EMAC ASSESSMENTS, LLC

CREATE COMPETITIVE ADVANTAGE WITH EMAC'S LAMPE

LEADERSHIP MODEL AND OUR HALO TECHNOLOGIES.

FROM DATA TO IMPLEMENTED SOLUTIONS

In terms of surveys, data are the choices by respondents. When an employee completes an HALO, he or she provides data in each Customized Organizational Inquiry (COI) and Organizational Diagnostic Survey (ODS) item answered. Results are artfully rearranged and compiled data. The worth of the data and results they permit to be generated are determined by the choices of those selecting the items, the sample, and the means of analysis. Some effects of context and situation are incorporated in the data by means of the selection of items and the sample. However, effects of context and situation are not involved in the production of results.

Results open up new possibilities which cause managers to think of entirely new means for dealing with their problems. An engaged manager will want to interpret the results. What do they mean? What should be done about them? Unfortunately, the conversion of results to conclusions involves issues and considerations not in the original data.

Interpreting results to reach conclusions goes beyond gathering the data and how the data are processed into results. Conclusions begin to involve considerations of the manager's context, situation, and purposes. For example, a result might be that the organization's practices and policies for its rewards systems are not working well. But, the context might be that a recommendation to change them is impractical because there is a union contract which restricts the manager's freedom to alter the rewards systems' practices and policies. There might be 100 results yielding ten conclusions. Winnowing results down and interpreting them in light of the application is more than an exercise in data expertise.

Moreover, conclusions are not the same as recommendations for actions. Recommendations go beyond conclusions by delving more deeply into the context and situation. For example, every manager faces realistic constraints that must be considered in interpreting one's conclusions in the form of implementable actions. Examples include budget constraints, other on-going projects, restraints placed by the Board of Directors, political constraints that prevent implementation, economic realities that cannot be ignored, etc. Any recommendation that ignores real life restrictions is unlikely to get implemented. The 100 results, and ten conclusions may boil down to only four recommendations.

Furthermore, even if the manager agrees with the recommendations, it is a vital to test them by having them analyzed and discussed by those who will be responsible for implementing them. Most managers have access to those whose judgments are trusted. What do they think? Can the recommendation be implemented? Do some recommendations have higher priority than others? This "give-and-take" may alter the initial recommendations produced. This step of vetting the recommendations helps prevent Type III error of solving the wrong problem. It builds confidence in the recommendations that emerge, which helps their implementation.

At this stage there is commitment to a set of recommendations and a decision to act on them. The next step is to work out exactly how to actually implement the recommendations. This step is called implementation planning and it is not uncommon, during this stage, to uncover hidden flaws in the recommendations which require sorting out.

Meanwhile, the organization's situation may suddenly change and these changes are folded into the implementation plans. Eventually, the implementation of the recommendations begins. Despite all of the care exercised, one must remember that the manager does not manage a closed system. Changes can keep occurring during the implementation that must be dealt with as they occur.

It is worthwhile to encapsulate this discussion with Figure 1. The curves in Figure 1 are illustrative and are drawn to make the point that results are not even close to being implemented solutions.

Interestingly, the methods of Intervention Effectiveness which involve linear programming allow one to shine some light in the region between results and recommendations. Normal Employee Opinion Surveys cannot do this because their scales are knobless and because there is a lack of an analytical pyramid of defined interdependence. Our HALO allows us to use these powerful methods because each item has a knobby scale.

A Useful Reference

Mackenzie, K. D., Rahim, M. A., & Golembiewski, R. T. (2000). Evolving from data expertise to expert data. In M. A. Rahim, R. T. Golembiewski, & K. D. Mackenzie (Eds.), Current Topics in Management (Vol. 5, pp. 357-368). Stamford, CT: JAI Press.