Zendesk to Tableau

This page provides you with instructions on how to extract data from Zendesk and analyze it in Tableau. (If the mechanics of extracting data from Zendesk 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 Zendesk?

Zendesk is an online customer service and support ticketing (help desk) system.

What is Tableau?

Tableau is one of the world's most popular analysis platforms. The software helps companies model, explore, and visualize their data. It also offers cloud capabilities that allow analyses to be shared via the web or company intranets, and its offerings are available as both installed software and as a SaaS platform. Tableau is widely known for its robust and flexible visualization capabilities, which include dozens of specialized chart types.

In addition to its business software, Tableau also offers a free product called Tableau Public for analyzing open data sets. If you're new to Tableau, this offering is a great way to experience Tableau's capabilities at no cost and share your work publicly.

Getting data out of Zendesk

You can extract data from Zendesk's servers using the Zendesk REST API, which exposes data about tickets, agents, clients, groups, and more. To get data on a ticket, for example, you could call GET /api/v2/tickets.json.

Sample Zendesk data

The Zendesk API returns JSON-formatted data. Here's an example of the kind of response you might see when querying for the details of a ticket.

{
  "id":               35436,
  "url":              "https://company.zendesk.com/api/v2/tickets/35436.json",
  "external_id":      "ahg35h3jh",
  "created_at":       "2017-07-20T22:55:29Z",
  "updated_at":       "2017-08-05T10:38:52Z",
  "type":             "incident",
  "subject":          "Help, my printer is on fire!",
  "raw_subject":      "{{dc.printer_on_fire}}",
  "description":      "The fire is very colorful.",
  "priority":         "high",
  "status":           "open",
  "recipient":        "support@company.com",
  "requester_id":     20978392,
  "submitter_id":     76872,
  "assignee_id":      235323,
  "organization_id":  509974,
  "group_id":         98738,
  "collaborator_ids": [35334, 234],
  "forum_topic_id":   72648221,
  "problem_id":       9873764,
  "has_incidents":    false,
  "due_at":           null,
  "tags":             ["enterprise", "other_tag"],
  "via": {
    "channel": "web"
  },
  "custom_fields": [
    {
      "id":    27642,
      "value": "745"
    },
    {
      "id":    27648,
      "value": "yes"
    }
  ],
  "satisfaction_rating": {
    "id": 1234,
    "score": "good",
    "comment": "Great support!"
  },
  "sharing_agreement_ids": [84432]
}

Loading Data into Tableau

Analyzing data in Tableau requires putting it into a format that Tableau can read. Depending on the data source, you may have options for achieving this goal, but the best practice among most businesses is to build a data warehouse that contains the data, and then connect that data warehouse to Tableau.

Tableau provides an easy-to-use Connect menu that allows you to connect data from flat files, direct data sources, and data warehouses. In most cases, connecting these sources is simply a matter of creating and providing credentials to the relevant services.

Once the data is connected, Tableau offers an option for locally caching your data to speed up queries. This can make a big difference when working with slower database platforms or flat files, but is typically not necessary when using a scalable data warehouse platform. Tableau's flexibility and speed in these areas are among its major differentiators in the industry.

Analyzing Data in Tableau

Tableau's report-building interface may seem intimidating at first, but it's one of the most powerful and intuitive analytics UIs on the market. Once you understand its workflow, it offers fast and nearly limitless options for building reports and dashboards.

If you're familiar with Pivot Tables in Excel, the Tableau report building experience may feel somewhat familiar. The process involves selecting the rows and columns desired in the resulting data set, along with the aggregate functions used to populate the data cells. Users can also specify filters to be applied to the data and choose a visualization type to use for the report.

You can learn how to build a report from scratch for free (although a sign-in is required) from the Tableau documentation.

Keeping Zendesk up to date

You've built a script that pulls data from Zendesk and loads it into your destination database, but what happens tomorrow when you have dozens of new tickets and related data?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Zendesk's API returns updated_at fields that allow you to identify new records. Once you've taken new data into account, you can set up your script as a cron job or continuous loop to keep pulling down new data as it appears.

From Zendesk to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Zendesk data in Tableau is to store that data inside a data warehousing platform alongside data from your other databases and 3rd party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Zendesk to Redshift, Zendesk to BigQuery, and Zendesk to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Zendesk data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Tableau.