Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data and the combining of data sets to enrich data.

Data preparation is often a lengthy undertaking for data professionals or business users, but it is essential as a prerequisite to put data in context in order to turn it into insights and eliminate bias resulting from poor data quality.

For example, the data preparation process usually includes standardizing data formats, enriching source data, and/or removing outliers.

Benefits of Data Preparation

76% of data scientists say that data preparation is the worst part of their job, but the efficient, accurate business decisions can only be made with clean data. Data preparation helps:

• Fix errors quickly: Data preparation helps catch errors before processing. After data has been removed from its original source, these errors become more difficult to understand and correct.
• Produce top-quality data: Cleaning and reformatting datasets ensures that all data used in analysis will be high quality.
• Make better business decisions: Higher quality data that can be processed and analyzed more quickly and efficiently leads to more timely, efficient and high-quality business decisions.

Additionally, as data and data processes move to the cloud, data preparation moves with it for even greater benefits, such as:

• Superior scalability: Cloud data preparation can grow at the pace of the business. Enterprise don’t have to worry about the underlying infrastructure or try to anticipate their evolutions.
• Future proof: Cloud data preparation upgrades automatically so that new capabilities or problem fixes can be turned on as soon as they are released. This allows organizations to stay ahead of the innovation curve without delays and added costs.
• Accelerated data usage and collaboration: Doing data prep in the cloud means it is always on, doesn’t require any technical installation, and lets teams collaborate on the work for faster results.

Additionally, a good, cloud-native data preparation tool will offer other benefits (like an intuitive and simple to use GUI) for easier and more efficient preparation.

Data Preparation Steps

The specifics of the data preparation process vary by industry, organization and need, but the framework remains largely the same.

1. Gather data

The data preparation process begins with finding the right data. This can come from an existing data catalog or can be added ad-hoc.

2. Discover and assess data

After collecting the data, it is important to discover each dataset. This step is about getting to know the data and understanding what has to be done before the data becomes useful in a particular context.

3. Cleanse and validate data

Cleaning up the data is traditionally the most time consuming part of the data preparation process, but it’s crucial for removing faulty data and filling in gaps. Important tasks here include:

• Removing extraneous data and outliers.
• Filling in missing values.
• Conforming data to a standardized pattern.
• Masking private or sensitive data entries.

Once data has been cleansed, it must be validated by testing for errors in the data preparation process up to this point. Often times, an error in the system will become apparent during this step and will need to be resolved before moving forward.

4. Transform and enrich data

Transforming data is the process of updating the format or value entries in order to reach a well-defined outcome, or to make the data more easily understood by a wider audience. Enriching data refers to adding and connecting data with other related information to provide deeper insights.

5. Store data

Once prepared, the data can be stored or channeled into a third party application such as a business intelligence tool clearing the way for processing and analysis to take place.

Preliminary Steps in Quantitative Data Analysis

After collecting and before analyzing survey data, we recommend closely examining the data set to ensure the accuracy and representativeness of the information and the integrity of subsequent analyses. Data conditioning involves attending to detailed components of both an actual data set and the particular analytic techniques chosen to examine the data. This often requires more time and attention to detail than either the data collection or the subsequent analytic procedures. Though data conditioning can be a time-intensive step, carefully executing these practices allows one to responsibly proceed with accurately analyzing, interpreting, and reporting quantitative data. In addition, it offers a more fine-grained picture of the study abroad student sample, which can be quite informative even before more focused statistical analyses are begun.

Data Accuracy

The initial step in data conditioning attends to the issue of accurate data entry. This step requires an examination of how data have been entered (or uploaded) into a data file and a consideration of issues that could yield inaccurate analyses. Comparing the actual obtained data to the final data file is an essential step; however, the size of the sample under study affects the method by which this is typically executed. Tabachnick and Fidell (2013) outlined several components to consider in ensuring data accuracy; for example with small data sets, careful proofreading of all variable values is recommended, but for larger data sets, analyzing particular descriptive statistics and graphic representations of variables is typically more efficient in ensuring appropriate variable value ranges (e.g., possible minimum and maximum values). Analyzing descriptive statistics of variables differs depending on the types of variables examined (i.e., categorical or continuous variables). Categorical variables consist of data that are grouped into discrete categories: either nominal classifications devoid of any particular order or ordinal classifications that have a meaningful ranked order. For example, the location of a study abroad program (e.g., Asia, Europe, or South America) is a nominal variable, whereas asking participants to rate their responses to questions along a Likert-type rating scale (e.g., 1 = strongly disagree to 5 = strongly agree, or 1 = poor to 7 = excellent) is an example of an ordinal variable. Though Likert-type scale responses are technically categorical variables, these responses are often treated as continuous variables in data conditioning and later analyses. Continuous variables take on numeric values within a defined range and have equal intervals between data points (e.g., a student’s age or number of months immersed in a host country).

