This feature is currently in beta.
If you are participating in the beta and come across any issues, please email email@example.com.
The updated Data Explorer is a powerful new way to build metrics. It allows you to quickly get insights, navigate your data, and use Dashboard Filters to slice and dice all your metrics at the same time from the dashboard. The Data Explorer and the Dashboard Filters were released in beta during our Escondido launch in Jan 2020.
Since each user’s data and use cases are unique, we can’t automatically convert existing metrics that are using the current Basic Builder to the new Data Explorer.
NOTE: Dashboard-level filtering will only work with series-based chart types (i.e., Column, Stacked Column, Line, Bar, and Area), table charts, pie charts, and single value metrics.
How to Convert an Existing Metric
Here is the recommended process for updating your existing metrics to take advantage of the updated Data Explorer and Dashboard Filtering.
- Turn off the Basic Builder
- Remove transforms that limit your data.
- Select the primary date column.
- Select the calculation you want on each Value column.
Let’s dive into each one of these steps.
1. Turn on the Data Explorer
All your metrics will stay in Basic Builder until you switch it over to use the new advanced Data Explorer. That is controlled with the Chart Builder setting. To switch a metric to use the new metric builder:
- From the dashboard, click on “Edit Metric” from the metric action menu at the top right of the metric (the three dot menu).
- Go to the Chart tab and click on the ‘Chart Builder Settings’ gear icon at the bottom left.
- Check the box ‘Use Data Explorer’. This metric is now using the new advanced metric builder.
2. Remove transforms that limit your data.
Since the new metric builder allows you to change your grouping, filtering, and aggregations on the fly, we need to do those types of calculations in the Chart tab and not using transforms in the Data Pipeline.
Best practice is to split transforms into two groups, Definition transforms, and Presentation transforms.
“Definition” transforms are used to define what your data actually is. How does your company define a sale? For example, you might use a Filter transform to remove refunds. Other definition transforms could be adding a Tag Transform to label sales teams, or Formatting a Date.
“Presentation” transforms are used to specify the data you want to see in the chart and how you want it displayed. These transforms usually limit, group and remove data. For example, using the Filter transform to only see 30 days of data. Other presentation transforms could be Grouping by Week, Sorting Desc, and Pivot tables to get sliced data.
Definition transforms reduce the amount of data your metric can work with. For example, if your metric is looking at new sales, but you data is a list of transactions that include sales, refunds and upgrades, a Filter transform to remove the refunds and upgrades is a definition transform and correct. You don’t want your metric to use refunds. You are using that transform to define what a sale is.
Presentation transforms change how the data is displayed. If you do Presentation transforms in the Data Pipeline it reduces the amount of data the metric can use and can make it harder to get the insights you want. For example, if you use a Filter transform to only show 30 days of data, then when you want to see your metrics for the whole year, the metric doesn’t have access to that data, since you filtered it out. Choosing to show 30 days of data in the Chart tab instead makes it so you can change the date filter on the fly from the dashboard.
What you need to do is remove all the Presentation transforms from the Data Pipeline in the chart tab, and recreate them in the chart tab of the metric builders.
Using our earlier Filter transform examples, you would keep the Filter removing refunds, and remove the Filter restricting the date range to last 30 days from the Data Pipeline in the chart tab. You would then go to the Chart tab, and recreate that in Date Range > Display Date Range > Last 30 Days. Now on the dashboard when you select "1 Year Back" with the date range filter, the metric has access to all the data.
3. Select the primary date column, and
4. Select the calculation you want on each Value column.
In the new metric builder, we give you the ability to filter and group by date. So you need to tell Grow what date column to work off of, and how to group and calculate (aka Count, Sum, Average) for each column you want to display.
Here is how to set those two things.
- In the Chart tab, go to ‘Date Range’ and select or re-select the date column you want to use as your date range filter. This can be any date column, even if it is not charted.
- For each of your Values, select how you want it to calculate when it groups. You can pick from Sum, Count, Average, Min, Max, Median.
Why Do I Have to Do This Manually?
With any update, we try to find ways to streamline and automate the process of using the updated features. However, since each metric is set up differently and each company’s use of their data is unique, we can’t automatically change each of the settings and assume each metric is handling the data in the same way. The main areas that we cannot automate for each user is how the data is aggregated, which date columns to use, and which transforms are crucial to the definition of their data.