What stage of the Data Maturity Journey is your company in?

Companies that are just beginning their data journeys frequently utilize a straightforward architecture geared toward data collection and activation. On the other end of the scale, businesses use machine learning to provide users with real-time interactions.

A simple framework structured in five components is provided here to assist you in choosing the right data infrastructure components for your business based on the complexity of your needs and the level of data maturity of your company.

I´d like to share four stages of the Data Maturity Journey in companies :

– Starter Modern Data Stack.
– Unbundling CDP Modern Data Stack.
– Growth Modern Data Stack.
– Machine Learning Modern Data Stack.

Different components of the Modern Data Stack will be needed based on the company’s data maturity stage.

Starter Modern Data Stack

With Starter Stack, events are streamlined so the architecture is really clean: SDKs in your website and mobile app send single user events with a standardized schema and update downstream tools.

Components required for implementing the Starter Modern Data Stack :

1. Data source.

SDK for tracking behavioral events  (APP and Website)

5. Data Activation.

-Product analytics.

-Email marketing and automation.

-CRM.

-Ad platforms.

 

Unbundling CDP Modern Data Stack

The CDP Stack introduces a “unified data layer” that addresses two fundamental data challenges that companies must solve first in order to enable more powerful data use cases:

-Leveraging a single system to create and update both customer behavioral data and customer profiles data.

-Removing the need for point-to-point or custom integration work by unifying all integration needs into a single, automated integration layer.

A unified data layer that sends consistent behavioral events and customer profile data to your entire stack and gives you accurate visibility into who your customers are and how they interact with your product.

Components required for implementing the Unbundling CDP Modern Data Stack :

1. Data source.

-SDK for tracking behavioral events  (APP and Website)

-ETL from SAAS TOOLS

2. Integration Layer.

3. Storage Layer.

4. Transformation Layer.

5. Data Activation.

-Product analytics.

-Email marketing and automation.

-CRM.

-Ad platforms.

-Customer Success.

-Account Health.

-Personalization.

-Finance.

Growth Modern Data Stack

The growth stack introduces bi-direccional data flows and centralized data storage/compute layer to address two fundamental problems caused by data silos :

Incomplete data: when data created by downstream tools is trapped in those tools, it’s impossible to have a full picture of the customer journey or a customer profile that uses every customer data point.

Complexity of activating centralized data: if you do centralize data, you get the full picture in one place for analysis, but advanced optimization requires the ability to access every customer data point to use in downstream tools.

Making the central data store (behavioral events + complete customer profiles + complete customer journeys)  accessible to every tool in the stack.

Components required for implementing the Growth Modern Data Stack :

1. Data source.

-SDK for tracking behavioral events  (APP and Website)

-ETL from SAAS TOOLS

-Event Streaming from SAAS TOOLS

2. Integration Layer.

3. Storage Layer.

4. Transformation Layer.

5. Data Activation (rETL).

-Product analytics.

-Email marketing and automation.

-CRM.

-Ad platforms.

-Customer Success.

-Account Health.

-Personalization.

-Finance.

Click here for more information on Growth modern data stack.

Machine Learning Modern Data Stack

Delivering personalized experiences in real-time to the application layer.

Components required for implementing the ML Modern Data Stack :

1. Data source.

-SDK to track behavioral events (Web+App)

-ETL from SAAS TOOLS

-Event Streaming form SAAS TOOLS

2. Integration Layer.

3. Storage Layer.

4. Transformation Layer + ML Layer.

5. Data Activation (rETL).

-Product analytics.

-Email marketing and automation.

-CRM.

-Ad platforms.

-Customer Success.

-Account Health.

-Personalization.

-Finance.

-APP and Website.

 

Eric Omwega’s idea post inspired this piece of content.

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Posted by:Fran Castillo

@francastillo