To check data accuracy for categorical variables, evaluators and researchers must examine the frequencies of responses in each possible category. For example, utilizing the frequency function in SPSS will display tables that include the number and percentage of responses in each of a variable’s categories, as well as the number of valid and missing values (after opening SPSS and loading your data file, follow these SPSS menu choices: Analyze > Descriptive Statistics > Frequencies). In addition, various types of charts can also be generated through the same SPSS navigation menu to graphically display frequencies, including bar charts, pie charts, and histograms. In looking at the frequency tables, we can find several questions that are helpful to ask. Are any values out of the range of the numbered categories (e.g., there are three categories of study abroad program types arbitrarily numbered 1 through 3 but the frequency chart or table indicates other number categories beyond these three values)? Finding nonexistent categories easily brings to light these types of data-entry errors. What do the frequencies suggest? How many responses are in each category? Which category contains the lowest and highest number of responses? What are the implications of low or high frequencies in particular categories?

To examine data accuracy for continuous variables (including Likert-type scales), we must analyze other descriptive statistics beyond frequencies. For instance, we often analyze the mean values (the averages) and dispersion (i.e., ranges and minimum-maximum values) of the continuous variables in SPSS (follow these SPSS menu choices: Analyze > Descriptive Statistics > Descriptives > Options) to answer important questions about the accuracy of the data. Do all of the values fall within the range of possible scores? If not, this points to data-entry errors. Do the mean values for the variables make sense based on what is already known about the population under study? The dispersion of a variable is also important to examine, particularly if there are any out-of-range values (i.e., below the minimum or beyond the maximum possible values). In addition, the standard deviation (the amount of variation from the mean) is also important to consider, as this indicates how closely values are dispersed around the sample’s mean. A low standard deviation value suggests that overall scores are generally clustered around the mean with little variation, making the likelihood of finding differences across the sample relatively small. Conversely, a high standard deviation value indicates that the sample’s scores are more widely dispersed across a wider range of scores, indicating a greater likelihood of differences in scores within a sample.

Finally, it is important to ensure that missing data are properly entered and coded in the data file. Data are missing from data files for several reasons, and these must be identified for accurate analyses and reporting. Participants, for instance, may choose not to answer particular questions on a survey, whereas others may have inadvertently skipped several questions or run out of time to complete the survey, leaving some answers blank. Finally, the nature of some survey questions may require participants to legitimately skip particular questions or blocks of questions. In SPSS, missing values are indicated by either an asterisk or the absence of any values. A more thorough discussion of missing data is found later.

Participant Response Rates

Once the data are checked for accuracy, response rates must be carefully examined to understand the representativeness of the sample. For several reasons, it is often not possible to survey, interview, or otherwise investigate every individual from a population of interest. Comparing the sample participants to the larger overall population of interest examining how representative the sample is and discussing any significant distinctions between the two is critical before findings can be understood and applied more broadly. Furthermore, external validity which considers the generalizability of one’s findings or the extent to which one’s findings generalize beyond the current sample to the overall population under study is an important aim of quantitative inquiry.

It is essential to know and report a participant response rate by determining the total number of individuals invited to participate in a study and those who actually participated. This is a simple proportion to calculate by dividing those who participated by the total invited, although it is important to take into account those who never received the initial invitation because of invalid e-mail addresses or returned mail. Beyond understanding response rates, it is necessary to consider how representative a sample is relative to the overall population of interest. How many and what types of individuals compose the overall population under study, and how does this compare to your final sample? Is the sample representative of important demographics of the total population, including race, ethnicity, gender, age, and other salient characteristics? Are there over- or underrepresented groups in your sample? What are the implications of these disparities? If these data are not readily accessible, campus institutional research or enrollment management areas can typically provide assistance in obtaining population data. Although beyond the scope of this chapter, weighting techniques can also be applied to correct for nonresponse biases (see NSSE, 2014).

Missing Data

The issue of missing data is one of the most prevalent quandaries in quantitative research and assessment efforts. In an extended discussion on the implications of and strategies for handling missing data, Tabachnick and Fidell (2013) stated that it is essential to first determine the severity of any missing data, particularly the patterns of missing data, the amount of data missing, and the reasons why the data may be missing. In quantitative research, missing data are often categorized as MCAR (missing completely at random), MAR (missing at random, which constitutes ignorable nonresponses), and MNAR (missing not at random, which constitutes nonignorable nonresponses) (Little, Jorgensen, Lang, & Moore, 2014). Randomly scattered missing values are less serious than nonrandom missing values, as the latter can affect the generalizability of results.

