Desk.com to Tableau

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

Desk.com, owned by Salesforce, is an online help desk and customer service application aimed at small businesses. It supports email, phone, and social media channels, and provides a reporting interface for tracking ticket and agent performance.

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 Desk

Desk.com provides a REST API that lets you pull information on dozens of categories. If you wanted to get a list of topics in your system, for example, you could call GET /api/v2/topics. You can provide optional parameters to limit and sort the information returned.

Sample Desk data

Desk.com's API returns JSON-format data. The data returned for a "list topics" call might look like this:

{
  "total_entries": 2,
  "page": 1,
  "_links": {
    "self": {
      "href": "/api/v2/topics?page=1&per_page=50",
      "class": "page"
    },
    "first": {
      "href": "/api/v2/topics?page=1&per_page=50",
      "class": "page"
    },
    "last": {
      "href": "/api/v2/topics?page=1&per_page=50",
      "class": "page"
    },
    "next": null,
    "previous": null
  },
  "_embedded": {
    "entries": [
      {
        "name": "Customer Support",
        "description": "This is key to going from good to great",
        "position": 1,
        "allow_questions": true,
        "in_support_center": true,
        "created_at": "2017-10-08T18:18:06Z",
        "updated_at": "2017-10-13T18:18:06Z",
        "_links": {
          "self": {
            "href": "/api/v2/topics/1",
            "class": "topic"
          },
          "articles": {
            "href": "/api/v2/topics/1/articles",
            "class": "article"
          },
          "translations": {
            "href": "/api/v2/topics/1/translations",
            "class": "topic_translation"
          }
        }
      },
      {
        "name": "Another Topic",
        "description": "Not the first one, but another one!",
        "position": 2,
        "allow_questions": true,
        "in_support_center": true,
        "created_at": "2017-10-08T18:18:06Z",
        "updated_at": "2017-10-13T18:18:06Z",
        "_links": {
          "self": {
            "href": "/api/v2/topics/2",
            "class": "topic"
          },
          "articles": {
            "href": "/api/v2/topics/2/articles",
            "class": "article"
          },
          "translations": {
            "href": "/api/v2/topics/2/translations",
            "class": "topic_translation"
          }
        }
      }
    ]
  }
}

Preparing Desk 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. Desk.com'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.

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 Desk data up to date

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 Desk.com.

And remember, as with any code, once you write it, you have to maintain it. If Salesforce modifies Desk.com's 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 Desk.com to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Desk.com 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 Desk.com to Redshift, Desk.com to BigQuery, and Desk.com 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 Desk.com 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.