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  Data quality management using business process modelingThe Patent Description & Claims data below is from USPTO Patent Application 20080195440. Brief Patent Description - Full Patent Description - Patent Application Claims
BACKGROUND OF THE INVENTION
1. Field of the Invention The present application generally relates to modeling and quantitative analysis techniques for managing the quality of data and, more particularly, to extending a business process model with constructs to identify the sources data whose quality is of interest, the data transformative tasks where error may be introduced, the error detection and correction controls in the process, and the data repositories whose quality is to be assessed. 2. Background Description As companies increasingly adopt information systems that cover a range of functional areas, they have electronic access to vast amounts of transactional data. Increasingly companies are looking develop dashboards where a variety of key performance indicators that are composed from the transactional data are displayed to assist to business decisions. The quality of data contained in these enterprise information systems has important consequences, both from the internal perspective of making business decisions based on the data as well as the legal obligation to provide accurate reporting to external agencies and stakeholders. As a result, companies spend considerable time and money to assess and improve the quality of data in the transactions that flow through its information systems and are stored in its repositories. A considerable body of literature exists on the issue of data quality assessment from the perspective of auditing a given information processing system. The prior work on data quality management comes from the fields of financial accounting and auditing and information systems. Data quality and control assessment has been studied in accounting literature since the early 1970s. Most of the studies have approached reliability assessment with the accounting system viewed as a “black box” that transforms data into aggregations of account balances contained in various ledgers (see, for example, W. R. Knechel, “The use of Quantitative Models in the Review and Evaluation of Internal Control: A Survey and Review”, Journal of Accounting Literature, (Vol. 2), Spring 1983:205-219). This approach works well from the perspective of an auditor who is interested in assessing the reliability with which the black box performs the data transformations. We review this literature to make note of the key concepts, definitions, and analyses that we adopt and extend in order to develop data quality modeling and analysis techniques at the detailed level of the transformational tasks and processes that are contained within the accounting system. B. E. Cushing in “A Mathematic Approach of the Analysis and Design of Internal Control Systems” in The Accounting Review 1974, pp. 24-41, developed a mathematic formulation for measuring the reliability for an accounting system. He used the probability that the system makes no errors of any kind in its outputs as the system reliability measure. He also derived a cost measurement by taking into consideration of the cost of executing error correction controls and the risk of undetected errors in the system. It is useful in the sense of evaluating the reliability assessment of a given system. However, Cushing's control model takes the system structure as given; it does not address any problem from the system design perspective. We apply the same basic concepts of reliability and cost measurement to the problems of evaluating system reliability for a detailed process model and to design the optimal set of corrective controls with the objective of cost minimization. S. S. Hamlen in “A Chance-Constrained Mix Integer Programming Model for Internal Control Systems”. The Accounting Review 1980, pp. 578-593, proposed a mixed integer programming model for designing an internal control system. Her model minimizes the cost of controls subject to a given percentage of quality improvement desired in the output from the system. In order to formulate a linear program, the model imposes instrumental polynomial terms with their respective constraints which have the drawback of growing exponentially with the number of terms. The accounting system is modeled as a set of controls that can correct a set of error types (which could be errors in various ledgers). We extend Hamlen's approach to a more detailed model that identifies error sources within the business process of the accounting system and controls that may be selectively applied to these error sources. Our model also allows us to assess the effect of applying a control to an error source on the resulting probability of errors at all the ledgers that are linked to that error source. This leads to greater flexibility in selecting controls to apply with the potential of better solutions. We also show how our optimization problem formulation, though more detailed than Hamlen's, can be reduced to a non-exponential series of knapsack problems without having to convert a non-linear system into a linear one. Other research in accounting literature focused on probabilistic modeling and quantitative assessment of accounting information system reliability. These studies have focused at the accounting system level modeling of reliability assessment using probabilistic or deterministic methods. They treat the transactions streams and transformative processes within the accounting information systems as a black box. Recent studies have begun to develop more detailed models for the assessment of accounting system reliability. R. B. Lea, S. J. Adams, and R. F. Boykin in “Modeling of the audit risk assessment process at the assertion level within an account balance”, Auditing: A Journal of Practice & Theory 1992 (Vol. 11, Supplement): 152-179, discussed the audit risk assessment models at different levels of detail within accounting systems. They model how risks of error at the level of the various transaction streams are related to the risk of error at the account balance level to which they contribute. They note that the level of tolerable error at the transaction stream level cannot be assumed to be the same as that for the account balance level. Their risk model covers both inherent risk (in the absence of internal controls) and control risk. We follow their motivation to decompose an account balance to its constituent transaction streams but extend their purely additive model to include (a) the volume of transactions in the various streams and (b) the probabilistic network structure of these transaction streams, identifying the various sources of errors (as represented by a process model). This allows us to overcome the assumption made by their model that the errors in the various transaction streams are independent. R. Nado, M. Chams, J. Delisio, and W. Hamscher in “Comet: An Application of Model-Based Reasoning to Accounting Systems”, Proceedings of