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Exploring Data

This tutorial introduces you to data exploration with Sendwise Analytics. After the tutorial you will be able to understand how to pull data into Analytics, modify a report to see more details and how to deep dive into the data. 

Where To Start?

The explore section is the starting point to deep dive into your order and shipment data. 

Click on Explore your data in the right side bar menu of Analytics.  The arrow allows to hide the menu to see the Explore in full screen. 

The Basics

To get started exploring, follow these steps:

  1. Click one or more grey fields (called dimensions) to group your data.
  2. Click one or more orange fields (called measures) to add information about those groups, such as totals and counts. In this example, we selected two measures.
  3. Click Filter, if desired, to add a filter to your report based on that field.
  4. Set the condition for any filters you’ve added. You can also click the Custom Filter checkbox in the upper right of the filters section for more flexible options.
  5. If desired, choose a visualization type, such as a column chart in this case. You can click on the gear menu in the upper right of the visualization section to customize your chart.
  6. Click Run.

A 'dimension' can be described as a group of data. Whereas a measure is information about that group of data. Imagine you want to know the number of orders (measure) per delivery to promise deviation cluster (dimension) per order creation date. In this case all the number of shipments are grouped by the delivery to promise deviation cluster.

In the Explore view you have dimensions and measurements in the categories: 

Carrier: 

master data about each carrier

Customer: 

customer data on order / shipment level

Order: 

order specific data

Shipment: 

shipment specific data

Shop: 

master data about the shop defined in shop settings

Warehouse: 

master data about the warehouses defined in shop settings

These options are just the basics. The Explore section has additional features that you can learn about in this tutorial.

Explores are the Starting Point for Exploration

For this tutorial, let’s imagine we operate an e-commerce store. The Explore page presents an Explore for looking at our e-commerce store data. An Explore is a starting point for a query, designed to explore a particular subject area. In our e-commerce store model we have explores for Users, Products, Orders (a purchase event), Order Items (the products associated with an Order), and Inventory Items (Products in inventory). For example, when we have questions about users, we probably want to start exploring from the Users explore.

The Look shown below displays the number of orders per day, by querying the Orders explore, and displaying one dimension (ORDERS Created Date) and one measure (ORDERS Count).

A “dimension” can be thought of as a group or bucket of data. A “measure” is information about that bucket of data. In this case, all order records have been grouped together by day (the dimension). Then we’ve asked for a count of orders (the measure) for each of those days.

Next let’s add another dimension by using Analytics’s query builder.

Adding More Dimensions for More Detail

Let’s see how many orders are from returning customers. Adding the dimension for ORDERS Is First Purchased (Yes/No) splits the counts between first time and returning customers.

Sorting Data

Let’s see which date has the most orders from returning customers (in other words, customers not making first-purchases). Clicking on the ORDERS Count column header under No will sort from highest to lowest. If you want to, you can sort by multiple columns by holding down shift, then clicking on the column headers in the order you would like them sorted.

Note that if you reach a row limit you will not be able to sort row totals or table calculations.

Pivoting Dimensions

Multiple dimensions are often easier to look at when you pivot one of the dimensions horizontally. Each value in the dimension will become a column in your Look. This makes the information easier to consume visually, and reduces the need to scroll down to find data.

To pivot a dimension, click PIVOT for that dimension. Before running the query, be sure that you also have included at least one unpivoted dimension and at least one measure. You can pivot additional dimensions as desired, but must always include at least one unpivoted dimension.

If there is no row of data whose value would appear in a column, that is indicated with the null symbol, a zero with a slash across. For example, on July 23rd we shipped one accessory but no active wear or blazers.

You can also sort pivoted dimensions by clicking the title of the dimension. To sort by multiple pivoted dimensions hold down shift, then click on the dimension titles in the order you would like them sorted. When sorting a pivoted measure, any rows with values in that column are sorted first followed by rows without data in that column (indicated by the null symbol.)

Displaying Totals

Sometimes a summary of your data is useful. You can add column totals to your report by clicking the Totals checkbox in the upper right, then running the report. You can also add row totals to your report, but only if you’ve added a pivot to your report:

Please note that if you’ve added row totals, and your query exceeds any row limit that you’ve set, you will not be able to sort the row totals column (though you can sort dimension and measure columns as normal). This is because you might be missing rows in your data that should be included in your totals. If you run into this issue, you can try increasing your row limit (up to 5,000 rows).

There are some cases when totals won’t be available:

  1. Totals are only available for measures (and table calculations based on measures), not dimensions.
  2. Certain types of columns won’t total, due to database limitations, or because the value would not make sense as a total. For example, you can’t add together a list of words.

Additionally, there are some things to keep in mind about how totals work in certain situations:

  1. Columns that count unique items might not add up as you expect, since the same item might show up in several categories, but only be counted as one unique item in the totals.
  2. If you’ve filtered your report by a measure, totals may appear to be too high. However, in actuality, what you’re seeing is a total for your data before the measure filter is applied. In other words, the measure filter may be hiding some data from your report, even though that data is included in the total.
  3. Similarly, if you’ve placed row or column limits on your report, and your report exceeds that limit, totals may also appear to be too high. However, what you’re seeing is a total for your data before the limits are applied. In other words, the limits may be hiding some data from your report, even though that data is included in the total.

In situations 2 and 3 above, it is possible to calculate totals only for the data you can see. To do so, you’ll need to use a table calculation, explained later in this tutorial. For a column total use sum(${view_name.field_name}). For a row total use sum(pivot_row(${view_name.field_name})).

Drilling Down Into the Data

In Analytics, every query result is the starting point for another query. Clicking on any data point will drill down, creating another query refined by the data point you clicked. In the example below, we see that August 2, 2017 has had 189 orders. Clicking on the count of 189 takes us to details about those specific records.


Drilling Deeper …

In the drill overlay, we can see all of the orders placed on June 16, 2018. From here we can:

  • Click the Explore from Here button to open an Explore that uses the fields in the drill overlay as a starting point.
  • Click the Download Results button to download the data, using the same options as shown here.
  • Click on the drillable Order ID field for an individual customer order to see all of his orders.

Conclusion

Now that you know how powerful the Analytics Explore page is for building queries, displaying results, and discovering insights through iterative searches, you might want to limit your results to just the data you’re interested in.

Filtering and Limiting Data in Analytics