Data Activation

Data Activation In The Modern Data Stack

So hot right now!

  1. All the technologies that make customer data available in downstream tools CDI (Customer Data Infrastructure), CDP (Customer Data Platform), Reverse ETL (or operational analytics), and whatever comes next.
  2. All the downstream tools where data is eventually activated — sales, marketing, advertising, and support tools.

CDI and CDP

There is some confusion between CDI and CDP primarily because a CDP is essentially a CDI that also has additional capabilities that include identity resolution visual audience builder, and destination integrations with third-party tools. Additionally, CDPs also store a copy of the data in their own data warehouse, allowing customers to access that data retroactively.

CDPs are Going Beyond

It’s worth mentioning that while CDP vendors have largely been focused on collecting and moving data, some of them also allow you to activate the data and orchestrate campaigns across multiple channels such as email and SMS.

Reverse ETL or Operational Analytics

The rapid adoption of cloud data warehouses like Snowflake, BigQuery, and Redshift has given rise to Reverse ETL — a new paradigm in data integration that enables activating data that is already stored in the data warehouse.

  • Maintain a data warehouse and ensure that data is clean and modeled (often using a transformation tool like dbt)
  • Track data from first-party apps and store it in the warehouse (using a CDI tool)
  • Ingest data into the warehouse from third-party tools (using an ELT tool)
  • And finally, write SQL to sync data from the warehouse to downstream tools (using a Reverse ETL tool)

The Unusual Suspects

There are a couple of unusual suspects that I think are worth mentioning when talking about data activation.

2022 Update: The Coming Together of CDI, CDP, Product Analytics, ELT, and Reverse ETL

👉 A lot has happened in one year in the CDP space but I definitely didn’t see this one coming: mParticle has moved into product analytics after acquiring Indicative.

  1. As explained above, CDI is a core offering of CDP vendors — if one decided to ditch their CDP completely, they will have to opt for another CDI solution and handle all the downstream dependencies which is a massive undertaking with hard-to-measure ROI.
  2. Dumping data in the warehouse is easy but preparing/transforming/modeling that data for analysis and activation purposes while maintaining data quality requires talent that is definitely not easy to find, let alone the resources needed in terms of time and money. CDPs, while not as flexible as the warehouse, have built-in capability to handle all that for the customer — for companies without dedicated data teams, the value of a CDP is hard to replace.
  3. Actually, there’s a third reason too: CDPs enable non-data teams to own their workflows end-to-end — from adding a new data source to building and syncing segments downstream. With a warehouse-only approach, even with the audience building capability of Reverse ETL tools, one will need to rely on the data team to set up a new source and wait for the data to be made available in the warehouse in the right shape before one can do anything using that data.

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store