Variability

Case Study – Big data technologies-based application is used to process, analyze and store the supply chain industry data. The ever-increasing importance of supply chain industry has brought together numerous challenges. Specifically, challenges are in collecting, processing, analyzing, and storing of data. These are due to the digitalization and automation devices have generate huge amount of data in the supply chain's applications. The supply chain industry applications collect data from multiple participants such as manufacturers, retailers, vendors, etc. The data is useful for planning, sourcing and development, execution, delivery and return of products. Currently, most of the supply chain applications still utilize traditional data processing systems to monitor and manage the real-time logistics tracking such as for sales numbers, inventory levels, delivery time, vendor, and shipping details, to name a few. In addition, most of the applications do not employ machine learning…

Case Study – Big data technologies-based application is used to process, analyze and store the supply chain industry data. The ever-increasing importance of supply chain industry has brought together numerous challenges. Specifically, challenges are in collecting, processing, analyzing, and storing of data. These are due to the digitalization and automation devices have generate huge amount of data in the supply chain's applications. The supply chain industry applications collect data from multiple participants such as manufacturers, retailers, vendors, etc. The data is useful for planning, sourcing and development, execution, delivery and return of products. Currently, most of the supply chain applications still utilize traditional data processing systems to monitor and manage the real-time logistics tracking such as for sales numbers, inventory levels, delivery time, vendor, and shipping details, to name a few. In addition, most of the applications do not employ machine learning…

PLEASE HELP! Introduction Conditional formatting is a powerful tool for data visualization and analysis, as it helps you focus on the most relevant information and enhances the overall clarity of your Excel spreadsheets. Questions In what scenarios would conditional formatting be effective in highlighting critical data? Which specific types of conditional formatting, encompassing both formatting choices and visualization techniques, would be most impactful in enhancing data interpretation? Feel free to provide examples and insights into the strengths and limitations of different approaches.

List and explain the steps required for data analysis

Describe how software is utilised to automate the data collecting process.

Explain Data flow analysis.

Data Preparation Perform and provide a summary of any data preparation tasks that are required before you embark on building your data visualisations, e.g. joining of files and identifying data problem

The word "parametric analysis" relates to the examination of data.

Explain the concept of OLAP (Online Analytical Processing) and its role in data warehousing for complex queries and reporting.