Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2000-07-17
2003-07-15
Alam, Shahid (Department: 2172)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C707S793000, C705S004000, C705S014270
Reexamination Certificate
active
06594668
ABSTRACT:
BACKGROUND OF THE INVENTION
Field of the Invention
The present invention, in general relates to processes that normalize data and, more particularly, to systems that include a data base and software to calculate a percentile (i.e. a norm) that indicates job performance level (i.e., competency) of a given person's file as compared with all other files in the data base.
There are countless situations that can benefit from the use of normalized data. By normalized data it is meant providing a percentile that indicates how a person, having data stored in a data base, compares with other people having data stored in the data base. This is discussed in greater detail hereinafter.
Usually, normalized data appertains to a measure of job performance or ability, often referred to as a competency. There is virtually no realm of human skills, preferences, and vocations that would not benefit from the availability of such data, as is also explained in greater detail hereinafter.
However, providing normalized data (i.e., norms) has been difficult to achieve heretobefore for a number of reasons. First, it is necessary to collect the data and to establish a file for each competency category that is being measured for each person for whom their performance is to be normed.
Second, it has been necessary to perform rather complex mathematical operations, including calculus, on the data to provide a norm. To avoid using calculus, an intermediate value, known as a “Z score”, can be calculated.
The Z score is then used to refer to a lookup table (of Z scores) to determine the area under a curve which represents the actual normed data (i.e., percentile) for a given competency and therefore how that person's scores compare in relation to all of the other people.
These steps must be repetitively performed for each person. Usually, the people doing this are not fluent with the mathematics or the calculus that is involved and so they may tend to shun the process.
Also, as new data becomes available for any of the people in the data base (i.e., having a file), it is necessary to redo the entire process so as to determine their most current percentile. As is discussed in greater detail hereinafter, it may be advantageous to use only recent data or it may be appropriate to use long-accumulated data to determine the norm.
The norm is expressed as a percentile. That percentile is indicative of job performance (i.e., competency) for a given area that is being measured when compared with other members in the group (i.e., having a file in the database).
It simply has not been practical heretobefore to often calculate norms. Neither has it been practical to collect the data in an efficient manner.
The inability to collect the data efficiently is compounded by the fact that the people themselves whose competencies are being measured may be scattered geographically.
For example, a firm may wish to determine the performance of its sales representatives. The sales representatives may be dispersed throughout the country or for that matter throughout the world.
Various factors may be included in the file in order to determine competency, such as the number of contacts each representative makes per month and the number of closes, (i.e., sales that result from each of those contacts). Many other factors may also be collected and deemed as useful in the norming process.
If this information is sent to an authorized company representative, for example an expert in human resources, there will be a delay in its acquisition. Accordingly, decisions based on the results of that data will of necessity be delayed until the data has been both collected and normed. As mentioned above, the norming itself is a cumbersome, slow process.
It is desirable to collect this information in as close to real-time as is possible and to do so in as cost-effective a manner as possible.
As mentioned above, many firms do not have the expertise “in-house” to even properly utilize this data (i.e., an ability to calculate the normed percentiles), nor do they have the means such as the necessary data base and software, none of which has been available heretobefore for a number of reasons.
As the mathematics involved is complex, it takes time to perform the necessary operations. There has been no way heretobefore to collect the data or to conveniently process the collected data (also known as raw data) to obtain the normed values.
One significant reason contributing to a lack of solution is that such software algorithms would, of necessity, be slow as they performed the complex mathematical operations. Therefore, it is desirable to be able to provide a quicker approach that can be used to normalize data. It simply has not been feasible previously.
If it were desired to access this data and initiate a calculation of the norms remotely, such as over the Internet, the slow speed could make such a system intolerable.
Ideally, if this data could be captured by a secure system connected to either the Internet or to an Intranet (i.e., an in-house computer system having remote access capabilities), then a means would be provided to capture the data in, or near, real-time.
Ideally, if this system included software that overcame the problems of slow calculations (which is compounded by the number of data points to be normed and the modem transfer rate), then an optimum system would be provided.
It is also important to note that access to such a system would likely be available for use on a fee schedule. That fee schedule could be based on a measurement of time that the database and system is accessed or it could be based on some other fee structure, such as the size of the database, or on a monthly fee. Obviously, a faster processing time will be of benefit to all concerned and would provide a more cost-effective solution.
Accordingly, subscribers could access the data and request normalized data for any of the files. If this could be done easily and as often as desired, then important decisions could be made quickly and efficiently. This would save a company a great deal of money. This is discussed in greater detail hereinafter.
In the above example, if one of the sales-persons was performing at a very low rate, closing a totally unacceptable rate of contacts and if that person had other poor parameters, knowing about this as soon as possible would be of great benefit to the company in making a decision to remove that person from a position which he is not well suited for and that was likely alienating many potential customers.
A normed percentile makes his performance or lack thereof obvious to those in a position to decide. This level of confidence is not available by examination of the “raw scores”, as is discussed in greater detail hereinafter.
Conversely, a person performing at a very high level should be soon rewarded lest he leave the firm for not being appropriately valued.
Some of the benefits the use of norms (i.e., normalized percentiles) can provide are as follows:
1. Norms provide a measure of competency based on the performance of any person relative to all of the members that are being compared.
2. Norms establish performance standards for the various groups (i.e., job classes).
3. Norms are useful in determining those members having exceptional ability in any group. This is useful to determine, among other things, which individuals are likely to become effective leaders or mentors.
4. Norms automate the evaluation and reporting of performance. This is a very difficult, time consuming, and subjective arena for most organizations.
5. Norms track and therefore help to determine the efficacy of programs that are designed to improve performance.
6. Norms provide valid, reliable data upon which human resource decisions are made. These include, among others, decisions that relate to hiring, firing, salary adjustments, bonuses, promotions, demotions, lateral changes in job assignments, etc.
7. Norms provide legally defensible data upon which these decisions are made. For example, a decision to terminate an employee can often subject an employe
Alam Shahid
Rinne, Jr. Risto A.
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