Decision Analysis for Managers

BUS9040M: Decision Analysis for Managers
Assignment 5: From Data to Decisions: Data Analytics
Data-driven decision-making (DDDM) is the process of using data to inform your decision-making
process. Data analytics is at the heart of DDDM. Data analytics refers to the process and practice of
analysing data to answer questions or to extract meaningful insights that an organization can use to
inform its strategy and, ultimately, reach its objectives. Therefore, data only has value if it is turned into
information. In the context of the DIK pyramid (Wallace, 2007) (see seminar 1), managers can then
use this information in combination with their experience and judgement to create
knowledge and
ultimately improve their decision-making.
This introduction sets the context of this assignment. Your task is to
independently apply data analytics
techniques that you learnt in the seminars to extract meaningful insights or information from data for
decision making. In the context of the DIK pyramid, you then draw out some knowledge or what you
have learnt from the data with a view to answering specific questions. In particular, follow the steps
Step 1: Questions: Develop 3 to 5 (research) questions that you seek to answer from data. These should
be questions that may be of interest to managers or to an organisation seeking to extract useful insights
or information from data (see introduction above).
Step 2: Data: Find some data (see guidance on data sources in Box 1 below), download it onto an Excel
spreadsheet. You may have to “clean” the data before analysis (step 3). Data cleaning includes checking
that the data is usable e.g., missing values are not too many, removing irrelevant data/variables,
naming/renaming variables, etc.
Step3: Data analysis and report writing: Analyse the data and write a short report. (your report
should be approximately 1,000 words, excluding Tables, Figures/charts and references
). Your report
should interpret/discuss the results with a view to answering the questions that you set out in step 1.
Your analysis and report should include the following:
(a) A selection of descriptive analytics (numerical measures) appropriate for your data
, questions or
information required from the data –
see seminar 1. Note: make your own table(s) and populate
them using a selection of key or relevant results from Excel results output – do simply copy and
paste everything from Excel results output. All tables must have a number and title e.g., “Table
1: “title” and referred to in the report e.g., “as shown in Table 1 …….”
(b) A selection of descriptive analytics (data visualization) appropriate for your data, questions or
information required from the data –
see seminar 2. Ensure that any data visualisations
(figures/charts) are well presented –e.g., all figures/charts must have a number and title e.g.,
“Figure 1: “title” and the axes should be named, where applicable. Refer to the figures/charts in
the report e.g., “Figure 1 shows that….”
(c) Predictive analytics (regression analysis) – see seminar 4. Note: Here, do not simply copy and
paste (unedited) Excel results output – make your own tables and populate it using a selection of
relevant regression results from Excel. Follow examples in Seminar 4 as a guide to how to
summarise or report regression results in a table.
Of course, you may include any other data analytics techniques that you learnt in the seminar series,
appropriate for your data, questions or information required from the data.
Note: You should use ONE dataset to address all the questions you set out in step 1, rather than
sourcing different datasets for each question.

More guidance on writing the report
Your report should be structured around your answers to the questions you set out in step 1. That is, the
questions and your answers should form sub-sections of your report. Essentially, the report is a synthesis
of the results of your analysis (step 3). It should be well presented and structured and guided by the
principles of good academic writing (see lecture notes on the topic “Tips on academic writing”).
Importantly, the report should be coherent and logically flow through the steps above i.e., from
questions (step 1) through to analysis of data to effectively synthesising the results with a view to
answering the questions, and finally drawing some conclusions (step 3). Again, structure your report
around your answers to the questions you set out in step 1.
Box 1: Guidance on data and sources

There are many sources of data of raw data. Ensure you appropriately acknowledge your data source.
Below are some sources of data (this is not exhaustive list; of course, you may use your own sources:
(1) The “
UK Data Service” is one of the University library’s databases for data from a wide range of
sectors. Researchers may access open data collections without the need to register or login:
Go to university library website; under Find, click Databases, then find UK Data Service.
Alternatively, this link will take you directly to all Databases (
A-Z Databases ( Then
find the relevant database (i.e.,
UK Data Service) and login using your usual University login details
and you will be ready to search your data.

(2) Publicly available data e.g.,
World Bank:,
Office of national statistics for the UK or for your own country
(3) If you are interested in financial data, particularly stock market data, you may access historical
stock market data for any of the world’s major indices from:
(4) Kaggle, a subsidiary of Google LLC, is an online community of data scientists. Among many
things, Kaggle allows users to find and publish data sets.
(5) Of course, you may use your own data sources e.g., if you have access to data from an organisation
that you previously worked for or are familiar with. However, ensure it is good quality data.
Finally, ensure you have a reasonable sample size of (raw) data (a sample size of least 30 to around
100 observations or lines of data. You may use larger datasets if you like – the more the better!).
Needless to say, the data should be relatively recent (perhaps during the last 20 years), unless you
are using historical time series data from the most recent going back in time.
NOTE: You MUST separately upload onto blackboard your Excel spreadsheet containing your
RAW data as part of your submission. There will be a separate submission point specifically for
the data used in this assignment (separate from the main assignment submission). If you DO
NOT upload your data, your assignment will be assessed as incomplete.