1. Unit 2 - Global Information
3.4 – Data Analysis Stages

2. Objectives Understand what is meant by data analysis
Know each of the different stages of data analysis and what is involved at each stage.

3. What is data analysis?
The process of evaluating data using analytical and logical reasoning to examine each component of the data provided.
This form of analysis is just one of the many steps that must be completed when conducting business or personal research.
Data from various sources are gathered, reviewed and then analysed to form some sort of finding or conclusion.
There are a variety of specific data analysis methods, some include:
Data mining
Text analytics
Business intelligence
Data visualisations
Data analysis is there to find answers to questions that will be based on good quality information from carefully selected and sourced data.

4. The 8 stages Identify Need Define Scope Identify Potential Sources
Source and Select Information
Select Most Appropriate Tools
Process and Analyse Data
Record and Store Information
Share Result

5. 1. Identify the Need (What information is needed
1. Identify the Need (What information is needed? What do we want to find out?)
This is where the objectives of any data analysis program will be set.
By the end of this stage it should be clear what is hoped to be learnt from the project, including the information that is required from the completed project.
In your business data analysis you must begin with the right questions.
These must be measurable, clear and concise.
Design your questions to either qualify or disqualify potential solutions to your specific problem/opportunity.
For example, start with a clearly defined problem: A government contractor is experiencing rising costs and is no longer able to submit competitive contract proposals. One of many questions to solve this business problem might include: Can the company reduce its staff without compromising quality?
Then define what the information gathered is going to be used for, how will it benefit the company, what exactly do you need to know in order to research the information. Knowing this will help you decide where to look, what to look for and how you will know when you have found it.

6. 2. Define Scope (Content, detail, timescales, constraints)
This stage defines the restriction on the project. For example the overall budget may be set or times by which the information must be available identified.
Decide What To Measure
Using the government contractor example again, consider what kind of data you’d need to answer the key question. In this case, you would need to know the number and cost of current staff and the percentage of time they spend on necessary business functions
In answering this question n, you likely need to answer many sub questions (Are staff currently under-utilised? If so what process improvements would help?)
Finally, in your decision on what to measure, be sure to include any reasonable objections any stakeholders might have (eg. If staff are reduced, how would the company respond to surges in demand?)
Decide How To Measure IT
Thinking about how you measure your data is just as important, especially before the data collection phase, because your measuring process either backs up or discredits your analysis later on. Key questions for this step include:
What is your time frame? (e.g annual versus quarterly costs)
What is your unit of measure? (eg. Pounds versus Euro)
What factors should be included? (eg. Just annual versus annual salary plus cost of staff benefits)

7. 3. Identify Potential Sources (sales figures, customer surveys etc.)
The planners of a project should be able to identify a wide range of sources and ensure that the information gathered is suitable, provides enough information to cover the objectives and is unbiased.
The actual source of data depends on the project. For example customer surveys can be a good source of opinion about how well the organisation is meeting their needs.
For example the Queensland Government want to source specific geographical information, there they might use:
The Queensland Government Priorities in Progress Report -
The Queensland Government data hub -
Australian Bureau of Statistics -
These data sets will most likely be relevant to the collection of background information and baseline data, for example, information on the demographic features of communities or past responses to surveys
Key term – Data set – a group of related data
Before developing plans to collect new data, identify what data sets are already available. While the research plan should be guided by the evaluation questions rather than the available data, there are often pre-existing data sets that can be utilised or adapted. These include internal agency and program information and data sets collected by the government. However, it is important to clarify early if these data sets are available in the format required for the evaluation. Data sets might not be able to be disaggregated down to the geographic level or time period required.

8. 4. Source and Select Information (Determine accuracy and reliability of source, selecting the best)
This is the stage where the information gather and the best is selected. The gathering process uses existing information – for example sales figures or population growth projections.
The selection process is intended to exclude any information that may not be suitable. For example an interviewers poor interpersonal skills could have a negative impact on the answer given by some interviewees making the results unreliable or inaccurate. This information would skew the conclusions based on that data set. In such a case it would be better if this information was ignored.
With your question clearly defined and your measurement priorities set, now its time to collect your data, As you collect and organise your data remember to be the points below in mind:
Before collecting new data, determine what information could collected from existing databases or source. Collect this data first.
Verify the trustworthiness and validity of the sources.
Determine a file storing and naming systems ahead of time to help all tasked team members collaborate. This process saves time and prevents collection of the data twice.
If you need to gather data via observation/interviews, then develop an interview template ahead of time to ensure consistency and save time.
Keep collected data organised in a log with collection dates and add any source notes as you go (including any data normalisation performed). This practice validates your conclusions down the road.

