Responsys to Tableau

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

Oracle Responsys, a component of Oracle Marketing Cloud, lets organizations manage and orchestrate marketing campaigns and interactions with customers across email, mobile, social, display, and the web. Responsys provides cross-channel orchestration of customer touchpoints using the medium(s) customers prefer.

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 Responsys

Responsys has a REST API that you can use to get at information stored in the platform. For example, to retrieve an email or push campaign schedule, you would call GET /rest/api/v1.3/campaigns/{campaignName}/schedule/{scheduleId}.

Sample Responsys data

Here's an example of the kind of response you might see with a query like the one above.

{
    "id": 1491,
    "scheduleType": "ONCE",
    "scheduledTime": "2019-01-25 06:00 AM",
    "launchOptions": {
        "proofLaunch": true,
        "proofLaunchEmail": "someemail@a.com",
        "proofLaunchType": "LAUNCH_TO_ADDRESS",
        "recipientLimit": 3,
        "samplingNthSelection": 1,
        "samplingNthOffset": 1,
        "samplingNthInterval": 1,
        "progressEmailAddresses": [
            "email1@a.com",
            "email2@a.com"
        ],
        "progressChunk": "CHUNK_10K",
        "links": [
            {
                "rel": "self",
                "href": "/rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "POST"
            },
            {
                "rel": "createSchedule",
                "href": "/rest/api/v1.3/campaigns/test/schedule",
                "method": "GET"
            },
            {
                "rel": "updateSchedule",
                "href": "rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "PUT"
            },
            {
                "rel": "deleteSchedule",
                "href": "rest/api/v1.3/campaigns/test/schedule/1491",
                "method": "DELETE"
            }
        ]
    }
}

Preparing Responsys 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. Responsys'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. In these cases 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 Responsys 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.

Responsys lacks key fields that a script could use to bookmark its progression as it looks for updated data. However, you can create .csv or .txt files as part of a Responsys Connect data export job and use a date/time prefix or suffix in the file names. You could then set up your script as a cron job or continuous loop to get new data as it's exported from Responsys.

From Responsys to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Responsys data in Tableau 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 Responsys to Redshift, Responsys to BigQuery, Responsys to Azure SQL Data Warehouse, Responsys to PostgreSQL, Responsys to Panoply, and Responsys to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Responsys to Tableau automatically. With just a few clicks, Stitch starts extracting your Responsys 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.