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AI and data Governance: 3 steps to kick-start your journey

In a continuously evolving environment, organizations are transforming and implementing successive and rapid changes to adapt. At Sia, we call this hyper-transformation. In this context, digital transformations are not uncommon, and artificial intelligence is at the forefront of the agenda. According to the Future of Jobs Survey 2025, “[…] artificial intelligence (AI) and information processing technologies […] are expected to have the biggest impact – with 86% of respondents expecting these technologies to transform their business by 2030.”[1]

The possibilities related to AI and generative AI are increasingly known and accessible thanks to the dissemination of use cases across a range of functions (e.g., HR, finance, marketing) and sectors (e.g., health, environment, education). As organizations seek to maximize efficiency, these solutions become attractive, yet often leaders don’t know where to start.

When exploring these use cases, organizations must sooner or later position themselves strategically regarding data, which is one of the driving forces behind digital transformations: “Do we have the necessary data to achieve our ambitions, and how can we ensure its security and confidentiality?” Data is therefore a critical asset in a context of innovation and digital transformation, and indispensable when developing AI use cases.  Given this, it is key for organizations to prioritize their governance[2].

Imagine this scenario for a moment: you are considering using AI to plan your future workforce requirements. What data might you need? The answer might be internal data related to your employees – for example, number of employees, retirement forecasts, skills – in addition to external data – for example, labour market and immigration data. Factors such as the availability and quality of this data will have a major impact on your decision to move forward with this strategic workforce planning project.

3 powerful steps to accelerate your data governance journey

  • Understand the quality of your data

To define clear, coherent, and structured data governance, a crucial first step is to assess your organization’s maturity in this area. With this assessment, you can establish your vision and build a roadmap adapted to your reality, and especially to your digital transformation and AI objectives. The ultimate goal is to define a sustainable data governance model that will help you have access to quality data in a timely manner while proactively managing risks.

  • Visualize data flows

You can get started quickly by mapping the data (and the systems in which they are available). What data is being used? By whom? What data exist in silos? This overview will be extremely important in the context of data governance and your efforts in identifying AI use cases. It is a task that deserves to be carried out and updated regularly. Data mapping is a powerful visual decision-making tool for business leaders at all levels within the organization.

  • Lay the foundations for decision-making

To support data governance, you should aim to create a “data culture” within your organization. Leaders and teams who share a common understanding of data-related concepts (e.g., data definition, data governance principles) are better equipped to lead conversations, make decisions, establish strategies, and ultimately manage risks.

Regardless of the size of your organization, data governance is one of the foundations that will allow you to transform and innovate through artificial intelligence. This will also enable you to respond to market fluctuations more effectively.

Stéphanie Lavergne is an Associate Manager at Sia, a global management consulting firm. Known for her tailored approach, she helps clients navigate transformation and change by aligning strategies with their unique culture and dynamics. With a multidisciplinary background, Stéphanie has a keen interest in the impact of digital transformation on organizations.

 

[1] Source: World Economic Forum Centre for the New Economy and Society. (2025). Future of Jobs Report 2025. World Economic Forum. https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf

[2] Sia defines data governance as “A holistic set of policies and procedures that standardize the way an organization uses data in order to improve business outcomes.” Source: Sia Partners. (2020). Data Governance: Maximizing the Value of Data. https://www.sia-partners.com/en/insights/publications/data-governance-maximizing-value-data

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