What is Data Aggregation?

Data aggregation is one of the most central operations in data analysis. It is essentially a data split into groups based on one or more characteristics (dimensional dimensions), and then some mathematical operation (measurement) of the values within each group. For example, KiloCell of orders are grouped by “city” and “total sales” are calculated, thus helping managers to quickly understand performance in the regions.

Common polymer functions and their business values

SUM (summation): Calculates the total. For example, the calculation of the total quarterly allowance, total electricity consumption, etc.
AVG (average): Measured average. For example, the analysis of the unit price of the client, the average score of the employee KPI.
COUNT (count): Statistics frequency. For example, user-active days statistics, aggregate orders.
STDEV (standard deviation): Measuring volatility and risk. The greater the standard deviation, the greater the data distribution and the higher the operational uncertainty.
MAX/MIN (peripheral): Find the operational boundaries. If you look for a single maximum amount for each year.

Advantages of this tool: purely local BI workstations

Privacy： All the polymer logic is done through high-performance JavaScript in your local browser. Your sensitive financial data, personal information, will never leave your computer. This is safer than any cloud-based reporting tool that requires uploading files.

A very simple operation: No complex SQL statement (Group By) or Excel view table is required. A simple bottom-down selection is required to complete the multidimensional statistical analysis.

How do you make perfect syndication reports?

1. Data preprocessing: Before aggregation, it is recommended that the "data cleansing tool" of the station be used to remove empty lines and obscurities from the original data.
2. Select the right dimension: (a) The dimension field (e.g. date, sector, state) should be discrete disaggregated data; Measurement fields (e.g. price, length) should be numerical data.
3. Export and Archive: The data after aggregation is usually very refined, and it is recommended that you be inserted in your monthly presentation slide after export to CSV.