Instruction 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. In the context of the DIK pyramid, you then draw out some knowledge or what you have learnt from the data. In particular, follow the steps below.

Step 1: Develop some 3 5 questions that you seek to answer from data.

Step 2: Find some data (see guidance on data sources in Box 1 below), download it onto an Excel spreadsheet, analyse the data and interpret the results with a view to answering the questions that you set out in step 1. Note, your analysis should include (but not limited to) the following: A selection of descriptive analytics (numerical measures) appropriate for your data, questions or information required from the data

A selection of descriptive analytics (data visualization) appropriate for your data, questions or information required from the data

Predictive analytics (regression analysis)

Step 3: Write a short report, structured around your answers to the questions you set out in step 1 (approximately 1,000 words, excluding Tables, Figures/charts and references). In your report, include some tables summarising the results of your analysis (step 2) and some data visualisation in the form of figures or charts (step 2). Also ensure that you clearly tell us your source of data (e.g., if it is from Statistica, MarketLine or Financial Times) or your own sources. If you use publicly available data (e.g., from the World Bank, office of national statistics of your country, etc), ensure you appropriately acknowledge your data source.