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Crystal Reports Administration: Data Parameter Validation

Parameter edit masks can control data input.

Usually, we leave parameters for the user to enter the required values. They will quickly learn to enter the date in an unusual format, or discover you wanted them to select the customer code rather than the full name.

But sometimes we might need to help them a little bit with the parameter entry as well.

At one site, there was a risk of entering product codes in lower case. So we used the “Edit Mask” feature to force the input to UPPER case.

Edit masks are specified on the “Default Values” screen and we used a mask of
>LLLLLLLL

The “>” forces the input into UPPER case and the “L” indicates an optional alphanumeric value.

There is a range of edit mask control characters available. Use them if you want to restrict user input.

And don't forget the relevancy issues. While not really an accuracy concern, data of low relevance can have a negative impact on the end results.

Administering and controlling such data uses up resources. Not just system resources, but also the resources of the people who are using the reporting system.

Consider the example of a marketing company that was sending out surveys to shoppers. These surveys were complex and required about 5 hours per week per shopper to fill out. People got sick of this, and--just to get through the survey--entered wrong information.

When a new director came in, she took one look at the survey and said, "This is insane!" She asked her bosses what it was they most wanted to know, and told them they had to limit that to what could be obtained in 15 minutes per week from a shopper. The new survey, sent to the same people, show markedly different responses to the same data requests. The functional concept here: "Overload doesn't work."

Some thoughts on data accuracy

In recent years, there have been several high profile scandals involving advocacy groups that have published false statistics or other false data. In many cases, this was deliberate. In other cases, the problem was those using the data simply did not vet the data because what they were looking at supported their existing notions and agenda.

The first thing to consider is the source. Are the data from a reliable source? An accurate historical book, for example, will draw mostly on primary sources--original correspondence, for example. An inaccurate one will draw on newspaper accounts (notoriously inaccurate), which are tertiary sources at best.

In your own organization, where are your data coming from? If the sources have a vested interest in a particular outcome, your data are probably biased. So look at who is providing your data and how your data are collected. You want to eliminate bias and error at the source and also eliminate dubious sources.

Next, look at the data collection method. Consider the case of the typical hotel survey that asks, "How are we doing?" You get a few multiple choice questions, with the answers being "Outstanding, excellent, very good, good, average, poor." Do you see the problem there? How can you graph the results in any meaningful way? You can't; it will be skewed beyond any reliability and thus any value.

A third aspect to consider is whether you are getting the relevant data and the right data. Avoid the situation that's analogous to the guy who's looking for his lost nickel under the street lamp because the light's better there than 50 feet away where he actually dropped the coin. Adding irrelevant data leads to drawing irrelevant conclusions.

Finally, ensure you aren't asking for too much data. A common problem in plant maintenance organizations is the techs are burdened with filling out huge reports while doing preventive maintenance. They just check everything off as OK and then look at the equipment based on their experience and without regard to the checklist. This defeats the purpose of data collection. It's better to get a small but reliable sample of data than a huge but unreliable sample. You may have to focus on the top few things that matter, rather than everything that could possibly matter.