Developing an optimization model for two-operator data entry

Firms sometimes use two-operator data entry as a method to achieve or maintain data­ base quality. When in-house staff are used, the firm typically selects data entry operators from a pool of junior staff and then assigns them into operator pairs, often on a random basis. Keying discrepan cies between operator pairs are compared to determine incorrect entries, in the same row and column. Because the likelihood of making an error on a given key varies among opera­ tors, the objective of this study was to optimize database quality by systematically matching operators. The model was developed by having 32 operators key data into two databases and monitoring the location of each operator error. The database quality of all operator combina­ tions were compared to determine optimal operator pairings. This resulted in 319% fewer er­ rors in a second database over the expected number of errors which would have occurred from random operator pairings, and produced a database that was nearly 99.95% error-free. Re­ gression analysis used operator error rates and total number of errors from each operator pair as independent variables. The dependent variable was the number of errors committed by each operator pair in the second database. The model explained 69% of the variability, and was used in a subsequent study using 28 different operators who entered a second database which was different from the first study. This resulted in a 0.85 correlation between the predicted versus actual in the second group. INTRODUCTION As firms become increasingly dependent on their Management Information Systems, their need for higher quality databases also increases. This necessity is a result of ongoing trends in the data processing field. One trend pertains to the fact that a given piece of information in a database is now more likely to be used by several subunits within the organization. For example, a Manufacturing Resources Planning 11 system coordinates sales, purchasing, manufacturing, finaiace, and engineering by adopting a central manufacturing plan and uses a single unified database to plan and update the activities in these systems (Adam & Ebert, 1992). Another trend pertains to liability issues. To illustrate, the Texas Court of Appeals recently held an oil pipeline company liable for negligently causing the delivery of 93,000 barrels of crude oil to the wrong consignee. The pipeline company was found liable for 50% of the damages for negligence, which arose from a data entry error (Westermeier, 1993). Because of these factors firms might be encouraged to develop a zero defects program for data entry. 51 1 Nibler: Developing an optimization model for two-operator data entry Published by CSUSB ScholarWorks, 1995 Journal of International Information Management Volume 4, Number 1 During the past ten to fifteen years a number of changes have taken place that affect the data entry process of many companies. One change is distributed data processing, stemming from the development of PCs and Local Area Networks. This has been an important factor resulting in data being entered in a decentralized fashion as opposed to centralized systems using professional "Heads-down" data entry operators. Although this trend places data entry close to the source and therefore improves the opportunity for on-line validity checks, it can also result in the deterioration of database quality because data entry is often performed by a junior staff on a part-time basis in conjunction with other (and usually more interesting) duties. More­ over, in a number of cases, the data is entered by only one person, as opposed to the verifier method in which a second operator enters the same database and discrepancies between the two operators are compared and corrected. Another change involves the data entry method. Recently there have been significant de­ velopments in source data automation such as Bar Coding, Image Recognition, Optical Charac­ ter Recognition, Voice Recognition, and Electronic Pens. Many of these methods have improved database quality under certain conditions, but they continue to have their limitations. With auto­ mated entry, the input documents must meet higher clarity standards than for keyboard data entry (Gumey, 1992; Betts, 1991; Francis, 1991). In some situations the data to be entered must pass certain validity checks, which can only be done on a cost-effective basis by someone who is familiar with the data. In any event, the ultimate data source for most automated entry contin­ ues to be the human aided by mechanical or electronic devices. To this extent, automated data entry usually cannot be more accurate than the person who initially captured the data. To raise or maintain the quality of critical databases, some companies should consider establishing minimum error standards for each database and then developing appropriate con­ trol measures. However, data entry is expensive and accounts for nearly 20% of the data pro­ cessing costs in many firms (Bodeck, 1988; Rhodes, 1987). The challenge is to locate qualified staff to perform data entry, particularly in situations where data entry is spread throughout the functional areas of the firm and in dispersed geographical locations. A possible alternative is to use a professional data entry firm, especially one located offshore where labor is relatively inexpensive and savings of 25% 50% can be obtained (O'Connor, 1992; Nurse, 1992; Anthes, 1991). Unfortunately, unless costly and sophisticated telecommunications equipment is utilized, long response times, loss of validity checks, and data confidentiality may negate cost savings. Since some firms wish to achieve and/or maintain database quality at professional data entry standards, which are often at the 99.95% level or higher, two-operator data entry using in-house staff may be the most cost effective alternative.