Make the Most of Your Gladly Data in BigQuery
Gladly and BigQuery are a natural fit for data modeling, business intelligence and data activation – especially for retail brands interested in optimizing their overall business performance, acquisition and retention marketing programs, and merchandising and fulfillment operations decisions.
Gladly is the AI customer service platform that ditches tickets to transform support heroes into revenue drivers and deliver radically personal, always-on service across all channels. Gladly turns customer service agents into heroes by giving them one lifelong, channel-independent conversation with all the context needed to build lasting connection and loyalty. BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. Use built-in ML/AI and BI for insights at scale. Together, the software application and cloud data tooling provide business and data practitioners with an opportunity to analyze and optimize shopper engagement, clienteling and personalization to drive profitable growth.
Connect to Gladly
The first step toward useful, modeled Gladly data in BigQuery is to connect the source and destination systems. There are many legacy tools available in market that handle the ETL or ELT transfer of Gladly data to BigQuery, and there are emerging tools that accomplish this transfer while providing value-added services like local data logging, and semantic data labeling and mapping along the way – making Gladly data modeling, analytics and activation easier once the data is landed in BigQuery.
To connect to Gladly, follow these easy steps
Open SoundCommerce in any browser. Open the “Intelligent Pipeline” application from the top right navigation menu. Select “Sources” from the left navigation menu. Choose “Add New Source” from within the Sources pane to open the data source library.
Search or browse to find “Gladly” within the data source library.
Complete the “Connection Setup” form with your credentials and token to securely connect to Gladly and begin collecting source data.
Log Gladly Data for Flexible Modeling in BigQuery
There are a few more considerations to address along the way. First, what happens if Gladly is unavailable for some reason, or the data you’re expecting has been purged by Gladly? What happens when Gladly changes their API schemas and data scope? What happens if you need to reinterpret your Gladly data for a new use case in the future?
You’ll want your Gladly data immutably logged locally, just upstream of BigQuery to ensure you have the data and data flow flexibility you need to future-proof your Gladly data and models. SoundCommerce provides permanent logging of Gladly data upstream of BigQuery to ensure failover and future-proofing. Regardless of how you connect your Gladly and BigQuery data, you’ll want a data lake or event log in the middle to ensure data integrity and modeling flexibility.
Define and Label Gladly Data for BigQuery
As new technologies arise and best practices evolve, traditional integration tools like ETL and ELT data pipelines are giving way to intelligent pipelines that help prep data for BigQuery starting at ingest. Simply moving JSON from Gladly to BigQuery leaves all the work for your data team in BigQuery.
As you onboard your Gladly data into BigQuery, you’ll want to create semantic labels and metadata that describe the Gladly data for easier unification and modeling across other systems and data in BigQuery.
There are third-party solutions that will catalog your Gladly data and generate semantic labels and mappings after you’ve landed it in BigQuery. With SoundCommerce, the Gladly data is defined and labeled on its way into BigQuery instead, to avoid this costly rework later. You’ll end up with business-ready entities like orders, customers, products and campaigns, making it much easier to model your Gladly data in BigQuery.
Map Gladly Entities to BigQuery
Once the raw Gladly data has been organized into useful entities, it’s time to map the Gladly data into useful tables in BigQuery.
Why do defined and labeled entities from Gladly matter so much? The main reason is that Gladly data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw Gladly data in BigQuery without this semantic understanding means data engineering and analyst teams must do all of the heavy lifting regarding the meaning of the Gladly data and the standardization of the meaning of that Gladly data from scratch in BigQuery.
Defining, labeling and mapping the Gladly data on the way in means much less effort once the data is landed in BigQuery.
Materialize Gladly Data in BigQuery
Next, you’ll establish a secure connection to BigQuery:
Select “Destinations” from the left navigation menu. Choose “Add New Destination” from within the Destinations pane to open the data destination library.
Complete the “Connection Setup” form to securely connect to BigQuery to establish a secure destination for your labeled, mapped and modeled data.
That’s it! You now have logged, labeled and mapped data from Gladly flowing securely to BigQuery.
Model Gladly Data in BigQuery
Once you have well-formed entities from Gladly onboarded to BigQuery, it’s time to build useful analytical and behavioral models on the Gladly data – and combine the Gladly data with data sets from other systems in BigQuery for more advanced, cross-dimensional analysis.
You can build your own analytical models on the Gladly data in BigQuery using languages like SQL and Python, organized into model libraries in tools like DBT or Coalesce. With SoundCommerce, you get prebuilt analytical models for Gladly running in BigQuery, with ready access to the model source code in DBT.
Host the Modeled Gladly Data in BigQuery for Analytics
BigQuery supports reporting and visualization through a wide variety of analytics tools including Sigma, Tableau, Looker, Power BI and Microstrategy to name a few. You can build your own dashboards, tabular views and graphs in any of these tools to reveal insights about Gladly in your BigQuery models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of Gladly data out of BigQuery – so you can start making better decisions and taking better action as soon as you’ve connected Gladly to BigQuery.
Host the Modeled Gladly Data in BigQuery for Campaign and Customer Activation
Whether your marketing team uses Gladly for activation – or uses other tools and channels or both to take action on the data – you’ll want to be able to easily move your modeled Gladly data in BigQuery to your most important marketing applications.
If you’ve followed the steps above to properly onboard and model your Gladly data in BigQuery, it’s easy to use reverse ETL (rETL) tools like Census or Hightouch to orchestrate the data from there, or use SoundCommerce native orchestrations to push data into common channels and applications like Facebook, Instagram, TikTok, Braze, Klaviyo, Insider or Dynamic Yield to put the Gladly data in BigQuery to use!
Getting Your Gladly Data Defined, Labeled, Mapped and Modeled in BigQuery is Easy!
SoundCommerce can automate the steps necessary to bring Gladly data into BigQuery, addressing the key functions of raw Gladly data logging, Gladly semantic definitions and mappings, and pre-built Gladly data models that are analytics- and activation ready in BigQuery.
Contact us today to get started with Gladly in BigQuery!