This page provides you with instructions on how to extract data from Xero and analyze it in Google Data Studio. (If the mechanics of extracting data from Xero seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)
What is Xero?
Xero offers cloud-based accounting software for small and medium-sized businesses.
Getting data out of Xero
Xero provides a REST API that lets you get accounting, payroll, asset, and other information stored in the system. To get a list of payments from the Accounting element, for instance, you could call
GET /api.xro/2.0/Payments/. This call has four optional parameters that let you filter and sort the data being returned.
Sample Xero data
The Xero API returns data in XML format. For example, the result of a call to retrieve a particular payment might look like this:
<Payment> <PaymentID>b26fd49a-cbae-470a-a8f8-bcbc119e0379</PaymentID> <Date>2016-12-12T00:00:00</Date> <Amount>281.25</Amount> <CurrencyRate>1.000000</CurrencyRate> <PaymentType>ACCRECPAYMENT</PaymentType> <Status>AUTHORISED</Status> <UpdatedDateUTC>2017-02-20T08:22:27.847</UpdatedDateUTC> <IsReconciled>true</IsReconciled> <Account> <AccountID>297c2dc5-cc47-4afd-8ec8-74990b8761e9</AccountID> </Account> <Invoice> <Contact> <ContactID>3e1d3ba5-609a-4e10-bb1d-75b6d31ce922</ContactID> <Name>Seymour Grimes</Name> </Contact> <Type>ACCREC</Type> <InvoiceID>d3cb96c6-8f3a-45ec-a261-0a7b65d4b877</InvoiceID> <InvoiceNumber>OIT00504</InvoiceNumber> </Invoice> </Payment>
Preparing Xero data
If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Xero's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.
Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
Keeping Xero data up to data
At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.
Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Xero.
And remember, as with any code, once you write it, you have to maintain it. If Xero modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.
From Xero to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Xero data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Xero to Redshift, Xero to BigQuery, Xero to Azure SQL Data Warehouse, Xero to PostgreSQL, Xero to Panoply, and Xero to Snowflake.
Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Xero with Google Data Studio. With just a few clicks, Stitch starts extracting your Xero data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.