the Eighth Innovative Applications of Artificial Intelligence Conference AAAI Press (1996) pp. 1482-1490, developed a process model based reasoning system, which they called “Comet”, for analyzing the effectiveness of controls. This is one of the earliest attempts to decompose the accounting system structure into the level of tasks that process transactions and implement internal controls. They modeled accounting systems as a hierarchically structured graph with nodes representing the transaction processing activities and collection points. The potential for failure in each activity is propagated to the collection points that are the accounts being audited. Controls are modeled in terms of the probability that they will not cover the failures. This model can be used to select the key set of controls that reduce the risk of failure below a threshold. However, the paper does not clarify the quantitative model (if any) that is used. It models only the probability of failures but ignores the magnitude of error in these failures. It also implicitly assumes identical and fixed costs for all controls. Our model adopts the basic process modeling concepts introduced in this paper and extends them to develop the quantitative framework described hereinafter. This enables the performance of rigorous quantitative analysis including Monte Carlo simulation of inherent and control risk and optimization of control usage based on risk and cost. Research on data quality in the information systems literature has focused on identifying the important characteristics that define the quality of data (see, for example, Y. Wand and R. Y. Wang, “Anchoring data quality dimensions in ontological foundations”, Communications of the ACM (39:11) (1996), pp. 86-95, and R. Y. Wang, “A Product Prospective on Total Data Quality Management”, Communications of the ACM, (41:2) (1998), pp. 58-65). Recently, the management of data quality and the quality of associated data management processes has been identified as a critical issue (see D. Ballou, R. Wang, H. Pazer, and G. Tayi, “Modeling Information Manufacturing Systems to Determine Information Product Quality”, Management Science (44:4), April, 1998, pp. 462-484). However, most of the papers describe the criteria for the information systems design to improve or achieve good data quality (DQ) or information quality (IQ). To our knowledge, none of the papers have tackled data quality management from the point of view quantitative reliability assessment and optimization, nor did they bring the costs of quality and quality improvement into the DQ or IQ assessment consideration. We consider these issues to be critical from the practical perspective of design and management of enterprise information systems. Wand and Wang, supra, are amongst the first who studied the data quality in the context of information systems design. They suggested rigorous definitions of data quality dimensions by anchoring them in ontological foundations and showed that such dimensions can provide guidance to systems designers on data quality issues. They developed a set of Ontological Concepts, and defined Design Deficiencies and Data Quality Dimensions. Then they presented the analysis of Dimensions and the Implications to Information Systems Design. Wang, supra, and Ballou et al., supra, developed the Total Data Quality Management methodology (TDQM). TDQM consists of the concepts and the principles of information quality (IQ) and the information product (IP), and procedures of information management system (IMS) for defining, measuring, analyzing, and improving information products. L. L. Pipino, Y. W. Lee, and R. Y. Wang, in “Data Quality Assessment”, Communications of the ACM, (45:4), (2002), pp. 211-218, introduced three functional forms of data quality: simple ratio, min or max operators, and weighted average. Based on these functional forms, they developed the illustrative metrics for important data quality dimensions. Finally, they presented an approach that combines the subjective and objective assessments of data quality, and demonstrated how the approach can be used effectively in practice. H. Xu in “Managing accounting information quality: an Australian study”, Managing Accounting Information Quality, (2000), pp. 628-634, developed and tested a model that identifies the critical success factors (CSF) influencing data quality in accounting information systems. He first proposed a list of factors influencing the data quality of AIS from the literature, and then conducted pilot case studies, using the findings from the pilot study together with the literature to identify possible critical success factors for data quality of accounting information systems. He did case studies of accounting information quality in Australian organizations in practice to test and customize the initial research model and compared similarities and differences between proposed critical success factors with real-world critical success factors. E. M. Pierce in “Assessing Data Quality with Control Matrices”, Comminations of the ACM, (47:2), (2004), pp. 82-86, developed a technique for information quality management based on the practice from auditing field: an information product control matrix, to evaluate the reliability of an information product. Pierce defined the components of the matrix, and presented a way to link the data problems to the quality controls that should detect and correct these data problems during the information manufacturing process. D. Strong, Y. W. Lee, and R. Wang in “Data Quality in Context”, Communications of the ACM, (40:5), (1997), pp. 58-65, propose a data-consumer perspective for data assessments as opposed to the traditional intrinsic DQ assessment. They presented a set of DQ dimensions that consists of not only the Intrinsic DQ, but Accessibility DQ, Contextual DQ and Representational DQ. The latter three concern about the user-task context. They argued that data quality assessment should incorporate the task context of users and the processes by which users' access and manipulate data to meet their task requirements. Adopted from Strong et al.'s idea, C. Cappiello, C. Francalanci, and B. Pernici in “Data quality assessment from the user's perspective”, International Workshop on Information Quality in Information Systems, 2004, proposed a data quality assessment model that takes into consideration user requirements in the assessment phase. In their mathematical formulation, parameters and matrices to capture the user and user class's preference and requirement are introduced. Their model showed how data quality assessment should take into account how user requirements vary with the accessed service. SUMMARY OF THE INVENTIONOur invention addresses the issue of data quality management from the perspectives of the owner or the consumer of the information processing system and predicting and managing the quality of its data when faced with anticipated changes in the business environment in which the system operates. Such changes could include:
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