We can determine random from nonrandom missing data by testing for patterns in the missing data. Tabachnick and Fidell (2013) recommended two ways to test for this: First, one can construct a new variable that represents cases with missing and nonmissing values for an independent variable (e.g., a new variable could be created and coded as 0 = missing and 1 = not missing) and then test for mean differences on a continuous outcome measure between the groups using an independent-samples t-test (follow these SPSS menu choices: Analyze > Compare Means > Independent Samples t-Test). We can then examine the SPSS output and determine whether the two means differ significantly. The second strategy Tabachnick and Fidell (2013) outlined is SPSS’s missing value analysis (follow these SPSS menu choices: Analyze > Missing Value Analysis), which highlights the numbers and patterns of missing values by providing statistics including frequencies of missing values, t-tests, and missing patterns.

Once the missing data patterns have been identified, there are a few different approaches and resulting implications in handling missing data that emphasize either excluding or substituting missing values. Excluding cases (participants) with missing data from analyses is a reasonable option if there is a random pattern of missing values, very few participants have missing data, and the participants are missing data on different variables and it appears that the missing cases represent a random subsample of the aggregate sample (Tabachnick & Fidell, 2013). By default, cases with missing values are usually excluded from most analyses in SPSS based on a listwise deletion technique.  Although an acceptable approach  provided that the previous points are considered excluding cases with extensive missing values (over 10% in most cases) can compromise the external validity of the results.

Tabachnick and Fidell (2013) recommended a number of different imputation or substitution approaches to use if a variable is missing extensive data yet is important to the analysis: First, one can use prior knowledge to replace missing values with an informed estimate if the sample is large and the number of missing values is small. For instance, if given experience or expertise in a field one is sure that the missing values would equate to the median, mean, or most frequent response, it is acceptable to substitute those values and note the reasons for doing so. Second, one can transform an ordinal or continuous variable into a dichotomous variable (e.g., participated or did not participate in study abroad; low or high engagement) and predict into which category to place the missing case. For longitudinal data, one can use the last observed value to fill in missing data, but this implies that there was no change over time. Third, one can substitute missing values by inserting an overall sample mean or a subsample mean defined by a particular grouping variable. Finally, one can utilize a regression-based technique on those cases with complete data to generate an equation that substitutes estimated missing values for incomplete cases. In the long run, effective methods of reducing missing data may focus on well-constructed surveys in which students are less likely to leave data blank and exhortations for students to leave no answers blank as they work through the questions.

For those interested in a much more in-depth discussion of missing data analysis, see Enders (2010) for quite thorough overviews and methods of different techniques to handle various types of missing data.

Detecting Outliers (Extreme Values)

Occasionally, outliers or extreme, unexpected values surface in the data and must be addressed, especially with small sample sizes. Participants can randomly respond to questions or represent genuinely rare cases, so it is often helpful to examine the other items attached to a particular participant to see a fuller picture and possibly explain any extreme values. Univariate outliers (an extreme value on one variable) and multivariate outliers (an unusual combination of scores on two or more variables) distort sample statistics (i.e., can lead to either stating there is a relationship or effect when there is not one or failing to detect a relationship or effect when there is one) and interfere with generalizability.

Tabachnick and Fidell (2013) discussed several reasons for outliers: First, incorrect data entry can produce incorrect values, some of which may be outliers (e.g., accidentally typing a value of 22 instead of 2). Second, failure to specify missing-value codes for data that should be read as real data can also produce outliers. Third, an outlier could be from outside of the population from which we wish to sample; we should delete these cases once they are detected, as they are not relevant to our analyses. Finally, an outlier could be from the population of interest, but the distribution of the variable has more extreme values than expected in a normal distribution. In this final case, we can retain these outliers but change the value on the variable so that the outlier’s impact on the analyses is attenuated. Given the more advanced nature of identifying and handling multivariate outliers, we recommend referring to Tabachnick and Fidell 2013) for a more extended discussion.

