Data strategy


Data governance

The origin of a Data Governance project is very diverse. From the need to adapt to regulations (e.g. RGPD) to ensuring the consistent use of data throughout the organization.

Data governance is the set of rules, responsible and information processes, which aims to get more value from data, reduce the cost of data management and take care of security, compliance and improved decision making.

Some of the challenges we can help with:

  • Project Vision Definition, Objectives and Metrics, Rules and Definitions, Data Decisions (who, what, how, when), Risk Control.
  • Users who create and use data (Stakeholders), Manager who supports Data Governance (DGO) and Stewards who make data related decisions.
  • Standardized, documented and repeatable processes related to data management.

Use Cases Data Analysis

When the organization has already integrated the data into an analytics repository and has implemented data quality processes, it is time to define an analytics work plan and size the resources and time required.

Identifying and prioritizing those analytics projects that will bring the most value to the organization with the least effort is a process in which several technical and business areas of the company must participate.

Some of the challenges we can help with:

  • Identify Business Initiatives, Objectives and KPIs.
  • Identify the People that may be impacted or may condition the analytical models. Find business and technical experts who can help with their expertise.
  • Identify Data Sources and Variables that we believe can help create the analytical model.
  • Prioritize cases by value and effort, identify dependencies, impediments and risks.

Design of technological environments

When implementing new systems (ERP, CMS, CRM, PIM, DAM…) we must ensure that the systems are properly integrated into the existing technological environment.

Understanding what technologies we have implemented and drawing which tools should be incorporated is not an easy task. These projects must also be approached from data management, process efficiency and team capabilities.

Some challenges we can help with:

  • Define and prioritize the “real” requirements.
  • Define use cases and review functionalities.
  • Evaluate the scalability and modularity of the tools.
  • Development of the Business Case of a solution.
  • Roadmap and implementation time.

Data Team Design

If you are creating a new Data/Digital unit or incorporating new tools into the company’s architecture, you need a team with the right profiles.

Knowing how to create and retain a good team is critical to the success of the project. “Individuals and interactions over processes and tools” (Agile Methodologies Manifesto).

Some challenges we can help with:

  • Selection of a Team leader who can create a data culture in the company, manage expectations, and convey the scope and accomplishments of the work.
  • Create a Team that is balanced in terms of business and technical profiles, experience, technical and soft skills.
  • Encourage collaboration, continuous learning, and mapping at the level of training, recruitment, and retention of talent.
  • Establish communication channels, methodologies, and tools to share knowledge, and resources and work together effectively.


B2C predictive marketing plan.

CRM with eCommerce, ERP, POS and dashboards integration.

PIM and DAM with integration with ERP, CMS, POS, Marketplace.

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