Data quality management using business process modeling

Abstract

A business process modeling framework is used for data quality analysis. The modeling framework represents the sources of transactions entering the information processing system, the various tasks within the process that manipulate or transform these transactions, and the data repositories in which the transactions are stored or aggregated. A subset of these tasks is associated as the potential error introduction sources, and the rate and magnitude of various error classes at each such task are probabilistically modeled. This model can be used to predict how changes in transactions volumes and business processes impact data quality at the aggregate level in the data repositories. The model can also account for the presence of error correcting controls and assess how the placement and effectiveness of these controls alter the propagation and aggregation of errors. Optimization techniques are used for the placement of error correcting controls that meet target quality requirements while minimizing the cost of operating these controls. This analysis also contributes to the development of business “dashboards” that allow decision-makers to monitor and react to key performance indicators (KPIs) based on aggregation of the transactions being processed. Data quality estimation in real time provides the accuracy of these KPIs (in terms of the probability that a KPI is above or below a given value), which may condition the action undertaken by the decision-maker.

Description

BACKGROUND OF THE INVENTION [0001] 1. Field of the Invention [0002] 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. [0003] 2. Background Description [0004] 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...

AI Technical Summary

Benefits of technology

[0020] Changes in the relative volume of transactions arriving from different input sources. For example, a small but fast-growing business unit alters the mix of sales transactions over time and therefore impacts the overall quality of sales data.

[0023] This invention provides the modeling and analysis for predicting how these changes impact data quality. Then, on the basis of this predictive ability, optimization techniques are used for the placement of error correcting controls that meet target quality requirements while minimizing the cost of operating these controls. This analysis also contributes to the development of business “dashboards” that allow decision-makers to monitor and react to key performance indicators (KPIs) based on aggregation of the transactions being processed. Data quality estimation in real time provides the accuracy of these KPIs (in terms of the probability that a KPI is above or below a given value), which may condition the action undertaken by the decision-maker.

Problems solved by technology

For example, a small but fast-growing business unit alters the mix of sales transactions over time and therefore impacts the overall quality of sales data.

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