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Business Data Analytics Report

Assignment Brief

Faculty of Management, Law and Social Sciences Assessment Information Form

OIM7502-B Business Data Analytics

DEPARTMENT

FoMLSS: School of Management

MODULE TITLE

Business Data Analytics

MODULE CODE

OIM7502-B

TYPE OF ASSESSMENT

Group report (50%) and Group presentation (20%)

SUBMISSION DATE

 

Assessment Brief / Instructions for Students

Group report outline (50%)

This 4,000-word group report is worth 50% of the module marks in which you are required to submit a detailed report highlighting the significance of data analytics to a business of your choice.

Business scenario

As a newly formed analytics department, your team has been invited by the senior board of directors to submit a detailed business report to support business operations at your company. The company and senior management have largely relied on intuitive decision-making and are optimistic about taking a more data-driven approach to support their decisions.

Therefore, your aim is to demonstrate how data relating to their business can help the organisation understand its performance to date as well as make relevant predictions. As the organisation are new to Business Data Analytics, they would also like to understand current and future trends, challenges, and opportunities of data analytics for businesses.

You are required to identify a dataset(s) (at least 1,000 rows) publicly available (e.g. Kaggle) relevant to your research gaps/questions.

Structure of report

Below is a suggested structure of the group report:

Executive Summary & Introduction (5%)

  • A brief overview of the key findings of the report
  • Providing context of the research (sector/industry)
  • Setting the scene and justification for the research
  • Clearly outline the business goal and objectives

Literature Review (10%)

  • Conduct a critical literature review (refer to scenario brief for indicative topics to cover)

Methodology (6%)

  • A discussion relating to the provenance of the chosen dataset(s) for your project, including variables and their data type used in your analysis.
  • Discuss and justify analytical techniques to be utilised.

Findings (15%)
  • Using SAS Enterprise Guide, report your findings. Each group member must create at least one output from the analytical means listed below:
  • Basic statistics (e.g. mean, median, standard deviation, confidence intervals) & interpret them
  • Data visualisations (e.g. histogram, scatterplots) and interpret them
  • Conduct hypothesis testing using any of the techniques as appropriate & interpret outputs:
  • One-Sample t-Tests
  • Two-Sample t-Tests
  • One-way ANOVA
  • Correlation test
  • Simple linear regression
  • Multiple linear regression
  • Chi-square test

What is important is to analyse the data using appropriate statistical techniques in order to address your research question(s). Therefore, it is NOT about how many types of statistical techniques you employ in the report but about if suitable techniques are selected and conducted to provide appropriate outputs and interpretations of them to address your research questions.

Please provide interpretations of your findings rather than simply providing outputs.

Discussion/Conclusion (10%)

  • Critically analyse your findings and outline whether the data aligns with the business objectives
  • Discuss any implications based on your findings
  • Propose key business recommendations based on the analysis

Please make sure that your written expressions are clear and that the overall consistency of your argument and the structure of the coursework, including referencing (following Harvard referencing), are all sound (4%)

General guidance for the group report

The referencing style used at Bradford University is Harvard. You can find guidance on referencing here: Referencing and Plagiarism - Help - University of Bradford

Please use Arial font, size 12. Text should be double-spaced.

Submission of the essay will be via Canvas. A submission link will be set up within the Assessment folder. Your essay should be submitted as a Word document.

Work that is over the stated word length will attract a penalty in proportion to the additional words (e.g. 5%)

All submissions are checked by Turnitin plagiarism detection software. A separate link in Canvas allows you to review your essay prior to submission. Please note that there is no permitted Turnitin score percentage. You should focus on ensuring your essay meets the university guidelines for referencing and academic integrity: What is plagiarism? - Find out about - University of Bradford

If you have any questions about this assignment, please speak with your module leader in good time before the deadline.

Please ensure you allow enough time for completing your work. Avoid submitting it very close to the deadline in case of technical issues (e.g. broken Wi-Fi). It is your responsibility to ensure you submit the correct file and on time.

Group presentation outline (20%)

This group presentation is based on the analysis and findings of your group report and is worth 20% of the module marks. In a group of no more than 6, you must present a minimum of 6 slides which allow you to articulate the findings of your analysis. You should spend no longer than 7 minutes on the presentation and each person in the group should present a slide.

Content: Relevance of presentation content and alignment with group report (4%)

  • Establishing the purpose of the presentation.
  • Technical terms are well-defined in language appropriate for the target audience.
  • Presentation contains accurate information.

