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Improving the quality of decisions in companies

management, business, decision making in companies, primary data, secondary data, administration, planning, directing, controlling, leading, staffing

In order to improve the quality of decisions it is important to evaluate them at the time of deciding. One approach is to decide:
1.What is the objective quality of the decision? Was the full process of decision making adopted, i.e. diagnosing all facts, evaluating them, developing alternatives; if so, the decision should be high quality. The more technical the problem, the more a quality decision will solve it;
2.What is the amount of acceptance of the decision by subordinates? Difficulty would ensue if quality considerations conflicted with accepted. Subordinates may resist decisions they thought was made with insufficient facts or inaccurate logic. A manager can, of course, compel or persuade subordinates to accept the decision. The more people are involved in a problem, the less likely a decision based on ‘quality’ would be sufficient.

Another approach suggested that managers should evaluate each problem to see how they can increase the effectiveness of decisions by seeing how important it was to have quality and /or acceptance:
•Where high quality is important, not high acceptance (in financial areas, for example, acceptance is not so important);
•Where acceptance is more important than quality (group decisions help here to ensure a solution is made to work, e.g. holiday rotation);
•Where both high quality and acceptance is needed (e.g. change in the wage payment system this could be solved by management stating its views, but a better (more acceptable) way would be to lead group discussions to come to an acceptable answer).

The role of statistical data for decision making in companies
Information is collected by everybody through the media and people pick out those items that interest them. In business, certain information is needed and it is important that it is known where to find it and how to collect it. Statistics is that part of mathematics that deals with collecting and analyzing data. Data are items known or assumed as a basis for inference. Statistics is concerned with the systematic collection and interpretation of numerical data.

Business statistics therefore provides quantitative bases for arriving at decisions with respect to selected matters connected with the operation of a business. Business statistics includes also the methods and inferences needed to:
•Provide an adequate flow of quality raw material;
•Select and evaluate the performance of both machines and human resources;
•Design products and maintain their quality;
•Evaluate new methods of production, advertising and selling the goods and services of industry at the present time and in the future.

Many people do not find statistical statements easy to read and there is a tendency to translate statistics into a verbal form. Any survey or investigation must be planned and designed carefully in order to reach a valid conclusion. Statistical treatment of a problem is more than examination of data, making calculations and coming to a conclusion. Questions that are also important are:
•How was the data collected?
•How was the survey planned?

Numerical data by itself is meaningless unless it is related in some way. For example, if inflation is said to be 5 per cent, this can have a number of meanings. Does it relate to the last year or the last six months, or is it the average inflation over a period of years/ statistics are used at times to exaggerate, inflate or oversimplify, and careful attention needs to be placed on the method used to collect and summarize the data.

Descriptive statistics is a term used to denote tabular or graphical presentation of data including the calculation of percentages and averages and the measurement of correlation and dispersion in other words, any treatment of numerical data which does not involve generalizations.

Inductive statistics involves making generalizations, estimations or predictions. Many of the methods used are based directly on probability theory.

Primary and secondary data
•Primary data is collected by or on behalf of persons who hope to make use of it.
•Secondary data is derived from the processing or publicizing of primary data. In other words, if data is used by persons other than those for whom it was collected, it is secondary data. This is why it is important to know how data has been collected and processed, how far it is a summary and how accurate it is, before one can appreciate its reliability and full meaning.

Statistics compiled from secondary data are termed secondary statistics. For example, the government publishes tables of unemployment figures (these are secondary data). When this data is used for calculations, they are termed secondary statistics.

Sources of information

Before data is collected, careful planning is needed of the study and the precise formulation of its purpose, scope and objectives. Data taken from a company’s internal records, e.g. production, marketing and finance data, is specific to the business itself.
External data is that obtained from outside the organization, e.g. public sector and government. This could be information on population, unemployment of finance. Both types of data may be required for some studies.

Government statistics

These are widely available and are produced to measure the effects of their policies and the effect external factors have on them. Trends can also be noted and this assists future planning. The central statistics office coordinates the government’s statistical services.

Accuracy of information

Statistical information is rarely perfectly accurate and approximations have to be made quite often. The accountant can be very precise in the collection and paying out of cash. Government statistics of the trade figures are rounded to thousands or hundreds of thousands of units. High levels of accuracy may not always be needed. There is always the consideration of the high cost of very accurate measurements. The degree of accuracy required in statistics will depend upon the type of data being measured and also its proposed uses.

Formal arithmetic is concerned with the manipulating exact quantities; statisticians are generally concerned with manipulating of quantities which are only known approximately. The real measurements of a continuous variable can only be given to a number of decimal places, depending upon the accuracy of the measuring instrument.
An accountant usually records cash to the last penny in the accounts. For other summaries of accounting data, he may only consider pounds and ignore pence. He will then round off to the nearest pound. The convention is to round fractions or decimals to the nearest whole number by rounding 0.5 and above to the next highest whole number and 0.4999 (recurring) to the next lowest whole number. It is important to use rounding methods carefully in order that biased errors do not occur. If the figures are very large, rounding makes them more comprehensible, giving sufficiently accurate figures.
Published: 2007-04-14
Author: Martin Hahn

About the author or the publisher
Martin Hahn PhD has received his education and degrees in Europe in organizational/industrial sociology. He grew up in South-East Asia and moved to Europe to get his tertiary education and gain experience in the fields of scientific research, radio journalism, and management consulting.

After living in Europe for 12 years, he moved to South-East again and has worked for the last 12 years as a management consultant, university lecturer, corporate trainer, and international school administrator

www.martin-hahn.net

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