Synthetic Data Management: Principles for Effective Governance

By Teresa Roma, Business Line Manager Kirey Group

Synthetic data are not fake data. They are not a convenient surrogate. They are indeed fictitious data, but built on solid real foundations. Their goal is not to “invent” reality but to reproduce it faithfully, safely, and with respect for the complexity and specificity of the business phenomena they represent. In a nutshell, this is how we might define synthetic data: the new frontier of digital evolution born from the latest regulatory constraints, privacy concerns, and the growing need to feed intelligent systems with information of impeccable quality. 

Synthetic data form an ecosystem of artificial data, behaviorally indistinguishable from real data yet completely detached from any sensitive identities or references. They do not replace real data; rather, they become a key tool for accelerating innovation, reducing time-to-market, and tackling the challenges of digital transformation in a secure, scalable, and sustainable way. 

Their applications are manifold, from healthcare to financial services. Take, for example, a bank that wishes to undertake a dynamic-pricing project: here, synthetic data allow analysis of customer behaviors without exposing sensitive information, speeding up experimentation and ensuring full compliance. 

Representativeness is key: synthetic data must be a behaviorally coherent translation of real data, replicated for precise purposes. With this in mind, synthetic data management requires strong governance. 

The New Data Challenge: Governing Data—Including Synthetic Data  

Generating synthetic data requires expertise, methodology, and awareness. It involves designing faithful representations of business processes, maintaining consistency with metadata and corporate identity through precise know-how, model tuning, and careful evaluation. Otherwise, one risks generating not an asset but an artifact that, if poorly built, can even leak sensitive information.

A Rigorous Methodology: Innovation Cannot Be Improvised

Creating synthetic data must always begin with an in-depth study of real data, which must be clean, certified, and representative, so as to model behaviors, habits, and correlations through advanced statistical techniques and generative algorithms. 

A rigorous, replicable process can be outlined in five phases: 

1. 1. Cleaning and Certification of the Source Data
      No synthetic data can be reliable if the real data from which it originates are not clean, coherent, and governed. This means clearly defining metadata, semantics, and context of use. The data must be “officially recognized” by the organization.
   2. Statistical-Phenomenological Analysis
      This is the most delicate phase: studying the phenomena described by the data (purchasing behaviors, browsing flows, operational sequences, etc.) for extracting their underlying statistical structure. This is where the data’s “identity card” is created.
   3. Design of Generative Algorithms
      Algorithms (GANs, probabilistic simulations, agent-based modeling, etc.) are chosen and configured to generate coherent data. The focus is not only on form but on data dynamics.
   4. Validation of Statistical Coherence
      The synthetic data are compared with the real data using measures of similarity, distribution, and correlation to verify that they replicate behavior and not just the appearance of the source data.
   5. Labeling and Documentation
      Every synthetic datum—even if anonymized—must be identifiable as such. It is essential to mark its synthetic origin unambiguously to ensure transparency and traceability. This marking can take the form of metadata associated with the file or record, naming conventions, tags in data-management systems, or—in more advanced cases—digital watermarking techniques. The goal is to avoid any ambiguity with real data and allow for targeted audits and analyses.

Synthetic Data Management: Culture Beyond Technology  

As you can see, the value of synthetic data lies in the technology to generate them, but also in managing their life cycle. This requires method, culture, and vision to form a genuine governance framework that defines roles, rules, and responsibilities for using synthetic data, controls their risks, and integrates them into business processes. 

In this way, synthetic data can rise from a mere “trend” to a concrete lever of responsible innovation—a bridge between the urgency of AI- and data-driven business and the protection of personal data in compliance with regulations.