Key Performance Indicators
Key Questions
The challenge consisted in aligning the Data and AI division with the group's strategic goals by establishing a clear mission, prioritizing impactful use cases, and building a robust organizational and governance structure to drive innovation and efficiency
In this context, Heaviside addressed several key questions:
- How can the group’s strategic objectives be defined and translated into a clear mission for the Data and AI division?
- Which use cases should be included in the Data and AI division’s roadmap, and how should they be prioritized?
- How should the data organization and governance be structured? What roles need to be introduced, and how can key data processes be effectively organized?
- What inspiring organizational models can guide the strategic decisions of the Data and AI division?
Action #1
Run of a full Maturity audit
Mission
Our goal in the maturity audit was to assess the broadcaster’s current state of data governance and AI readiness. This involved evaluating existing processes, technology infrastructure, and organizational practices to identify strengths, weaknesses, and opportunities for improvement. We provided a comprehensive roadmap to align the company’s data strategy with its business objectives
KPIs
We established key performance indicators for the maturity audit to measure progress and outcomes, such as data quality improvement (accuracy, completeness, consistency), speed and accuracy of decision-making processes, and adoption rates of new data governance practices by internal teams
Use-cases
We identified and prioritized high-value use cases for data and AI. For instance: audience segmentation and personalized content recommendations; or optimization of advertising revenue through predictive analytics
Governance
We implemented a robust data governance framework as part of our audit, defining clear roles and responsibilities for data management, establishing policies for data privacy, security, and compliance (e.g., GDPR), and creating guidelines for data usage and ethical AI practices
Action #2
Detailed benchmark
We provided an external perspective to identify inspiring initiatives and industry best practices in data governance.
Action #3
Group's ambition definition
We assisted in recalibrating the Group’s strategic goals and aligning the Data & AI division within the organization.
Action #4
Resources strategy recommendations
We recommended appropriate resources (talents, tools, organization) to address key business challenges and internal business needs
Action #5
Use Cases roadmap and key management tools and performance indicators definition
We developed a roadmap to strategically plan and implement data-driven solutions: in-depth work to identify, prioritize, and map out critical use cases while developing essential management tools to support efficient execution and maximize the value of data governance
