Data Privacy Implementation Support

Our standard approach – Implementation of corrective measures

Sia Partners has assisted many organizations in the implementation of data privacy roadmaps, both on Local Data Privacy Laws and GDPR regulation, by proposing a standard but customisable approach,  implementation using quick wins or minimal viable compliance manual, followed by an Automation Phase.

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The goal of the phased approach is to achieve minimal compliance in the first months. Industrialization & Automation is then applied to achieve an effective and efficient solution. Phase 2 regroups all major IT impacts.

Key Success Factors


Identify an efficient and accurate data cluster to initiate the setting up of the framework

Reduce the scope of data at the beginning of the project and expend it over time with a trajectory that has been validated


Document the data flows and produce a reporting of the Data Privacy level measured

The first results of Data Privacy measures will follow the identification of controls, in the main critical data batch that has been identified


Use as much as possible the existing tools, controls, actors, committees and documents

Many data privacy components are already in place within the company. They have to be used in order to simplify the accession of Data Privacy actors


Define an efficient governance across the company

Data Privacy is a transversal subject across the company. A Data protection officer has to rely on a data privacy network of correspondents


Nominate a sponsor for the Data Privacy Project

The quantity of actor in a Data Privacy governance makes the decisions difficult.

A sponsor will take the decisions about Data Privacy issues


Communicate throughout the project to make as easy as possible the accession of actors

The communication in a Data Privacy project is essential. Newsletters, training sessions and Data Privacy reports can be produced on a regular basis


Ensure the availability of operational teams

Many works have to be led with operational teams (identification of the processes and the controls, definition of automation rules…)


Analyse the opportunity to industrialize some data flows

The automation of processes and the implementation of Data Privacy controls has to be considered. They improve significantly the quality of the data

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