course | Big Data Principles and Practices

Introduction to Big Data
Understanding Data
•    What data is and why it matters 
•    Data, information, and insight 
•    How data is created in the workplace 
•    Internal and external sources of data 
•    Structured and unstructured data 
•    Examples of data used in everyday work 

Understanding Big Data

•    What Big Data means 
•    Traditional data versus Big Data 
•    The main characteristics of Big Data: 
o    Volume 
o    Velocity 
o    Variety 
o    Veracity 
o    Value 
•    Common misconceptions about Big Data 
•    Why Big Data is relevant to non-technical professionals 
Practical Activity
•    Identifying the data generated and used in participants’ departments 
•    Group discussion: How does data currently support our work? 
 

Business Applications of Big Data

How Organisations Use Big Data

• Understanding customers and stakeholders 
• Improving products and services 
• Supporting operational efficiency 
• Identifying problems and risks 
• Recognising patterns and trends 
• Supporting planning and forecasting 

Examples Across Business Functions

• Human resources and workforce planning 
• Finance and budgeting 
• Marketing and customer service 
• Operations and supply chain 
• Projects and performance management 
• Government and public services 

From a Business Problem to a Data Question

• Starting with the business need 
• Defining the problem clearly 
• Identifying the information needed 
• Determining how data could support the decision 
• Understanding the limitations of available data 

Practical Activity

• Converting a workplace challenge into a clear data question 
• Reviewing simple Big Data use cases from different departments 

From Data Collection to Insight
The Basic Data Journey

•    Defining what the organisation needs to know 
•    Collecting data from relevant sources 
•    Organising and storing data 
•    Reviewing and preparing data 
•    Analysing information 
•    Communicating findings 
•    Taking action and reviewing results 

Understanding Data Quality

•    Why data quality matters 
•    Accurate and inaccurate data 
•    Missing, duplicated, inconsistent, and outdated information 
•    How poor-quality data affects decisions 
•    The employee’s role in maintaining reliable data 
•    Checking information before using or sharing it 

Understanding Basic Findings

•    Reading simple tables and charts 
•    Recognising patterns and trends 
•    Understanding comparisons and percentages 
•    Identifying unusual results 
•    Distinguishing facts from assumptions 
•    Considering context before reaching conclusions 
Practical Activity
•    Identifying problems in a sample dataset 
•    Interpreting a simple workplace report 
 

Responsible Use of Big Data

Asking the Right Questions

• Where did the data come from? 
• Is the information complete and current? 
• Is the data relevant to the decision? 
• What information may be missing? 
• Are there alternative explanations? 
• Can the findings be trusted? 

Data Privacy and Security

• Personal, confidential, and sensitive data 
• Appropriate access to organisational information 
• Protecting data from unauthorised use 
• Safe storage and sharing practices 
• Responsibilities of employees when handling data 

Ethical Use of Data

• Using data fairly and responsibly 
• Avoiding bias and misleading conclusions 
• Transparency in data use 
• Balancing organisational benefits with individual privacy 
• The importance of human judgement 

Big Data and Artificial Intelligence

• The basic relationship between Big Data and AI 
• How AI uses data to recognise patterns 
• Simple workplace examples 
• Why data quality affects AI results 
• The need for human review and accountability 

Practical Activity

• Reviewing a privacy and ethics scenario 
• Group exercise: Identifying responsible and irresponsible uses of data 

Applying Big Data in the Workplace
Developing a Data-Driven Mindset

•    Using evidence to support decisions 
•    Combining data with professional experience 
•    Asking better questions about reports 
•    Sharing information across teams 
•    Avoiding common data-related mistakes 
•    Encouraging responsible data use 

Identifying a Big Data Opportunity

•    Selecting a relevant workplace challenge 
•    Defining the desired improvement 
•    Identifying available and required data 
•    Determining who will use the findings 
•    Considering data quality, privacy, and security 
•    Defining simple measures of success 
•    Identifying practical first steps 

Final Workplace Exercise
Participants will prepare and present a simple Big Data opportunity covering:

•    The workplace problem or opportunity 
•    The information required 
•    Possible data sources 
•    How the data could support a decision or improvement 
•    Expected benefits 
•    Potential risks or limitations 
•    Recommended next steps 

Course Review and Action Planning

•    Reviewing the main concepts 
•    Addressing remaining questions 
•    Identifying personal workplace applications 
•    Preparing a short individual action plan 
•    Final knowledge assessment