A data table is a structured data collection organized into rows and columns. It is utilized for data storage and organization. Each row represents a record or an instance, while each column contains the data of a particular type, representing the attributes or fields of the record. From a business perspective, the composition of a data table intertwines with business processes and objects. Each row corresponds to a business process (also referred to as a record), while each column corresponds to a business object.
Count: FineBI provides you with the Count field to represent the number of records/rows in a data table. Besides, you can also switch the conditions on which the Count field depends (based on the deduplicated row number of a certain field). For details, see Count.
Field Type: As mentioned in the document, from a business perspective, if the data of each column corresponds to the same business object, the field type of those data will also be consistent. Therefore, FineBI introduces the concept of a field type. The field type in FineBI includes Text, Value, and Date, which supports mutual conversion.
For details, see Field Settings.
Tool
Minimum Data Cell
Data Feature
Application Scenario
Excel
Cells
The requirements for the standard of an Excel table are
low.
1. A cell is an intersection within a two-dimensional grid
used for data storage and display.
2. Data in each cell can be of different types.
3. A cell can be referenced by using addresses
(e.g., A1, B2, etc.). Data in cells can be calculated
through formulas.
4. Data at the cell-level can be edited.
1. Excel is mainly used for data analysis, simple calculations (especially cell-level
calculations), table and chart creation,
and so on.
2. Excel is suitable for the simple
calculation tasks of small-scale data.
FineBI
Field columns
The requirements for the standard of a FineBI table are high.
1. A table or field column is an independent unit
containing data used for visualization analysis and
report presentation.
2. Data in the same field column in a table must have
the same type.
3. Formulas or calculations are all for one column of
data.
4. Cell-level data in a data source table can not be
edited/modified after the table is uploaded to FineBI.
1. FineBI supports easier chart
integration, data calculation in batches,
and auto-update.
2. FineBI is suitable for the calculation
and display of large-scale and
multi-source data.
1. Concepts
A dimension refers to the perspectives from which data are viewed or problems are analyzed. It is typically represented by a date or text field type, for example, product types and sales regions are all dimensions.
An indicator refers to the specific data to be analyzed. It is typically represented by a value field type, for example, sales revenue, gross profit, and purchase costs are all indicators.
Further understanding of the dimension and indicator
The example includes three dimensions: time, region, and product type, which serve as the perspectives from which problems are analyzed. The two indicators (sales volume and profit rate) are the answers to the problems.
As mentioned in the document, a dimension is typically represented by a date or text field type, and an indicator by a value field type. In certain special cases, there are conversions between dimensions and indicators.
For example, the annual sales revenue was initially supposed to be a value field type, but in this scenario, it is treated as an analytical perspective. Therefore, the field is converted to a dimension, while the number of stores is treated as the indicator value.
2. Ways to differentiate and define the dimension and indicator fields in FineBI
For differentiation, dimension fields are marked in blue, and indicator fields in green in FineBI.
3. Scenarios for conversions between dimensions and indicators in FineBI
Converting Indicator Fields into Dimension Fields
Converting Dimension Fields into Indicator Fields
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