Lecture: AI Marwan - BI & Analytics Manager at StatsBomb

Introduction

• Speaker: AI Marwan, BI & Analytics Manager
• Company: StatsBomb
• Topics: Role, experience, environment at StatsBomb

Background and Experience

• Education: BSc in Business Informatics
  • Combination of computer science, databases, and business studies
• Initial Career: Teaching assistant, completed a master’s degree while working
  • Focus on data mining and BI
• 10 years industry experience before joining StatsBomb
  • Freelance work for small companies
  • Various corporate roles as a business analyst and data scientist
• Specialization: Business Intelligence (BI) and analytics

Current Role at StatsBomb

• Team: Strong profiles in data analysis
• Function: Assist various business functions (marketing, sales, customer success, tech, product, operations, quality)
• Daily Work: Help teams optimize their work and decision-making through analytics

How They Assist Teams

• Reports: Daily reports for decision-making
• Analytics Projects: Comprehensive statistical analysis and machine learning projects to generate insights
• Tools Used: SQL for data fetching, visualization tools like Power BI, Tableau, statistical tools, machine learning algorithms

Working Environment at StatsBomb

• Passion: Most employees are passionate about football or sports, which drives their work ethic
• Personal Interest: AI enjoys working with data and the challenge it presents, rather than having a specific passion for football

Differences in Roles

• Business Analyst: Business-oriented, focused on analysis related to business functions (e.g., financial analysis)
• Data Analyst: Technically adept, handles large datasets, uses tools to extract and analyze data, learns business needs
• Data Scientist: Focuses on more advanced techniques like machine learning and mathematical modeling, often on longer projects

Daily Routine of a Data Analyst

• Variety: No fixed routine, varies with project phase
• Project Phases: Business understanding, Data understanding, Data preparation, Analysis, Validation, Deployment
• Collaboration: Works across different functions, helps with understanding data and making business decisions

Skills and Attributes for a Successful Data Analyst

• Technical Skills: Knowledge of SQL, databases, programming languages like Python
• Analytical Skills: Understanding statistical techniques, machine learning, problem-solving abilities
• Soft Skills: Communication, stakeholder management, critical thinking
• Continuous Learning: Staying updated with new tools and technologies, practical application

Future of Analytics and AI

• AI Concerns: Not a threat; AI is a tool for data analysts and data scientists to solve business problems
• Automation: Focus on automating routine tasks to free up time for creative problem-solving

Career Progression

• Potential Paths: Technical (CTO) or Business-Oriented (VP of Business Operations)
• StatsBomb Initiatives: External internships for students, internal programs for employees to transition into data analytics roles
• Advice: Combine academic learning with practical application, adapt to industry needs, be proactive in problem-solving

Final Thoughts

• Career Satisfaction: Ensure continuous personal development, tackle new challenges, find a role that aligns with personal interests and skills
• Company Initiatives: Encourage internal mobility and development to retain talent
• Opportunities: Keep an eye out for internships and roles at companies like StatsBomb through their website and LinkedIn