The Potentials of Career Growth for Data Processing Professionals

Two professors from the University of Colorado, J. Daniel Couger and Robert Zawacki, have written a book titled Motivating and Managing Computer Personnel. They based a large portion of the book on a study of the needs of men and women in the data-processing field and how they are motivated to fill those needs in a job setting.

The study looked at three specific areas:

1. Social Need Strength (SNS).
   
   
   
   
2. Growth Need Strength (GNS).
   
   
3. Motivation Potential Score (MPS).
   

The SNS delved into how much social interaction the average DPer (Data Processing Professional) needs in order to be happy on the job. Because many in the field, or at least those who entered it early, seem to prefer to interact more with their machines than with their peers, it was expected that the score in this area would be low. But no one, including the researchers, expected it to be quite so low. In fact, people in data processing had the lowest SNS scores of six hundred professions included in the study.

At the same time, the GNS - Growth Need Strength - scores were the highest of those six hundred professions.

The MPS scores for motivational potential were generally high for DPers.

The conclusion? DP (Data Processing) people want the potentials of career growth and most likely see their chosen field as being able to provide it.

But here's where that SNS - Social Need Strength - rears its ugly head, at least from my perspective as someone deeply involved in the world of career achievement and success. The tendency to shun inter-personal relations on the job, and to depend upon introspective interaction with the technology in front of you, is counterproductive to achieving success beyond the computer.

Let me quickly say that I am not being critical of those who prefer to avoid the broader scope of daily business life. Every industry needs people who are content to do that, and who achieve satisfaction and rewards from focusing on their technical tasks. In fact, those working in data processing bring to the workplace an interesting and challenging new set of needs for management to grapple with in the coming years. People involved in technically innovative jobs don't respond as readily as nontechnical managers to such incentives as raises, security, hours, job titles, and location, according to various studies. The opportunity to learn new skills and to be left to function freely on interesting projects ranks higher.

For those of you who are looking for advancement within the technical scope of data processing, the rules for success differ somewhat from a quest for promotions to more general, nontechnical management positions. The differences are not, however, as profound as the similarities.

Most people in every walk of life have difficulty taking stock of themselves, particularly when it involves acknowledging weaknesses. This is often why individuals end up in jobs - and careers - in which they're unhappy and unfulfilled.

Let's take the example of a programmer who fits the psychological profile in the book by Couger and Zawacki. For reasons that have little to do with job fulfillment - family pressure, or chasing the myth that success is measured by the size of the office, the size of the salary and stock options, and the size of the title this person makes the shift from data-processing professional to a more general and nontechnical position in management. The myth is fulfilled, but the person spends his or her days missing what had once brought pleasure-being knee-deep in the technology of data processing. It's the old "Peter Principle" at work, being elevated to one's level of incompetence-or at least lack of interest.

This person would be better off looking for advancement within data processing.

On the other hand, someone who doesn't fit the profile of the "average" DPer might be insuring the same level of unhappiness by not looking to use the data-processing experience and knowledge as a basis for seeking a more broad-based management position.

The point is that the adage "Know thyself" is an important first step in planning for success.

What other attributes are commonly applied to people working in data processing?

My own Burke Marketing studies in this area have turned up other personality traits (I might as well call them that) that seem to apply to a majority of people in data processing, at least according to the consensus of data-processing management, as well as top management from among the nation's one thousand largest corporations.

For instance, when asked why people in data processing fail to get ahead, lack of motivation led the list, with certain personal traits not far behind. The findings about motivation go hand-in-hand with Couger and Zawacki's conclusions. Data processors need motivation on the job. When it is absent, a lethargy sets in that is counterproductive to career advancement.

The personal traits pinpointed in the Burke study included an inability or unwillingness to get along with others in the corporation; an inability to communicate clearly; a lack of personality; and not being a good listener.

When those same respondents in a subsequent survey were asked to list the most important attributes of a good data processor, they put personal attributes higher than skills and abilities.

But let's go a step further. One of my studies strongly indicates that the three most important attributes when hiring a data processor are prior experience, technical know-how, and education.