Looking for Correlations Among Variables

Data conditioning also involves examining the degree to which continuous variables (including Likert-type scales) are correlated – or related – to one another. Note that correlations are not viable using categorical data, as the numerical values of those variables are not meaningful (the numerical values solely serve to categorize data into discrete groups). When examining correlations between continuous variables, correlation coefficients in SPSS will indicate the direction and strength of the correlation between the variables. Correlation coefficients are reported as values between -1.0 and +1.0. (Note: A positive relationship indicates that as one variable either increase or decreases, the other variable increases or decreases in the same manner; a negative relationship indicates that as one variable either increases or decreases, the other variable moves in the opposite direction.) To examine the correlations among all of the continuous variables in a data set, we can produce a correlation matrix in SPSS (follow these SPSS menu choices: Analyze > Correlate > Bivariate), which is simply a table that allows one to see the correlation coefficients for the specified variables to determine the direction (positive or negative) and degree to which they are related with each other.  The closer correlation coefficients are to a value of -1.0 or +1.0, the stronger the negative or positive relationships, whereas the closer these values are to zero, the weaker the relationships.

For example, using responses from two survey items found on the GPI, we are interested in understanding the relationship between the number of multicultural courses taken at college and the degree to which students felt informed of current issues that impact international relations. Intuitively, it might seem that there could be a relationship between these two items, but whether this is statistically significant – and if so, the strength of this relationship – will be useful to understand.  Using the SPSS navigation described earlier, we ran a bivariate (two-variable) correlation on these two items and found a correlation coefficient of 0.058 that was statistically significant. This value indicates that there is a statistically significant and positive (the correlation coefficient was greater than zero) relationship between these variables; in other words, as students complete more multicultural courses, their understanding of current global issues also increase. This correlation coefficient also illustrates, though, that although statistically significant, it is a weak relationship, as the value is very close to zero at 0.058. In this case, our intuition was correct in that these GPI items are, indeed, related, but the weak relationship between them is not that meaningful.

Of particular concern in the data conditioning stage for multivariate analyses is when two or more variables are strongly correlated with each other. For instance, problems can occur when independent variables are highly correlated with each other in the same multivariate model, which may lead to unstable findings, larger standard errors, and a reduced likelihood of statistical significance (see Grimm & Yarnold, 1995, for an expanded discussion of multicollinearity issues). As such, it is important to examine a correlation matrix prior to engaging in multivariate analyses.

1. Univariate Data:

This type of data consists of only one variable. The analysis of univariate data is thus the simplest form of analysis since the information deals with only one quantity that changes. It does not deal with causes or relationships and the main purpose of the analysis is to describe the data and find patterns that exist within it. The example of a univariate data can be height.

Suppose that the heights of seven students of a class is recorded (figure 1), there is only one variable that is height and it is not dealing with any cause or relationship. The description of patterns found in this type of data can be made by drawing conclusions using central tendency measures (mean, median and mode), dispersion or spread of data (range, minimum, maximum, quartiles, variance and standard deviation) and by using frequency distribution tables, histograms, pie charts, frequency polygon and bar charts.

2. Bivariate Data:

This type of data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to find out the relationship among the two variables. Example of bivariate data can be temperature and ice cream sales in summer season.

Suppose the temperature and ice cream sales are the two variables of a bivariate data (figure 2). Here, the relationship is visible from the table that temperature and sales are directly proportional to each other and thus related because as the temperature increases, the sales also increase. Thus bivariate data analysis involves comparisons, relationships, causes and explanations. These variables are often plotted on X and Y axis on the graph for better understanding of data and one of these variables is independent while the other is dependent.

3. Multivariate Data:

When the data involves three or more variables, it is categorized under multivariate. Example of this type of data is suppose an advertiser wants to compare the popularity of four advertisements on a website, then their click rates could be measured for both men and women and relationships between variables can then be examined.

It is similar to bivariate but contains more than one dependent variable. The ways to perform analysis on this data depends on the goals to be achieved.Some of the techniques are regression analysis,path analysis,factor analysis and multivariate analysis of variance (MANOVA).

1. Mean

The arithmetic mean, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list. The mean is useful in determining the overall trend of a data set or providing a rapid snapshot of your data. Another advantage of the mean is that it’s very easy and quick to calculate.

Pitfall:

Taken alone, the mean is a dangerous tool. In some data sets, the mean is also closely related to the mode and the median (two other measurements near the average). However, in a data set with a high number of outliers or a skewed distribution, the mean simply doesn’t provide the accuracy you need for a nuanced decision.

2. Standard Deviation

The standard deviation, often represented with the Greek letter sigma, is the measure of a spread of data around the mean. A high standard deviation signifies that data is spread more widely from the mean, where a low standard deviation signals that more data align with the mean. In a portfolio of data analysis methods, the standard deviation is useful for quickly determining dispersion of data points.

