Make the Most of Your PredictSpring Data in BigQuery
PredictSpring 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.
PredictSpring is the leading omni-channel commerce platform in the retail POS market. PredictSpring Modern POS provides a seamless in-store experience for brands with support for full POS, mPOS, cash management, clienteling, endless aisle, inventory management and curbside pickup. 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 retail ERP, WMS, POS and carrier platforms to drive profitable growth.
Connect to PredictSpring
The first step toward useful, modeled PredictSpring 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 PredictSpring 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 PredictSpring data modeling, analytics and activation easier once the data is landed in BigQuery.
To connect to PredictSpring, 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 “PredictSpring” within the data source library.
Complete the “Connection Setup” form with your credentials and token to securely connect to PredictSpring and begin collecting source data.
Log PredictSpring Data for Flexible Modeling in BigQuery
There are a few more considerations to address along the way. First, what happens if PredictSpring is unavailable for some reason, or the data you’re expecting has been purged by PredictSpring? What happens when PredictSpring changes their API schemas and data scope? What happens if you need to reinterpret your PredictSpring data for a new use case in the future?
You’ll want your PredictSpring data immutably logged locally, just upstream of BigQuery to ensure you have the data and data flow flexibility you need to future-proof your PredictSpring data and models. SoundCommerce provides permanent logging of PredictSpring data upstream of BigQuery to ensure failover and future-proofing. Regardless of how you connect your PredictSpring 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 PredictSpring 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 PredictSpring to BigQuery leaves all the work for your data team in BigQuery.
As you onboard your PredictSpring data into BigQuery, you’ll want to create semantic labels and metadata that describe the PredictSpring data for easier unification and modeling across other systems and data in BigQuery.
There are third-party solutions that will catalog your PredictSpring data and generate semantic labels and mappings after you’ve landed it in BigQuery. With SoundCommerce, the PredictSpring 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 PredictSpring data in BigQuery.
Map PredictSpring Entities to BigQuery
Once the raw PredictSpring data has been organized into useful entities, it’s time to map the PredictSpring data into useful tables in BigQuery.
Why do defined and labeled entities from PredictSpring matter so much? The main reason is that PredictSpring data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw PredictSpring 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 PredictSpring data and the standardization of the meaning of that PredictSpring data from scratch in BigQuery.
Defining, labeling and mapping the PredictSpring data on the way in means much less effort once the data is landed in BigQuery.
Materialize PredictSpring 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 PredictSpring flowing securely to BigQuery.
Model PredictSpring Data in BigQuery
Once you have well-formed entities from PredictSpring onboarded to BigQuery, it’s time to build useful analytical and behavioral models on the PredictSpring data – and combine the PredictSpring data with data sets from other systems in BigQuery for more advanced, cross-dimensional analysis.
You can build your own analytical models on the PredictSpring 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 PredictSpring running in BigQuery, with ready access to the model source code in DBT.
Host the Modeled PredictSpring 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 PredictSpring in your BigQuery models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of PredictSpring data out of BigQuery – so you can start making better decisions and taking better action as soon as you’ve connected PredictSpring to BigQuery.
Host the Modeled PredictSpring Data in BigQuery for Campaign and Customer Activation
Whether your marketing team uses PredictSpring 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 PredictSpring data in BigQuery to your most important marketing applications.
If you’ve followed the steps above to properly onboard and model your PredictSpring 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 PredictSpring data in BigQuery to use!
Getting Your PredictSpring Data Defined, Labeled, Mapped and Modeled in BigQuery is Easy!
SoundCommerce can automate the steps necessary to bring PredictSpring data into BigQuery, addressing the key functions of raw PredictSpring data logging, PredictSpring semantic definitions and mappings, and pre-built PredictSpring data models that are analytics- and activation ready in BigQuery.
Contact us today to get started with PredictSpring in BigQuery!