Objectives

• Understand the need for a data collection plan
• Differentiate between the primary and secondary data
• Calculate the adequate number of samples
• Define and apply the data quality checklists
• Understand the properties of Big Data
• Recognize the benefits of Real-time data collection methods
• Understand the issues of privacy while conducting a data collection

Who Should Attend?

• Operation Managers
• Project Managers
• Financial Managers
• Data Analysis
• Urban Planners
• Transport and Traffic Engineers
• Supply Chain Managers
• Risk Managers
• Plant Managers
• Production Planners
• And everyone else who wants to learn how to gather high-quality data

Seminar Outline

DAY 1

The Importance of Data Collection 

• Historical Context
• Data Sources
• Defining the Data Collection Plan
• Determining the Sample Size Required
• Project Charter
• Common Sources of Data 

DAY 2

Collecting Data 

• Most Common Data Collection Techniques
• Conducting an Interview
• Using Questionnaires and Surveys
• Observations and Focus Groups
• The Aspects of Big Data
• Automated Techniques for Data Collection
• Data Management Strategy 

DAY 3

Examples of Use of Data Collection Techniques 

• Planning and Conducting an Interview
• Planning and Creating a Survey
• Determining Survey Scales
• Conducting Experiments
• Plan and Use Online (electronic) Surveying Tools
• Sources of Secondary Data
• Use and Referencing of Secondary Data 

DAY 4

Big Data Concepts 

• Big Data Fundamentals
• Five V’s of Big Data
• Enterprise Technologies for Big Data Collection and Analysis
• Big Data Storage and Processing
• Big Data Analytics
• Big Data Strategy
• Preserving Privacy with Big Data Applications
• Data Quality (completeness, uniqueness, timeliness, validity, accuracy, consistency) 

DAY 5 

Real-time Data Gathering and its Application 

• The Meaning of Real-time Data Gathering
• Gathering Data from RFID
• Gathering Geolocations of Mobile Phones and its Use in Urban Planning
• Multimedia Data
• Data Gathering for Risk and Uncertainty Management
• Errors and its Mitigation in Real-time Data Gathering
• New Concepts, Methodologies and Way Forward