TECHNICAL REPORT ON DATA ANALYSIS OF RETAIL SALES DATA

Introduction
The dataset is mainly for retail analytics which is used for segmentation and customer analytics. The scope of the report is backed by the order-date, order-number, name, address ETC as shown in the dataset.
This is a retail analytics which is used to keep track of business operations, including sales, and inventory by collecting, analyzing, and reporting this data. The aim is to gather insights into various facets of the business, from customer behaviour and loyalty to supply chains and inventory levels, with the intention of improving every element of the business operations.
Observation
This is a Relational database built on a retail organization principle of a flat file with twenty-five number of columns, each indicating a header.
Before shaping the data, you need to visualize the final output, and ask yourself the following questions: 
1. How big is the dataset? 
2. What type of filtering is required to find the necessary information? 
3. How should the data be sorted? 
4. What type of calculations are needed? 
The data can be analyzed by filtering and sorting the data for the first step, then functions like IF, COUNTIF, and SUMIF can be used for analyzing the data, as well as creating a pivot table.
Before creating a pivot table, ensure you:
1. Format your data as a table for best results.
2. Ensure column headings are correct, and there is only one header row, as these column headings become the field names in a Pivot Table.
3. Remove any blank rows and columns, and try to eliminate blank cells also.
4. Ensure value fields are formatted as numbers, and not text, and ensure date fields are formatted as dates, and not text.

Conclusion
Filtering the data makes it easier to control what data is displayed and what is hidden. This can help with the visibility of data by narrowing down the data to within specified 
criteria and parameters, and it can also help when searching for specific pieces of data.
Sorting the data helps to organize it as text-based data (alphabetically), number-based data (numerically), and date-based (chronologically). When you sort data using these logical parameters, it makes it easier for you to conceptualize and visualize your data in a more meaningful way. 
To obtain a presentable insight, pivot tables are needed to draw useful and relevant conclusions about the organization’s data.
A pivot chart, slicers, and timelines can further be used to analyse the data.

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This Report was made by Rosemary Idoga. slack name Austen Rosie

Emmanuel Idoga
Emmanuel Idoga
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