ECO9040, MGT9703: Assignment 2 Brief: From Data to Decisions: using data analytics to support decision-making

Published: 31 Mar, 2025
Category Assignment Subject Business
University University of Lincoln Module Title ECO9040, MGT9703: Decision Analysis for Managers/Managerial Decision Making

Introduction

Data-driven decision-making (DDDM) is the process of using data to inform decision-making. Data analytics is at the heart of DDDM. As discussed in the seminars, data analytics refers to the process and practice of analysing data to answer questions or to extract meaningful insights or information that an organization can use to inform its strategy and, ultimately, reach its objectives. 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.

In this assignment, your task is to independently apply data analytics techniques that you learn in the seminars to extract insights or information from data for decision making. In the context of the DIK pyramid, you may then draw out some information/knowledge from the data with a view to answering specific questions or to taking management action. Carefully, follow the steps below.

Step 1: Questions: Develop some 3 to 5 (research) questions that you seek to answer from data. The questions, data (and their answers) should be of interest to managers or to an organisation seeking to extract useful insights, information, or knowledge from data for the purpose of management decision making in your field of study or a closely related field.

Step 2: Data: Find some data relevant to the questions you seek to address in step 1 (see guidance on potential data sources in the Appendix below) and download it onto an Excel spreadsheet.You may have to “clean” the data before analysis in step 3. Data cleaning includes checking that the data is usable, ensuring that missing values are not too many, removing irrelevant data/variables, transforming existing variables, creating new variables, renaming variables, etc.

Step 3: Data analysis and report writing: Analyse the data and write a report. Your report should interpret/discuss the results with a view to answering the questions that you set out in step 1.

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