A modern GTM Data Infrastructure
to fuel your growth
A modern GTM Data Infrastructure
to fuel your growth
As companies grow, so does complexity. Businesses stay nimble by organizing and structuring all of their product and growth data in one place.
But how do you actually do that at scale?
The answer is using the modern data stack.
As companies grow, so does complexity. Businesses stay nimble by organizing and structuring all of their product and growth data in one place.
But how do you actually do that at scale?
The answer is using the modern data stack.
How it helps
It keeps all the GTM business activities tracked and lets you dashboard whatever you want (with some massaging).
That way, someone on your team doesn't have to run around looking for your own data in various different systems, often with inaccurate outputs.
It brings all of the data into a single, centralized place which helps with defining a source of truth
Sound too good to be true? It's actually doable. Here's a great article by Segment on some of the actual tools used, and how you can build it for yourself.
Let's break down the components so you can see the value for yourself.
Step 1: Extraction
Step 1: Extraction
The modern data stack enables you to first pull all of the underlying data out of all of the tools that you use (think APIs), and place them into a data warehouse. This approach allows you to extract product data, sales data, website clicks, form submissions, and any of the data generated in the various GTM tools that you use and put them into a single location.
The modern data stack enables you to first pull all of the underlying data out of all of the tools that you use (think APIs), and place them into a data warehouse. This approach allows you to extract product data, sales data, website clicks, form submissions, and any of the data generated in the various GTM tools that you use and put them into a single location.
Step 2: Transformation
Step 2: Transformation
Once your raw data is loaded into a data warehouse, you can use modern data practices to take that raw data, and stitch it together such that you create completely new and clean data that references all of the data that you care about — regardless of where it's from.
Once your raw data is loaded into a data warehouse, you can use modern data practices to take that raw data, and stitch it together such that you create completely new and clean data that references all of the data that you care about — regardless of where it's from.
Step 3: Dashboarding
Step 3: Dashboarding
Once your data is transformed into clean tables of only the data that you care about, you can create some simple dashboards that get you all of the information you need to run your business.
This data that you now have access to is extensive - any tool that you've set up an extractor for now is right at your fingertips to create a dashboard.
Once your data is transformed into clean tables of only the data that you care about, you can create some simple dashboards that get you all of the information you need to run your business.
This data that you now have access to is extensive - any tool that you've set up an extractor for now is right at your fingertips to create a dashboard.
Step 4: Automation
Step 4: Automation
Another big benefit of centralized, clean data is automation. When you can detect website visits, demo bookings, transactions, partner referrals all in the same location you can build automations that leverage this data to send notifications from one system to another, create automations that create objects/update records in your CRM with hyper-specific data, and much much more.
Two examples shown below are using data from data vendors to update your CRM, and listening for new demo bookings to create record/enrichment in your CRM.
Another big benefit of centralized, clean data is automation. When you can detect website visits, demo bookings, transactions, partner referrals all in the same location you can build automations that leverage this data to send notifications from one system to another, create automations that create objects/update records in your CRM with hyper-specific data, and much much more.
Two examples shown below are using data from data vendors to update your CRM, and listening for new demo bookings to create record/enrichment in your CRM.