect data from 477 corporations listed in the Stock Exchange of Thailand (SET) on January, 2011. Forty-nine questionnaires were returned. The organizations were categorized into eight groups including agriculture and food industry, natural resources, technology, financial, services, industrial goods, consumer goods, and real estate. Data collected from questionnaire was analyzed using descriptive statistics such as frequency and percentage. Results The results of this study are divided into three parts including: (1) general characteristics of respondents and their organizations; (2) perspective towards data and information quality of the organization; and (3) data governance maturity level. General Characteristics of Respondents and Their Organizations Respondents. A total of 49 questionnaires were returned. As shown in Table 1, 15 (32.65%) respondents are C-level executives such as chief executive officer (CEO), chief finance officer (CFO), chief marketing officer (CMO), chief operating officer (COO), and chief information officer (CIO), 11 (22.45%) respondents are IT manager, and nine (18.37%) respondents works in quality assurance department. The average work experience at their current positions was 7.4 years, while the average of their total work experience was 16.56 years. Table 1 Current Position of the Respondents Positions No. of respondents Percentage (%) CEO 1 2.04 CFO, CMO, COO, and CIO 15 30.61 Quality assurance manager 9 18.37 IT manager 11 22.45 Managers in other departments 7 14.29 Others 6 12.24 Total 49 100.00 Organizations. Major respondents include 14 (28.57%) organizations in the industrial goods, 11 (22.45%) organizations in the financial businesses, and eight (16.33%) organizations in the real estate. Seventeen (34.69%) organizations have more than 1,000 employees, 16 (32.65%) organizations have 101-500 employees and 15 (30.61%) have 501-1,000 employees.
188 IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS Perspective Towards Data and Information Quality of the Organizations Out of 49 respondents, 45 (91.84%) respondents work in the position which requires data and information for management and decision support. Out of these respondents, 34 (75.56%) respondents have experienced data quality issues. In addition, 35 (71.43%) organizations have internal unit(s) responsible for data and information quality management such as data owner, IT department, information development division, planning department, database unit, information system department, IT and database committee, quality assurance department, and internal audit. As described previously, CPM is the information system used to display KPIs for the executives in order to monitor the performance of the organization. From 49 organizations responded, 27 (55.10%) organizations have implemented CPM for average of 4.52 years. Most of the CPMs used in these organizations (14 organizations, 51.85%) are vendor software such as Enterprise Resource Planning (ERP) system from Systems, Applications, and Products in Data Processing (SAP), Business Connector (BC), Industrial and Financial Systems (IFS) and Coach, 10 (37.04%) organizations developed their own CPM using Java, Structured Query Language (SQL), Mypis, Excel, and VB, and three (11.11%) organizations have used a combination of proprietary commercial software and in-house development using J. D. Edwards, Report Program Generator (RPG), Activity-Based Costing/Activity-Based Management (ABC/ABM) (Oros system), SAP, Documentum, Mainstream Accounting System (MAS), Employee Self Service (ESS), SAP and Office Automation (OA) service. Out of 27 organizations which have CPM implemented, 23 (85.19%) organizations use CPM in financial/accounting, 19 (70.37%) organizations use CPM in marketing, sales and customer services, and 17 (62.96%) organizations use CPM in operation and personnel, as shown in Table 2. Overall, 19 (70.37%) organizations strongly agreed with the importance of CPM, while eight (29.63%) agreed with the importance of CPM. Table 2 Job Functions in Which CPM Is Implemented Job functions No. of respondents Percentage (%) Financial/accounting 23 85.19 Marketing 19 70.37 Sales 19 70.37 Customer service 19 70.37 Products 13 48.15 Operation 17 62.96 Personnel 17 62.96 IT 15 55.56 Others 2 7.41 Out of the organizations which CPM system was implemented, 17 (62.96%) respondents have accessed the system by themselves. When asked about the confidentiality of the data in CPM, 18 (66.67%) respondents feel confident for data in CPM system, seven (25.93%) respondents feel neither confident nor unconfident for data in CPM system, and two (7.41%) respondents feel strongly confident for data in CPM system, as shown in Table 3. Concerning the question what to do when the respondents feel unconfident with data in CPM, 24 (88.89%) respondents assign the owner or responsible unit of the KPIs to recheck the data, 19 (70.37%) respondents use unconfident data with more carefulness, five (18.52%) respondents reject the unconfident
IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS 189 data or KPIs, and two (7.41%) respondents use supplemental data with the unconfident data to make a decision, as shown in Table 4. Table 3 Level of Confidence of the Data in CPM Level of confidence No. of respondents Percentage (%) Strongly confident 2 7.40 Confident 18 66.67 Neither confident nor unconfident 7 25.93 Unconfident 0 0.00 Strongly unconfident 0 0.00 Total 27 100.00 Table 4 What to Do When the Respondents Are Not Confident With Data in CPM Methods No. of respondents Percentage (%) Use unconfident data with more carefulness 19 70.37 Assign the owner or responsible unit of the KPIs to recheck the data 24 88.89 Reject the unconfident data or KPIs 5 18.52 Others 2 7.41 For perspective towards data and information quality management in the organizations, most respondents strongly agree with: (1) information quality is important for organization management (28 respondents, 57.14%); (2) better quality information improves competitiveness and value of the organization (23 respondents, 46.94%); (3) centralized and standardized data for the whole organization increased effectiveness of the organization (26 respondents, 53.04%); and (4) information is a valuable asset of the organization, thus information quality management must be prioritized (33 respondents, 67.35%), as shown in Table 5. Table 5 Perspective Towards Data and Information Quality Management in the Organizations Issues (1) Information quality is important for organization management (2) Better information quality improves competitiveness and value of the organization (3) Centralized and standardized data increases effectiveness of organization management (4) Information is a valuable asset, it is, therefore, important to implement data and information quality management 5 (strongly agree) 4 (agree) Levels 3 (neither agree nor disagree) 2 (disagree) 1 (strongly disagree) 28 (57.14%) 19 (38.78%) 2 (4.08%) 0 (0.00%) 0 (0.00%) 23 (46.94%) 21 (42.86%) 5 (10.20%) 0 (0.00%) 0 (0.00%) 26 (53.06%) 20 (40.82%) 3 (6.12%) 0 (0.00%) 0 (0.00%) 33 (67.35%) 14 (28.57%) 2 (4.08%) 0 (0.00%) 0 (0.00%) Analysis of information quality problem in the CPM system showed that most organizations have experienced information quality issues. The most frequent problem is that the information is not up to date (46 respondents, 93.88%). Next issue is that the information is not correct because of error in information processing (43 respondents, 87.76%) as well as the information is not ready on time of request (43 respondents, 87.76%).
190 IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS Incomplete information is also another major problem (42 respondents, 85.71%), as shown in Table 6. Table 6 Data Quality Issues in the CPM Systems Data quality issues Have Do not have Information is inaccurate because of incorrect data 39 (79.59%) 10 (20.41%) Information is inaccurate because of information processing 43 (87.76%) 6 (12.24%) Information is doubtful because the source is unrespectable 32 (65.31%) 17 (34.69%) Information is incomplete because of incomplete data 42 (85.71%) 7 (14.29%) Inappropriate information security measures 37 (75.51%) 12 (24.49%) Information is unclear and not understandable 32 (65.31%) 17 (34.69%) Information is not ready on time of request 43 (87.76%) 6 (12.24%) Information is not up to date 46 (93.88%) 3 (6.12%) Information is inconsistent 41 (83.67%) 8 (16.33%) Information is insufficient for tasks 39 (79.59%) 10 (20.41%) Information is not concise 38 (77.55%) 11 (22.45%) Information is irrelevant or not relate to tasks 37 (75.51%) 12 (24.49%) Information is not useful for tasks 34 (69.39%) 15 (30.61%) Table 7 Data Quality Issue Levels in the CPM Systems Levels Data quality