Pitfall:

Just like the mean, the standard deviation is deceptive if taken alone. For example, if the data have a very strange pattern such as a non-normal curve or a large amount of outliers, then the standard deviation won’t give you all the information you need.

3. Regression

Regression models the relationships between dependent and explanatory variables, which are usually charted on a scatterplot. The regression line also designates whether those relationships are strong or weak. Regression is commonly taught in high school or college statistics courses with applications for science or business in determining trends over time.

Pitfall:

Regression is not very nuanced. Sometimes, the outliers on a scatterplot (and the reasons for them) matter significantly. For example, an outlying data point may represent the input from your most critical supplier or your highest selling product. The nature of a regression line, however, tempts you to ignore these outliers. As an illustration, examine a picture of ANSCOMBE’S QUARTET, in which the data sets have the exact same regression line but include widely different data points.

4. Sample Size Determination

When measuring a large data set or population, like a workforce, you don’t always need to collect information from every member of that population – a sample does the job just as well. The trick is to determine the right size for a sample to be accurate. Using proportion and standard deviation methods, you are able to accurately determine the right sample size you need to make your data collection statistically significant.

Pitfall:

When studying a new, untested variable in a population, your proportion equations might need to rely on certain assumptions. However, these assumptions might be completely inaccurate. This error is then passed along to your sample size determination and then onto the rest of your statistical data analysis

5. Hypothesis Testing

Also commonly called t testing, hypothesis testing assesses if a certain premise is actually true for your data set or population. In data analysis and statistics, you consider the result of a hypothesis test statistically significant if the results couldn’t have happened by random chance. Hypothesis tests are used in everything from science and research to business and economic

Pitfall:

To be rigorous, hypothesis tests need to watch out for common errors. For example, the placebo effect occurs when participants falsely expect a certain result and then perceive (or actually attain) that result. Another common error is the Hawthorne effect (or observer effect), which happens when participants skew results because they know they are being studied.

Overall, these methods of DATA ANALYSIS add a lot of insight to your DECISION-MAKING PORTFOLIO, particularly if you’ve never analyzed a process or data set with statistics before. However, avoiding the common pitfalls associated with each method is just as important. Once you master these fundamental techniques for statistical data analysis, then you’re ready to advance to more powerful data analysis tools.

The Classical Model 

On confrontation of a manager with a certain decision making situation, the manager would collect all the critical information and the data that is required for performing a particular activity and also would take the decision that will certainly be for the betterment of the organization.

The Administrative Model 

In such a model, the manager has more concern for himself.
b. On confrontation of a manager with a certain decision making situation, the manager would collect whatever information or the data that will be available and then will take a decision, which may not be in the best interests of the organization but will certainly be good for fulfilling his personal interests.
c. Expediency and the opportunism, both act as the hallmarks of the Administrative Model.

The Herbert Simon Model

1. This model is linked with the decision making process.
   b. Explains the core of the decision making.
   c. Used as the base for explaining the decision making process.
   d. According the Herbert Simon Model, the process of the decision making consists of the following phases:

A) The Intelligence Phase

In this phase, the various activities for finding out the problems related to the searching of the operating environment are involved. By this, the identification of the various conditions can be done which ultimately helps in taking the decisions at the different levels. Extensive and the comprehensive database is must for the intelligence phase, making this phase very suitable for searching or scanning of the environment.

In this phase, the type of the environment forms a very major factor and hence the types of the environment can be categorized as the follows:

1. The Societal Environment: Mainly includes the economic, the legal and the social environment and it is this type of the environment in which the organization operates.
2. The Competitive Environment: Includes the understanding and the analyzing of the characteristics, the trends and the behavior of or at the market place and also the various players of the market in which the organization operates.
3. The Organizational Environment: Includes the various capabilities, the strengths, the weaknesses, the constraints and the various other factors that affect the ability of the organization to discharge or operate its various types of the activities.

B) The Design Phase

• The inventing, the developing and the analyzing of the various alternatives or the solutions to the particular problem forms a major part of this phase. The various steps that are to be followed in this phase can be summarized as the follows:
• Support in getting the in depth knowledge of the problem.
  A correct model of the situation can be made and the assumptions of the model need to be tested.

Support for the generation of the solutions can be obtained by:
I. Manipulation of the model for the development of the insights.
II. Creation of the database retrieval system.

• Support for testing the feasibility of the solutions.

C) The Choice Phase

The selection of a specific alternative or the course of the action from the ones which have been generated and considered during the design phase, takes place during this phase. The choice procedure and the implementation of the chosen alternative form a very major part of the Choice phase.