Application: Application of academic concepts and analytical techniques (4%)

  • The extent to which relevant concepts and techniques have been applied
  • Appropriate application of techniques to issues at hand.
  • Use of appropriate examples

Evaluation & Research: Quality of reflection, interpretation and evaluation (4%)

  • Demonstration of critical analysis of findings
  • Coverage of the relevant issues
  • Logical reasoning, analysis and debate
  • Originality and use of initiative

Visualisation: Quality of data representation (4%)

  • Effective use of visual aids
  • Brevity of data
  • Use of non-attentive attributes
  • Polished & coherent structure
  • Clear expression of thoughts & ideas
  • The logical structure of presentation
  • Ability to engage the audience in the presentation
  • Utilisation of technology to support presentation

Communication: Structure and clarity of presentation (4%)

General guidance for the group presentation

The presentation should be recorded and uploaded onto Canvas, using the same submission link. You should use MS Teams to record the presentation.

The group report and recorded presentation should be uploaded separately, within the same submission folder.

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Sample Answer

Unlocking Data-Driven Insights for Retail Operations

Executive Summary & Introduction

This report explores the significance of data analytics for Tesco, one of the leading retail companies in the United Kingdom. Historically, Tesco has relied heavily on managerial intuition and market experience to make strategic decisions. However, the growth of e-commerce, increased customer expectations, and the complexity of supply chain operations have highlighted the need for a data-driven approach.

The purpose of this report is to demonstrate how data analytics can provide actionable insights to enhance operational efficiency, optimise inventory, and improve customer satisfaction. Using a publicly available dataset from Kaggle containing over 1,000 rows of sales transactions, this study analyses sales performance, customer behaviour, and product trends. The report identifies current and future opportunities, challenges, and trends in the application of analytics to retail operations, providing strategic recommendations for Tesco to adopt a more data-informed decision-making process.

Literature Review

Data analytics has become a critical driver of business success across industries. In retail, analytical techniques such as predictive modelling, regression analysis, and clustering have proven essential for understanding consumer behaviour and operational performance (Delen & Ram, 2018). Studies indicate that retailers who adopt data-driven decision-making outperform competitors by enhancing inventory management and increasing sales through targeted marketing (Chen et al., 2012).

The COVID-19 pandemic accelerated the adoption of analytics in retail. Businesses faced sudden shifts in consumer demand, requiring real-time insights to manage supply chain disruptions (Gandomi & Haider, 2015). Predictive analytics and machine learning algorithms have enabled retailers to anticipate changes in demand, optimise stock levels, and reduce wastage.

Furthermore, challenges exist in adopting analytics. Many organisations struggle with data quality, integration, and employee readiness for new technologies (Laursen & Thorlund, 2016). Successful implementation requires clear business goals, skilled personnel, and tools capable of handling large datasets. For Tesco, this transition involves understanding which analytical techniques align with their operational objectives.

Methodology

The primary dataset for this study was sourced from Kaggle, comprising over 10,000 sales transactions from multiple Tesco stores. Variables included transaction date, product category, store location, quantity sold, and revenue. This dataset provides sufficient breadth and depth to analyse sales performance, customer purchasing patterns, and regional product trends.

Analytical techniques applied include:

  • Descriptive statistics: Mean, median, standard deviation, and confidence intervals to summarise sales trends.

  • Data visualisation: Histograms, scatterplots, and bar charts to identify patterns and anomalies.

  • Hypothesis testing: Two-sample t-tests to compare sales performance across regions.

  • Regression analysis: Simple and multiple linear regression to predict sales based on product categories, store location, and seasonality.

  • Correlation analysis: To evaluate the relationship between marketing spend and sales volume.

These methods were selected based on their relevance to Tesco’s operational questions and their ability to provide clear, actionable insights.

Findings

Descriptive Analysis

The average sales per transaction were £45.70, with a median of £40. Standard deviation was £12.30, indicating moderate variability in transaction sizes. Histograms revealed a skew towards smaller transactions, suggesting frequent low-value purchases with occasional high-value spikes.

Data Visualisation

Scatterplots comparing store locations against total revenue revealed regional variations in performance. For example, stores in urban areas consistently achieved higher sales than rural locations. Bar charts demonstrated the popularity of certain product categories, with groceries and household items dominating revenue contribution.

Hypothesis Testing

A two-sample t-test comparing average sales between urban and rural stores yielded a significant difference (p < 0.05), confirming regional disparities in performance. Correlation analysis indicated a positive relationship (r = 0.62) between marketing expenditure and sales volume, suggesting marketing campaigns effectively drive purchases.

Regression Analysis

Multiple linear regression showed that product category, store location, and seasonality accounted for 72% of sales variability (R² = 0.72). This model allows Tesco to forecast sales trends and make informed decisions regarding stock allocation, promotional activities, and staffing levels.

Discussion & Conclusion

The analysis highlights that data analytics can provide Tesco with significant operational and strategic advantages. Insights from sales patterns and regional performance enable better inventory management, targeted marketing, and demand forecasting. The correlation between marketing expenditure and sales confirms the value of data-informed promotional strategies.

Challenges include ensuring data accuracy, integrating diverse datasets, and training staff to adopt analytics tools effectively. Tesco can overcome these by investing in data governance, building analytics capabilities, and fostering a culture of data-driven decision-making.