issues 3 (somewhat 1 (very 5 (very likely) 4 (likely) 2 (unlikely) likely) unlikely) (1) Information is inaccurate because of incorrect data 5 (12.82%) 8 (20.51%) 11 (28.21%) 15 (38.46%) 0 (0.00%) (2) Information is inaccurate because of information processing 0 (0.00%) 7 (16.28%) 10 (23.26%) 19 (44.19%) 7 (16.28%) (3) Information is doubtful because the source is unrespectable 4 (12.50%) 2 (6.25%) 10 (31.25%) 8 (25.00%) 8 (25.00%) (4) Information is incomplete because of incomplete data 2 (4.76%) 15 (35.72%) 10 (23.81%) 11 (26.19%) 4 (9.52%) (5) Inappropriate information security measures 3 (8.11%) 5 (13.51%) 8 (21.62%) 13 (35.14%) 8 (21.62%) (6) Information is unclear and not understandable 3 (9.38%) 3 (9.38%) 9 (28.13%) 12 (37.50%) 5 (15.63%) (7) Information is not ready on time of request 3 (6.98%) 10 (23.26%) 14 (32.56%) 11 (25.58%) 5 (11.63%) (8) Information is not up to date 3 (6.52%) 8 (17.39%) 12 (26.09%) 14 (30.43%) 9 (19.57%) (9) Information is inconsistent 2 (4.88%) 7 (17.07%) 7 (17.07%) 17 (41.46%) 8 (19.51%) (10) Information is insufficient for tasks 4 (10.26%) 4 (10.26%) 12 (30.77%) 13 (33.33%) 6 (15.38%) (11) Information is not concise 2 (5.26%) 6 (15.79%) 8 (21.05%) 13 (34.21%) 9 (23.68%) (12) Information is irrelevant or not relate to tasks 2 (5.41%) 3 (8.11%) 13 (35.14%) 10 (27.03%) 9 (24.32%) (13) Information is not useful for tasks 1 (2.94%) 3 (8.82%) 9 (26.47%) 10 (29.41%) 11 (32.35%) When consider each item regarding information quality problems, the results are shown in Table 7 and the key findings are as follows: (1) What most organizations reported very unlikely is information is not useful for tasks.
IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS 191 (2) What most organizations reported unlikely are information is inaccurate because of incorrect data, information is inaccurate because of information processing, inappropriate information security measures, information is unclear and not understandable, information is not up to date, information is inconsistent, information is insufficient for tasks, and information is not concise. (3) What most organizations reported somewhat likely are information is doubtful because the source is unrespectable, information is not ready on time of request, and information is irrelevant or not relate to tasks. (4) What most organizations reported likely is information is incomplete because of incomplete data. When asked about the source of information quality problem, 42 (85.71%) respondents answered that the source of information quality problem comes from data input error (staff error), 27 (55.10%) respondents answered that the source comes from data input error (customer error) and 26 (53.06%) respondents answered that the error comes from data transformation from the old system to the new system (see Table 8). Table 8 Sources of Information Quality Problem in the CPM System Sources of problems No. of respondents Percentage (%) Data input error (staff error) 42 85.71 Data input error (customer error) 27 55.10 Error from data transformation from the old system to the new system 26 53.06 Inconsistency data from different data owners 19 38.78 Error from external data 12 24.49 Error from system miscalculation 19 38.78 When asked about the benefits of having quality information, 48 (97.96%) respondents answered that quality information will increase confidence in making decision, and 30 (61.22%) respondents answered that quality information will both reduce cost and increase customer satisfaction (see Table 9). Table 9 Benefits of Having Quality Information Benefits No. of respondents Percentage (%) Increase confidence in making decision 48 97.96 One truth throughout the organization 26 53.06 Increase profit 25 51.02 Reduce cost 30 61.22 Increase customer satisfaction 30 61.22 Others 4 8.16 While most respondents (more than 90%) agreed with the importance of information quality dimensions, only some respondents reported that they have assessment method for those dimensions. The quality dimensions most assessed are security (47 respondents, 95.92%), accuracy (44 respondents, 89.80%), completeness (44 respondents, 89.80%) and validity (43 respondents, 87.76%) (see Table 10). When asked to rank the importance of information quality dimension, the highest ranked dimension is accuracy, timeliness, completeness, security and validity, respectively (see Table 11).