The flow of the activities takes place from the intelligence phase to the design phase and then finally to the choice phase. But one very important point that must be remembered here is that at any phase there may be a return to a previous phase.

Limitations of the Simon Model
1. This model does not go further than the choice model.
2. Does not include the cognizance of the implementation and also of the feedback aspects.

Main types

There are many types of decision making and these can be easily categorized into the following 4 groups:

• Rational
• Intuitive
• Combinations
• Satisficing
• Decision Support Systems
• Recognition primed decision making

Types

Rational

Rational decision making is the commonest of the types of decision making that is taught and learned when people decide that they want to improve their decision making. These are logical, sequential models where the emphasis is on listing many potential options and then working out which is the best. Often the pros and cons of each option are also listed and scored in order of importance.

The rational aspect indicates that there is considerable reasoning and thinking done in order to select the optimum choice. Because we put such a heavy emphasis on thinking and getting it right in our society, there are many of these models and they are very popular. People like to know what the steps are and many of these models have steps that are done in order.

People would love to know what the future holds, which makes these models popular. Because the reasoning and rationale behind the various steps is that if you do x, then y should happen. However, most people have personal experience that the world usually doesn’t work that way!

Intuitive

The second of the types of decision making are the intuitive models. The idea here is that there may be absolutely no reason or logic to the decision making process. Instead, there is an inner knowing, or intuition, or some kind of sense of what the right thing to do is.

And there are probably as many intuitive types of decision making as there are people. People can feel it in their heart, or in their bones, or in their gut and so on. There are also a variety of ways for people to receive information, either in pictures or words or voices.

People talk about extra sensory perception as well. However, they are still actually picking up the information through their five senses. Clair sentience is where people feel things, clair audience is hearing things and clairvoyance is seeing things.

And of course we have phrases such as ‘I smell a rat’, ‘ it smells fishy’ and ‘I can taste success ahead’.

Other types of decision making in the intuitive category might include tossing a coin, throwing dice, tarot cards, astrology, and so on.

Decision wheels are usually more humorous than intuitive but they do have a serious application.

Combinations

Many decisions are actually a result of combinations of rational and intuitive processes. This can be deliberate where a person combines aspects of both, or it can occur unwittingly.

For example, a person has listed the pros and cons of the options, assigned numerical values and added them all up. (The rational part.) But the end result is not really satisfactory, they are uneasy somehow (the intuitive part), so they change the parameters, and the numbers add up differently. This new result is more ‘satisfactory’, so they go with that one.

Satisficing

Instead of evaluating all the possible options and choosing the best, satisficing is where we pick the first one that will give us the result. We choose an option that is ‘good enough’, one that satisfies our needs and sacrifices other potentially better options. Hence, satisfice.

Decision Support Systems

Because computers can process large amounts of data quickly, they were soon put to use to help make decisions. Decision Support Systems range from a simple spreadsheet to organize information graphically, to very complex programs organizing info in international companies and including artificial intelligence that can suggest alternative options and solutions.

There are various types of decision making systems depending on how many people are involved, the form of the information being processed, what type of result is required, and so on.

There are pros and cons to using computers in this way, and of course, the computer is only as good as the information that it is processing. Which means that it still comes down to the humans…!

Recognition primed

Gary Klein has spent considerable time studying human decision making and his results are very interesting. He believes that we make 90 to 95% of our decisions in a pattern recognition way. He suggests that what we actually do is gather information from our environment in relation to the decision we want to make. We then pick an option that we think will work. We rehearse it mentally and if we still think it will work, we go ahead.

If it does not work mentally, we choose another option and run that through in our head instead. If that seems to work, we go with that one. We pick scenarios one by one, mentally check them out, and as soon as we find one that works, we choose it.

He also points out that as we get more experience, we can recognise more patterns, and we make better choices more quickly.

Of interest here is that the military in many countries have adapted his methods because they are considerably more effective than any of the types of decision making we’ve discussed already. In fact, you could say that his model is a combination of the rational and intuitive approaches. (That’s why I said above that there are only 4 groups!) It’s also an example of satisficing!

1. Title Page

The very first page in a business report should be the title page. And since this is the first thing the reader will see, the title should clearly set out the subject of the report. It is also standard to include the report author’s name and the date the report was completed.

2. Report Summary

Most business reports begin with a short summary. This is so readers can digest key points from the report quickly without having to read the entire thing. Try to include the following:

• A brief description of what the report is about
• How the report was completed (e.g. data collection and analysis methods)
• Your main findings from the research
• Key conclusions and recommendations

A paragraph or two should be enough for this in shorter business reports. However, for longer or more complex reports, you should consider including a full executive summary.

