Google Cloud Platform


Key value proposition


On partner's side

Anyone working in the digital marketing field is likely already familiar with Google’s Stack tools; by enabling more advanced marketing use cases, Google Cloud Platform can seamlessly extend your existing stack.


On fifty-five's side

However, the unlimited possibilities GCP provides must fit within your organization’s objectives, team skill sets, and budget constraints.

This is where fifty-five can help you build the relevant roadmap to achieve your objectives as quickly as possible without sacrificing either cost or quality.


The Data Platform

Implementing a Marketing data platform on top of GCP usually starts with building a solid Google Big Query asset, the star product which provides:

  • BigData as a service with frictionless query capabilities on any volume of data, no sizing or admin hastles.
  • Native export of Google marketing data to BigQuery making it possible to leverage your Google Analytics in no time.

fifty-five offers an extensive range of ready-made use cases built on top of Google Analytics Data.

Enriching this data with additional sources is facilitated by a wide range of services managing data acquisition and addressing any specific needs, such as:

  • Real time updates with dataflow - ad hoc custom scripts run with cloud run or cloud batch
  • Event driven data acquisition with cloud functions triggered by specific events
  • Google cloud storage for flat file import/export with legacy third party systems

Infrastructure as code is a must-have to:

  • Build as many copies of this environment as there are validation stages, business units, etc...
  • Seamlessly add data processing steps in the different processing pipelines that will leverage the collected data

We can design the right environment for any data worker to develop and implement pipelines with varied levels of complexity, from a simple script automated with Cloud Run to the most elaborated pipeline using Cloud Composer based on Airflow.

The right set of components

BigQuery, Dataflow, CloudRun, Cloud SQL, Cloud Spanner, Cloud Storage, Batch

Cloud Run
Cloud SQL
Cloud Spanner
Cloud Storage

The Data Governance

We believe that a cloud setup must be designed with full adoption in mind, which is why we choose to emphasize data governance as the best way to ensure a level of adoption that justifies the required investment.

Our engineering team will help you select the correct setup, featuring:

  • Efficient cost monitoring and alerting, security, data acquisition reliability and quality
  • Fine-tuned access rights and quota definitions to safely open a platform to a full range of data workers
  • Triggers on unusual behavior
  • Built-in data acquisition workflows for instrument data exchange between marketing teams and contractors

Our consultants will contact all data stakeholders to identify and design the operating model that best suits your objectives and organization.
They will also foster adoption, a crucial step as these initiatives require significant change management efforts.

Once roles and responsibilities are shared and understood, every data worker must be provided with all the metadata and business knowledge necessary for proper data usage.
This “data discipline” needs to be applied across the whole data supply chain, from raw data to refined data sets.

This is where Google Dataplex, Looker, and Data Catalog come into play.
All these projects depend on fluid, secure, and reliable data circulation.

The right set of components

Looker, Identity & Access Manager, Stackdriver, Security Command Center, Data Catalog, Dataplex

Identity & Access Management

Data Products

Business users must work autonomously on refined data, which must therefore be self-explanatory when exposed to data workers.
Looker represents a powerful way to provide this autonomy to business users without requiring deep, detailed knowledge of the data collection process.

Gen App Builder provides an extra layer of autonomy by augmenting data access with generative AI.

Vertex AI offers a modern and integrated workspace where data scientists can build models from scratch, amend existing models selectively curated by their teams, or leverage Google ML services.

As data grows more fragmented, the marketing landscape needs to fill in gaps in modelization to overcome new challenges.
Our data science team can help you build models in-housing MLOPS capabilities. exemplifies the Google approach to frictionless model elaboration and management, which our teams can help you get the best out of.

The right set of components

Vertexai, Tensorflow Enterprise, Recommendations Ai, Document Ai, Gemini