Head of Analytics

Managing the recruitment of a Head of Analytics means first and foremost having a thorough understanding of the role. It is summarised here in very general terms and must be adapted to your specific context.

The Head of Analytics is responsible for designing, developing, and delivering Business Intelligence (BI) and data visualization solutions within the organization. This role focuses on transforming data into actionable insights for business teams, ensuring clarity, quality, and impact of analyses. The Head of Analytics works closely with Data & AI, IT, and business teams to meet the organization’s analytical needs.

Core Responsibilities

a. Development and Delivery of Analytical Solutions

Dashboard and Report Design

Develop interactive dashboards (Power BI, Tableau, Looker) to meet the needs of business teams (sales, marketing, operations, finance).
Automate report generation to provide real-time or periodic insights.

Data Analysis

Conduct exploratory data analysis to identify trends, anomalies, or business opportunities.
Collaborate with data scientists to integrate predictive or prescriptive models into BI tools.

b. Management of Analytics Teams

Operational Leadership

Lead a team of Data Analysts, BI Developers, and Data Visualization Specialists.
Organize work in agile mode to deliver projects on time and according to business requirements.

Skill Development

Train teams in best practices for data visualization, storytelling, and exploratory analysis.
Stay updated on emerging tools and methods (e.g., data storytelling tools, new Power BI/Tableau features).

c. Collaboration with Business and Technical Teams

Understanding Business Needs

Work closely with business teams to translate their needs into analytical solutions.
Prioritize requests based on their business impact.

Integration with Data & AI Teams

Collaborate with Data Engineering and Data Science teams to ensure data is accessible, clean, and usable.
Integrate insights from AI models into dashboards to enrich analyses.

d. Data Quality Assurance and Governance

Data and Analysis Validation

Ensure quality and consistency of data used in reports and dashboards.
Implement validation processes to avoid analysis or visualization errors.

Compliance and Security

Comply with data governance rules and regulations (GDPR, etc.).
Work with security teams to protect sensitive data.

e. Optimization of Analytical Processes

Automation and Scalability

Automate data collection, transformation, and visualization processes to improve efficiency.
Optimize dashboard performance to support a large number of users.

Impact Measurement

Define and track KPIs to evaluate the adoption and impact of analytical solutions.
Gather user feedback to continuously improve tools.

Concrete Achievements Examples

Sales Dashboard: Developed an interactive dashboard for the sales team, leading to a 15% increase in sales due to better performance visibility.
Automation of Financial Reports: Implemented an automated financial report generation system, reducing production time by 50%.
Integration of AI into BI: Collaborated with the Data Science team to integrate demand predictions into dashboards, improving forecast accuracy by 20%.