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How:Slice & Dice Library Data: Difference between revisions
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{{Stamp| | {{Stamp|2023-08-12|5.0|2.0}} | ||
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{{PageTitle|How to do data analysis<br/>Slice & dice library data}} | |||
To describe how to perform the functions involved in slicing & dicing, we will use as an example a library Orders of orders on a date of a product by a customer, so there are related libraries Customers and Products. | To describe how to perform the functions involved in slicing & dicing, we will use as an example a library Orders of orders on a date of a product by a customer, so there are related libraries Customers and Products. | ||
; Preparing the example | ; Preparing the example | ||
: If you'd like to play along | : If you'd like to play along, [[How:Add a Library|add these libraries]] and [[Library Edit screen|fields]]. Alternatively, you could just read along and apply analogous actions to your own library: | ||
:* '''Customers''' with unique entry name and the following fields: | :* '''Customers''' with unique entry name and the following fields: | ||
::* '''Name''' — single-line [[Text field]], entry name | ::* '''Name''' — single-line [[Text field]], entry name | ||
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* Customers have types and states. This will enable orders to be summarized by customer type and by location. | * Customers have types and states. This will enable orders to be summarized by customer type and by location. | ||
* Products have categories, cost, and a standard price. We can summarize by product category, and cost & standard price give us the basis for calculation of margins. | * Products have categories, cost, and a standard price. We can summarize by product category, and cost & standard price give us the basis for calculation of margins. | ||
<br/> | |||
== Identify the library to center on for data analysis == | == Identify the library to center on for data analysis == | ||
Among these libraries, the Orders library has the most potential for fruitful analysis, though there could also be useful analysis done within Customers or Products. We will focus on the Orders library. | Among these libraries, the Orders library has the most potential for fruitful analysis, though there could also be useful analysis done within Customers or Products. We will focus on the Orders library. | ||
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; Orders — Orders: Enter as many orders as you can stand, making sure to reference as many customers of different types and states as possible and products of as many categories as possible; then try some slicing & dicing, while reading the following sections. Then enter some more, until you are satisfied with what you can get out of slicing & dicing. | ; Orders — Orders: Enter as many orders as you can stand, making sure to reference as many customers of different types and states as possible and products of as many categories as possible; then try some slicing & dicing, while reading the following sections. Then enter some more, until you are satisfied with what you can get out of slicing & dicing. | ||
== Slicing & Dicing == | == Slicing & Dicing — ad hoc data analysis == | ||
Once the libraries are set up, and data is entered, the job of viewing the data and making sense of it is at hand. While viewing '''[[ | Once the libraries are set up, and data is entered, the job of viewing the data and making sense of it is at hand. While viewing '''[[Library Entries List screen|the list of library entries]]''', the following activities will give you the best possible look at the data and any wisdom that may be contained within it. | ||
; | ; Predictive filtering | ||
: If you find yourself frequently focusing on one customer, on customers from one state, or on certain products or product categories, define & store a filter for each such circumstance ahead of time. Then, during ad hoc data analysis, you can quickly pick the filter you want.<br/>In this case, let's define filters for each product category. On the '''[[Library Entries List screen]]''', define the following filters: | : If you find yourself frequently focusing on one customer, on customers from one state, or on certain products or product categories, define & store a filter for each such circumstance ahead of time. Then, during ad hoc data analysis, you can quickly pick the filter you want.<br/>In this case, let's define filters for each product category. On the '''[[Library Entries List screen]]''', define the following filters: | ||
* '''Lumber''', with field '''Product category''' having the value '''Lumber''' | * '''Lumber''', with field '''Product category''' having the value '''Lumber''' | ||
* '''Tools''', with field '''Product category''' having the value '''Tools''' | * '''Tools''', with field '''Product category''' having the value '''Tools''' | ||
; | ; Ad hoc filtering | ||
: Choose from the list of filters you've previously defined to focus in on the data you want to analyze, choose no filter to analyze all the data, or just filter on the fly.<br/> With [[Library Entries List screen#Filter Tabs|filter tabs]] (available in mobile editions starting with release 4.0), you can employ filters in an enhanced manner.<br/>It is best here to be as inclusive as possible, so don't over-filter. For now, let's not filter, or choose the Consumers filter or whichever filter yields you the most test data. | |||
: Choose from the list of filters you've previously defined to focus in on the data you want to analyze, | |||
; Grouping | ; Grouping | ||
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; Sorting | ; Sorting | ||
: Use sorting primarily to maintain a meaningful order of groups and of orders within groups and also to make the '''No grouping''' list the most useful. Sorting by numeric values can make the largest values stand out from the smallest, while grouping this way and that. | : Use sorting primarily to maintain a meaningful order of groups and of orders within groups and also to make the '''No grouping''' list the most useful. Sorting by numeric values can make the largest values stand out from the smallest, while grouping this way and that. | ||
; Charting | |||
: Another tool Memento provides for data analysis is charting the data. Memento charting is very flexible, providing visual views of the data. Save any charts you think you might want to use next time. | |||
This is where you view the data however you can see various aspects of the data in different ways. Generally, as time moves forward and your data grows and/or moves forward in time, you can notice trends and detect patterns that you might not have anticipated and which inform your decisions regarding the process your data represents. | |||
=== Putting it all together === | === Putting it all together === | ||