9. 5. Select Most Appropriate tools (charts, graphics, regression, trend analysis)
As we have seen in previous topics information may be presented in many different ways. Charts and graphs present information visually so that patterns may be identified.
There are many well-developed methods available for conceptually or statistically analysing the different kinds of data that can be gathered. When analysing qualitative data, one can develop taxonomies or rubrics to group student comments collected by questionnaires and/or made in classroom discussions.
The frequency of certain types of comments can be described, compared between categories, and investigated for change across time or differences between classes. Frequency data and chi-square analysis can supplement the narrative interpretation of such comments.
For the analysis of quantitative data, a variety of statistical tests are available, ranging from the simple (t-tests) to the more complex (such as the use of factor analysis to develop scales).
Alternatively using ERM, MIS, CMS, an AI based interpreter or KMS tools will analyse the data with the right instructions to produce results.

10. 5. Select Most Appropriate tools (charts, graphics, regression, trend analysis)
Regression analysis
This considers how changing only one of many different variables affects an outcome.
For example when considering the possible impact of an increased population on the sales of tins of beans oncome may also have an effect.
Regression analysis would try to model the impact of a change in population while holding other possible factors as they were before. This information is generally presented graphically.
Or it could be to measure the impact of an increase in the price on the sales of a product, while at the same time holding all other variables that could impact on sales, such as advertising, customer income and price of competitors’ products, constant.
This results in a clear relationship between one factor that has changed and a measurable outcome, such as sales figures.
An example of regression analysis could be to measure the impact of an increase in price on the sales of a product while at the same time holding all the other variables that could impact on sales, such as advertising, customer income and price of competitors’ products, constant. This results in a clear relationship between the one factor that has changed and a measurable outcome, such as sales figures.

11. 5. Select Most Appropriate tools (charts, graphics, regression, trend analysis)
Can be presented in many ways but it attempts to present findings over time, so that behaviour patterns over time can be identified.
This is instead of gaining patterns at once precise moment.

12. 6. Process and Analyse Data (set up a spreadsheet to produce a graph)
This is the stage at which the data that has been collected is entered into software and analysed. For example data could be entered into a spreadsheet and a graph produced to show the information visually.
Remember by this stage you have collected the right data to answer your question from Step 1 so now it’s time for deeper data analysis. Begin by manipulating your data in a number of different ways, such as plotting it out and finding correlations or by creating a pivot table in Excel. A pivot table lets you sort and filter data by different variables and lets you calculate the mean, maximum, minimum and standard deviation of your data.
As you manipulate data, you may find you have the exact data you need, but more likely, you might need to revise your original question or collect more data. Either way, this initial analysis of trends, correlations, variations and outliers helps you focus your data analysis on better answering your question and any objections others might have.
During this step, data analysis tools and software are extremely helpful. Visio, Minitab and Stata are all good software packages for advanced statistical data analysis. However, in most cases, nothing quite compares to Microsoft Excel in terms of decision-making tools.

13. 7. Record and Store Information (Write a report based on the results of the processing)
This is the stage at which any report into the findings is written. This includes all of the results that have been processed.
In essence after analysing your data and possibly conducting further research it’s finally time to interpret and record your results! As you interpret your analysis, keep in mind that you cannot ever prove a hypothesis true: rather, you can only fail to reject the hypothesis. Meaning that no matter how much data you collect, chance could always interfere with your results.
As you interpret the results of your data, ask yourself these key questions:
Does the data answer your original question? How?
Does the data help you defend against any objections? How?
Are there any limitation on your conclusions, any angles you haven’t considered?
If your interpretation of the data holds up under all of these questions and considerations, then you likely have come to a productive conclusion. The only remaining step is to use the results of your data analysis process to decide your best course of action.

14. 8. Share results (Send report to stakeholders)
This final stage of the process is where the results are published so that Stakeholders may inspect them. This may be in the form of a written, printed report, or could be pages on a website for example.
There are two main approaches to writing up the findings of any form of research:
The first is to simply report key findings under each main theme or category, using appropriate verbatim quotes to illustrate those findings. This is then accompanied by a linking, separate discussion chapter in which the findings are discussed in relation to existing research (as in quantitative studies).
This may incorporate charts, graphs, analysis tools, conclusions and evidence of the research given to give it more kudos. For example, an end of year school report to parents would gather information from teachers, subjects, assessments, opinions and present these as a brief discussion. Whereas a report to governors on school attendances would include charts, separate tables for boys and girls, year group analysis etc. and contain tables and a link to findings.
The second is to do the same but to incorporate the discussion into the findings chapter.