This page provides you with instructions on how to extract data from Selligent and analyze it in Amazon QuickSight. (If the mechanics of extracting data from Selligent 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 Selligent?
Selligent is a B2C omnichannel marketing automation platform.
What is QuickSight?
Amazon QuickSight is the AWS business intelligence tool for creating dashboards and visualizations. Users are charged per session only for the time when they access dashboards or reports. QuickSight supports a variety of data sources, such as individual databases (Amazon Aurora, MariaDB, and Microsoft SQL Server), data warehouses (Amazon Redshift and Snowflake), and SaaS sources (Adobe Analytics, GitHub, and Salesforce), along with several common standard file formats.
Getting data out of Selligent
Selligent exposes data on programs, transactional mailings, and data sources via an API. We weren't able to find any online documentation on using Selligent's API, so you'll have to work closely with the company to create code to access your data.
Loading data into QuickSight
You must replicate data from your SaaS applications to a data warehouse (such as Redshift) before you can report on it using QuickSight. Once you specify a data source you want to connect to, you must specify a host name and port, database name, and username and password to get access to the data. You then choose the schema you want to work with, and a table within that schema. You can add additional tables by specifying them as new datasets from the main QuickSight page.
Using data in QuickSight
QuickSights provides both a visual report builder and the ability to use SQL to select, join, and sort data. QuickSight lets you combine visualizations into dashboards that you can share with others, and automatically generate and send reports via email.
Keeping Selligent 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 Selligent.
And remember, as with any code, once you write it, you have to maintain it. If Selligent 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 Selligent to your data warehouse: An easier solution
As mentioned earlier, the best practice for analyzing Selligent data in Amazon QuickSight 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 Selligent to Redshift, Selligent to BigQuery, Selligent to Azure Synapse Analytics, Selligent to PostgreSQL, Selligent to Panoply, and Selligent 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 Selligent with Amazon QuickSight. With just a few clicks, Stitch starts extracting your Selligent data, 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 Amazon QuickSight.