192 IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS Table 10 Importance and Assessment of Information Quality Dimensions Information quality dimensions Importance Assessment Important Not important Have Do not have Accuracy 48 (97.96%) 1 (2.04%) 44 (89.80%) 5 (10.20%) Consistency 46 (93.88%) 3 (6.12%) 36 (73.47%) 13 (26.53%) Completeness 49 (100.00%) 0 (0.00%) 44 (89.80%) 5 (10.20%) Timeliness 48 (97.96%) 1 (2.04%) 40 (81.63%) 9 (18.37%) Validity 49 (100.00%) 0 (0.00%) 43 (87.76%) 6 (12.24%) Accessibility 49 (100.00%) 0 (0.00%) 38 (77.55%) 11 (22.45%) Appropriate amount of data 49 (100.00%) 0 (0.00%) 33 (67.35%) 16 (32.65%) Believability 49 (100.00%) 0 (0.00%) 39 (79.59%) 10 (20.41%) Concise representation 42 (85.71%) 7 (14.29%) 23 (46.94%) 26 (53.06%) Ease of manipulation 47 (95.92%) 2 (4.08%) 23 (46.94%) 26 (53.06%) Interpretability 46 (93.88%) 3 (6.12%) 26 (53.06%) 23 (46.94%) Objectivity 49 (100.00%) 0 (0.00%) 32 (65.31%) 17 (34.69%) Relevancy 47 (95.92%) 2 (4.08%) 33 (67.35%) 16 (32.65%) Reputation 47 (95.92%) 2 (4.08%) 29 (59.18%) 20 (40.82%) Security 49 (100.00%) 0 (0.00%) 47 (95.92%) 2 (4.08%) Understandability 48 (97.96%) 1 (2.04%) 30 (61.22%) 19 (38.78%) Value-added 47 (95.92%) 2 (4.08%) 28 (57.14%) 21 (42.86%) Table 11 Ranks for the Importance of Information Quality Dimensions Information quality dimensions Rank Accuracy 1 Timeliness 2 Completeness 3 Security 4 Validity 5 Accessibility 6 Consistency 6 Objectivity 7 Believability 8 Appropriate amount of data 9 Value-added 10 Concise representation 11 Ease of manipulation 11 Relevancy 12 Interpretability 13 Reputation 14 Understandability 15 Data Governance Maturity Level Gartner s enterprise information management model enables organizations to identify what stage of maturity they have reached according to key characteristics. It also tells what actions to take to reach the next level. The respondents were asked about information management of the organization and the level of maturity was identified according to Gartner s model.
IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS 193 The results showed that most organizations (16 respondents, 32.65%) are level 4, 15 respondents (30.61%) are level 3, eight respondents (16.33%) are level 2, and eight respondents (16.33%) are level 5 (see Table 12). Table 12 Data Governance Maturity Level Maturity Level Number Percentage (%) Level 0: Unaware 0 0.00 Level 1: Aware 2 4.08 Level 2: Reactive 8 16.33 Level 3: Proactive 15 30.61 Level 4: Managed 16 32.65 Level 5: Effective 8 16.33 Discussion and Conclusions Corporate performance management system is an essential tool to support both strategic decision making and business operation in today organizations. We find that most organizations have deployed CPM system to many areas including financial, marketing, sale and customer service. Both vender and corporate-developed systems have been implemented in order to collect, analyze, and display KPIs used by C-level executives. Since information is a key component, quality of information in CPM system needs to be emphasized. The organizations viewed information as a valuable asset which is important for business management, increase competitiveness and value of the organization. In addition, the organizations agree that good information quality increases confidence in decision making, reduces cost, and improves customer satisfaction. The results agree with Elbashir, Collier, and Davern (2008) who reported that using business intelligence system for organizational performance management can increase organizational benefits, business supplier/partner relation benefits, internal processes efficiency benefits as well as customer intelligence benefits. Additional benefits are risk reduction for the company, higher staff satisfaction, and better rating for the company (Becket et al., 2009). When we asked about the confidence in the information in the system, responses are mostly positive. Furthermore, when the executive is not confidence in the information, it must be reevaluated and improved by owner or responsible unit before using for decision. While most organizations value the importance of information quality, the problem still persists. Our result agreed with Wand and Wang (1996) who reported that more than 60% of 500 medium-size corporations had problems with data quality. Time-related issues such as information is not up to date and information is not ready to be used by time of request as well as management issues such as inaccurate information due to error in information processing and incomplete information are common problems found in CPM system. These problems were also reported previously in which