3. Table of Contents

In any report more than a few pages long, you will need a table of contents. This should set out the title of each section and where readers can find them in the report. If you are writing your report in Microsoft Word, moreover, you can use the Heading styles to create a table of contents.

4. Introduction

The introduction is the first part of the report proper. Use it to set out the brief you received when you were asked to compile the report. This will frame the rest of the report by providing:

• Background information (e.g. market information or business history)
• The aims of the report (i.e. what you set out to achieve)
• The scope of the report (i.e. what it will cover and what it will ignore)

These are sometimes known as the ‘terms of reference’ for a report.

5. Methods and Findings

The next section should set out your research methods (i.e. what you did to collect information). This may be as simple as specifying where you found the information you used in the report, but make sure to provide a more detailed explanation if you have conducted any original research.

After this, you can set out your findings. Try to focus on information directly relevant to your brief here, as packing too much detail into your report may make it hard to follow. One good tip on this front is to use visual aids to present key data, such as by adding charts or illustrations.

6. Conclusions and Recommendations

Once you have explained your findings, you will need to make conclusions based on your research (i.e. set out what you have learned from writing the report). You may also need to recommend a plan or course of action based upon your findings, especially if this was part of the brief.

Anything you include in this section should be related to your brief. For example, if you were asked to write a report about expanding into a new country, your conclusions and recommendations would be about the viability of such an expansion and what the company could do to achieve its goals.

7. References and Appendices

Most business reports will draw information from a variety of sources. These should be cited in the text of the report itself, but you should also list your sources in a bibliography.

And finally, if required, you can include extra information in your report by adding an appendix (or multiple appendices if you have a lot of material to include). This is a good place to put in-depth data that does not fit easily into the main report, such as interview transcripts or survey results.

Summary: The Structure of a Business Report

Typically, most business reports will be structured along the following lines:

• Title Page: Give a clear, informative title that sets out what the report is about, as well as the report author’s name and a date of publication.
• Summary: A rundown of key points from the report, including research methods, findings, and any conclusions or recommendations.
• Table of Contents: In longer reports, include a table of contents. This should list the title of each section in the report and where it can be found.
• Introduction: A summary of the brief you received for the report.
• Methods and Findings: A more detailed look at data collection and analysis methods, along with the main findings of your research.
• Conclusions and Recommendations: What you have learned from your research and recommendations for what to do next (if required).
• References and Appendices: At the end of your report, include a bibliography detailing the sources you have used. You can add any extra material (e.g. interview transcripts or raw data) to an appendix.

Objectives of cost accounting are ascertainment of cost, fixation of selling price, proper recording and presentation of cost data to management for measuring efficiency and for cost control and cost reduction, ascertaining the profit of each activity, assisting management in decision making and determination of break-even point.

The aim is to know the methods by which expenditure on materials, wages and overheads is recorded, classified and allocated so that the cost of products and services may be accurately ascertained; these costs may be related to sales and profitability may be determined. Yet with the development of business and industry, its objectives are changing day by day.

Following are the main objectives of cost accounting:

1. To ascertain the cost per unit of the different products manufactured by a business concern;
2. To provide a correct analysis of cost both by process or operations and by different elements of cost;
3. To disclose sources of wastage whether of material, time or expense or in the use of machinery, equipment and tools and to prepare such reports which may be necessary to control such wastage;
4. To provide requisite data and serve as a guide for fixing prices of products manufactured or services rendered;
5. To ascertain the profitability of each of the products and advise management as to how these profits can be maximised;
6. To exercise effective control if stocks of raw materials, work-in-progress, consumable stores and finished goods in order to minimise the capital locked up in these stocks;
7. To reveal sources of economy by installing and implementing a system of cost control for materials, labour and overheads;
8. To advise management on future expansion policies and proposed capital projects;
9. To present and interpret data for management planning, evaluation of performance and control;

• To help in the preparation of budgets and implementation of budgetary control;
• To organise an effective information system so that different levels of management may get the required information at the right time in right form for carrying out their individual responsibilities in an efficient manner;
• To guide management in the formulation and implementation of incentive bonus plans based on productivity and cost savings;
• To supply useful data to management for taking various financial decisions such as introduction of new products, replacement of labour by machine etc.;
• To help in supervising the working of punched card accounting or data processing through computers;
• To organise the internal audit system to ensure effective working of different departments;
• .To organise cost reduction programmes with the help of different departmental managers;
• To provide specialised services of cost audit in order to prevent the errors and frauds and to facilitate prompt and reliable information to management; and
• To find out costing profit or loss by identifying with revenues the costs of those products or services by selling which the revenues have resulted.

