Unit 16 Business Data Analytics and Insights (M/618/5126) Assignment Brief 2026

Unit 16 Business Data Analytics and Insights Assignment Brief

Introduction

The value of data to organisations is driving data management and governance to top-level priority in most business organisations and is generating a wealth of career opportunities and employer demand in this growing sector. Core competence in using technical knowledge to mine, inspect and interpret data before transforming it into useful information that will influence business decision-making is highly valued,  as is being able to design, develop and implement data-collection databases and processes.

This unit aims to give students an understanding of how organisations in different contexts improve their efficiency through the use of effective data management techniques. Students will look at the importance of data analysis and interpretation in informing business decision-making processes to enable organisations to stay current and competitive in a volatile macroenvironment. Students will learn how key decisionmakers, at various levels, are able to improve strategic outcomes by using more effective processes to gain an insight into the most appropriate data and information available to a business. This, in turn, informs effective business strategy.

On completion of this unit, students will have greater understanding and awareness of fundamental data analysis processes, data mining and data transformation. Broader topics such as data management ethics, legislation relating to data and using data in strategic choices will also be explored. This will enable students to develop  a career that focuses on the analysis, interpretation and effective use of data.

Learning Outcomes 

By the end of this unit students will be able to:

LO1   Analyse the contribution of effective data analytics and insight to business  decision-making processes

LO2   Apply various data analysis methods and techniques that could inform business decisions

LO3   Examine the importance of ethics and conduct in data analytics and management

LO4   Develop data management processes that allow for improved decision-making in ever-changing business environments.

Essential Content 

LO1 Analyse the contribution of effective data analytics and insight to business decision-making processes 

Defining concepts: 

Defining data analytics and data management

Key tasks in data analytics e.g. generating summary accounts, creating reports with summary descriptive statistics, application of ‘data visualisation’ tools to create graphics that convey information contained in data

Terminology in data management e.g. data mining, raw data, file formats, repositories, data modelling, data visualisation, metadata, intellectual property, access rights

Key tasks in data management e.g. building databases, uploading data to these data stores, creating backup and historical copies of files, providing ‘permissions’ to access data files.

Data types and strategy: 

Different types of data – quantitative and qualitative, structured vs unstructured  Levels of strategy – operational, tactical and strategic decision-making  Appropriateness of data types to business decision-making.

Contribution to decision-making: 

The use of data analytics in decision-making e.g. for better planning, identifying problems and opportunities, providing real-time insights, forecasting

The steps of the decision-making process e.g. identifying a decision, gathering information, assessing alternative resolutions

Relationships between ‘effective’ or ‘poor’ data analytics and strategic decision making.

Advantages and disadvantages, impact analysis, return on investment (ROI).

LO2 Apply various data analysis methods and techniques that could inform business decisions 

Gaining business insight through data interpretation: 

Data analysis tools and techniques e.g. decision tree analysis, cluster analysis, regression analysis, cross-correlations, machine learning

Data collection in research:

Different research methodologies underpinning a philosophical approach:

positivism (deductive) vs interpretivism (inductive) paradigms

Qualitative and quantitative research methods

Mixed-method approaches, including limitations and advantages

Associated tools and techniques e.g. focus groups, in-depth surveys, questionnaires.

Ethics, reliability and validity:

Role and significance of ethics in conducting research e.g. informed consent, confidentiality

Reliability of research and the degree to which research methods produce consistent results

Validity of research, extending to which results measure what they are supposed to measure

Data sources, assessing credibility, reliability and validity

Representative data, sample size, research populations

Contextualised data sets for improved evidence-based determinations.

Data presentation: 

Data formats e.g. raw, processed, statistical data

Appropriateness of visual support aids – graphs, charts, tables, narratives, drawings, scatter charts and graphics

Stakeholder analysis for presentation formats.

LO3 Examine the importance of ethics and conduct in data analytics and management 

Topical data management issues and trends: 

Data manipulation, bias in data interpretation, privacy and personal data, access and storing of data, intellectual property, use of artificial intelligence in data processing.

Corporate social responsibility and compliance: 

Government expectations of data management responsibilities e.g. information technology (IT), security techniques, information security management systems requirements (ISO/IEC 27001:2022)

Compliance and associated regulations, including worldwide data protection  and privacy legislation e.g. UK General Data Protection Regulation 2018 (GDPR).

Poor data management implications: 

Organisational values and ethics, expectations of stakeholders, public image  and branding, legal consequences.

LO4 Develop data management processes that allow for improved decisionmaking in ever-changing business environments 

Data analytics process implementation: 

Creating data management process – stages, data collection, data quality assessments, data business models, piloting and testing, process implementation, execution and monitoring and review

Infrastructure analysis for data processing, IT competencies, SWOT (strengths, weaknesses, opportunities and threats) Data flow charts and communication channels.

Creating accountability and transparency: 

Roles in data management – processors, controllers, users

Producing data management structures

Use of responsibility assignment matrices (RAM), communication platforms  to enhance transparency.

Data governance: 

Business benefits associated with data governance

Creating success metrics aligned to organisational strategy

Quality Assurance of monitoring process, Information Commissioner’s Office (ICO) guidance.

Producing data management system proposals: 

Strategic, tactical and operational recommendations

SMART (specific, measurable, achievable, realistic and timely)  implementation plans.

Learning Outcomes and Assessment Criteria 

Answer

Request Answer of this Assignment