<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=586251215043677&amp;ev=PageView&amp;noscript=1">



Goal Setting: Accuracy, Speed, Exceptions, and Expectations

With all the tools mentioned, an organization can now estimate the levels of automation it can expect to achieve with data capture technology. In this estimation, an organization should include detailed answers to the five categories below for determining success.

1. Desired Accuracy Range

Organizations should be realistic in the accuracy range they expect. The range should have as a mid-point the estimated accuracy achievable on the subject document types. The reason this should be the mid-point is because this is the organization’s biased expectation, and the real accuracy will generally be higher or lower. Based on our experience with this type of technology, the estimate might be off and organizations should be prepared to make adjustments. The calculation of the range is based on the monthly page volume of documents, and the accuracy it will take to automate the percentage needed to reduce data entry cost.

2. Exception Documents

Organizations have been surprised that while they obtain tremendous ROI when they initially use data capture, the rate at which ROI decreases is tremendous due to poorly planned exception handling.

Exception documents are documents for which a configuration was unable to extract any, or was only able to extract minimal, usable data. Exceptions often mean additional setup and fine-tuning of a configuration. Because of this they may dramatically impact ROI, as each round of fine-tuning will result in an internal or external cost. Organizations should isolate this variable and determine an acceptable range of exceptions. Exceptions will always occur, and the range is relative to the monthly page volume, variations between document types, and the expected amount of new variations per month or year. At this time the organization must also decide the number of times any one exception must repeat before a round of fine-tuning is considered. It is not advisable to fine-tune for a class of exceptions that occur once or even five times. It helps organizations to list an acceptable cost range for working with an exception document type, and to step into the calculation of the number of fine-tuning rounds permitted in a given time period. Organizations have been surprised that while they obtain tremendous ROI when they initially use data capture, the rate at which ROI decreases is tremendous due to poorly planned exception handling.

3. Technical Ability

Organizations should be aware of the technical ability of the staff appointed to configure and use the data capture system. Usually, organizations do not have to consider the operating complexity of the data capture product, as software companies design data capture products to be simple to operate once they are configured. What varies more is the setup of a system. The complexity range that is acceptable is determined by the skill set of the staff designated to set up and support the data capture system. Some packages offer “what you see is what you get” or WYSIWYG type setup that only requires personnel who are familiar with basic Windows application usage. However some packages require a developer-level of expertise. Most packages have both, and developer assistance is only needed as one-off support. Setup complexity is important during the initial integration, though this may be handled by the vendor and during exception handling fine-tuning. The complexity of a product is not necessarily indicative of its accuracy.

4. Processing Speed

There are several stages in data capture processing where speed is an important consideration. In regards to this type of technology, a slower speed usually correlates to more accuracy. Organizations need to make the decision of what speed of processing, setup, and exception setup is acceptable for their business process. Speed of processing is determined by measuring how long manual entry takes per page. Documents entered using data capture should take less time to enter than if they were entered manually. For some organizations, entry in the same amount of time is acceptable; for most, it is between thirty to two hundred percent less time per page. Expectations should be realistic and based highly on the complexity of the documents. Often, organizations focusing only on speed will pick faster, less accurate technology, and will not obtain an ROI as there will be more manual quality assurance checking.

5. Setup Time

The amount of time it takes to set up and train for expectations impacts the time it will take to start gaining value from automation. The more time it takes to set up, the longer it takes to start automating documents. However, generally, the more setup time spent, the more accurate the system will be. Organizations need to know the range of time for initial setup that is acceptable. In data capture, the average is between 3 to 6 months, with outliers on either side for initial setup. The average time per exception document type can range from minutes to weeks.