The advantages of cost accounting are:

Disclosure of profitable and unprofitable activities

Since cost accounting minutely calculates the cost, selling price and profitability of product, segregation of profitable or unprofitable items or activities becomes easy.

Guidance for future production policies

On the basis of data provided by costing department about the cost of various processes and activities as well as profit on it, it helps to plan the future.

Periodical determination of profit and losses

Cost accounting helps us to determine the periodical profit and loss of a product.

To find out exact cause of decrease or increase in profit

With the help of cost accounting, any organization can determine the exact cause of decrease or increase in profit that may be due to higher cost of product, lower selling price or may be due to unproductive activity or unused capacity.

Control over material and supplies

Cost accounting teaches us to account for the cost of material and supplies according to department, process, units of production, or services that provide us a control over material and supplies.

Relative efficiency of different workers

With the help of cost accounting, we may introduce suitable plan for wages, incentives, and rewards for workers and employees of an organization.

Reliable comparison

Cost accounting provides us reliable comparison of products and services within and outside an organization with the products and services available in the market. It also helps to achieve the lowest cost level of product with highest efficiency level of operations.

Helpful to government

It helps the government in planning and policy making about import, export, industry and taxation. It is helpful in assessment of excise, service tax and income tax, etc. It provides readymade data to government in price fixing, price control, tariff protection, etc.

Helpful to consumers

Reduction of price due to reduction in cost passes to customer ultimately. Cost accounting builds confidence in customers about fairness of price.

Classification and subdivision of cost

Cost accounting helps to classify the cost according to department, process, product, activity, and service against financial accounting which give just consolidate net profit or loss figure of any organization without any classification or sub-division of cost.

To find out adequate selling price

In tough marketing conditions or in slump period, the costing helps to determine selling price of the product at the optimum level, neither too high nor too low.

Proper investment in inventory

Shifting of dead stock items or slow moving items into fast moving items may help company to invest in more proper and profitable inventory. It also helps us to maintain inventory at the most optimum level in terms of investments as well as variety of the stock.

Correct valuation of inventory

Cost accounting is an accurate and adequate valuation technique that helps an organization in valuation of inventory in more reliable and exact way. On the other hand, valuation of inventory merely depends on physical stock taking and valuation thereof, which is not a proper and scientific method to follow.

Decision on manufacturing or purchasing from outside

Costing data helps management to decide whether in-house production of any product will be profitable, or it is feasible to purchase the product from outside. In turn, it is helpful for management to avoid any heavy loss due to wrong decision.

Reliable check on accounting

Cost accounting is more reliable and accurate system of accounting. It is helpful to check results of financial accounting with the help of periodic reconciliation of cost accounts with financial accounts.

Budgeting

In cost accounting, various budgets are prepared and these budgets are very important tools of costing. Budgets show the cost, revenue, profit, production capacity, and efficiency of plant and machinery, as well as the efficiency of workers. Since the budget is planned in scientific and systemic way, it helps to keep a positive check over misdirecting the activities of an organization.

Disadvantages

1. Lack Of Fixed Principles

Generally, cost accounting system is practiced on presumed notions. It does not follow fixed accounting principles. So, there is a lack of uniformity in this system.

2. Costly System

This is another major drawback of cost accounting. There is a need of highly skilled and qualified manpower and resources to maintain cost accounting system in the organization. A lot of clerical works and various procedure make cost accounting more expensive.

3. Complex System

It is very complicated system of accounting. It requires various formulas to record cost related data. It needs specific knowledge to prepare different reports. Due to numerous steps and rules, it is considered as complex system of accounting.

4. Not Suitable for Small Business

Small business firms with less number of production or transactions do not prefer cost accounting because of higher cost and complexity. 

5. Ignores Financial Items

Actual profit or loss of the business cannot be ascertained by cost accounting because it ignores income and expenses of financial nature.

6. Lack Of Accuracy

Cost accounting avoids financial character expenses at the time of cost calculation. It does not follow double entry system to check the accuracy. So, result obtained from cost accounting may lack accuracy.

7. Not Helpful In Decision Making

Only cost related past data and information can be obtained from cost accounting. So, top level management cannot be benefited from cost accounting to make future decision and plans. Delay in data and information may also hamper decision making process.

8. Dependent

Cost accounting cannot be installed and maintained without other accounting system. It is totally dependent with other branches of accounting, especially with financial accounting.