accuracy was rated the most frequent problem, followed by completeness, consistency and timeliness, respectively (Becker et al., 2009). Although the level of these problems is relatively small, it may affect judgment and lead to wrong decision. In addition, lack of data quality verification may lead to lack of cooperation within and between organizations. Organization may not request data from other organization if it does not trust the data. Within organization, if the data is exchanged without knowing their actual quality, it may happen that data of low quality spread all over the enterprise (Mecella et al., 2002). The main sources of information quality problem come from human error such as input error by staff and incorrect data provided by customer as well as system error such as data transformation from the old
194 IDENTIFYING AND MANAGING INFORMATION QUALITY METRICS system to the new system. In addition, most organizations have assessment measure for common information quality dimensions such as security, accuracy, completeness and validity. When we asked the organization to prioritized information quality dimensions, the most emphasized dimensions are accuracy, timeliness, completeness, security and validity, respectively. Our findings are consistent with previous reports in which information quality usually refers to accuracy, timeliness, completeness, consistency and validity (Xu et al., 2002; Piprani & Ernst, 2008; Becker et al., 2009). We also find that security is additional quality dimension in which its importance is addressed in this study. Nevertheless, the description for these dimensions is largely controversial and the assessment metrics need to be further clarified. In conclusion, while most organizations concern about information quality and data governance management, some of them are not taking the issue seriously. This could be largely due to complexity of the organization, system and operation, technical readiness as well as lack of knowledge. Most of the organizations in this study are classified as level 4 on Gartner s data governance maturity model in which is described by understanding that information is critical, policies and standards are developed and understand throughout the enterprise, governance unit is in place to resolve issues related to cross-functional information management and valuation of information assets and productivity metrics are developed. References Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41, 1-52. Becker, J., Pöppelbuß, J., Glörfeld, F., & Bruhns, P. (2009). The impact of data quality on value based management of financial institutions. Proceedings from: The 15th Americas Conference on Information Systems (pp. 1-13). San Francisco. Berti-Èquille, L. (2007). Data quality awareness: A case study for cost-optimal association rule mining. Knowledge Information System, 11, 191-215. Bose, R. (2006). Understanding management data systems for enterprise performance management. Industrial Management & Data Systems, 106, 43-59. Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting and Information Systems, 9, 135-153. Golfarelli, M., Rizzi, S., & Cella, I. (2004). Beyond data warehousing: What s next in business intelligence? Proceedings from: The 7th ACM International Workshop on Data Warehousing and OLAP (pp. 1-6). Washington DC. Hassan, B. (2003). Examining data accuracy and authenticity with leading digit frequency analysis. Industrial Management & Data Systems, 103, 121-125. Mecella, M., Scannapieco, M., Virgillito, A., Baldoni, R., Catarci, T., & Batini, C. (2002). Managing data quality in cooperative information system. Lecture Notes in Computer Science, 2519, 486-502. Mosley, M., Brackett, M., Earley, S., & Henderson, D. (2009). The DAMA guide to the data management body of knowledge. New Jersey: Technics Publications. Piprani, B., & Ernst, D. (2008). A model for data quality assessment. Lecture Notes in Computer Science, 5333, 750-759. Scarisbrick-Hauser, A., & Rouse, C. (2007). The whole truth and nothing but the truth? The role of data quality today. Direct Marketing, 1, 161-171. Shankaranarayanan, G., & Cai, Y. (2006). Supporting data quality management in decision-making. Decision Support Systems, 42, 302-317. Solomon, M. D. (2005). Ensuring a successful data warehouse initiative. Information Systems Management, 22, 26-36. Wand, Y., & Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Communication of the ACM, 39, 86-95. Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. IEEE Computer, 40, 96-99. Xu, H., Nord, J. H., Brown, N., & Nord, G. D. (2002). Data quality issues in implementing an ERP. Industrial Management & Data Systems, 102, 47-58.