Data Analytics Building Center of Excellence.pptx

Building Center of Excellence for Data Analytics Practice, In detail!

1. Data Analytics Center of Excellence ( CoE ) Driving Data-Driven Innovation and Excellence in the Beverage Industry

2. Introduction Objective: Establish a Data Analytics Center of Excellence to enhance decision-making, optimize operations, and drive growth. Vision and Objectives: Transform Monster Energy into a data-driven powerhouse in the beverage industry, optimizing operations and driving innovation. Enhance Consumer Insights Optimize Supply Chain Drive Product Innovation Boost Marketing Effectiveness Governance and Structure: Governance Framework: Data Governance: Policies for quality, security, and compliance. Roles and Responsibilities: CDO, Data Stewards, Analytics Leads. Organizational Structure : Core Team: Data Scientists, Analysts, Data Engineers. Advisory Board: Key stakeholders from marketing, supply chain, and R&D.

3. Key Focus Areas Consumer Insights and Market Trends: Analyse consumer behaviour and trends. Product Performance Analytics: Track product sales and performance. Supply Chain Optimization: Improve efficiency with predictive analytics. Marketing Analytics: Measure and enhance marketing effectiveness. Innovation & R&D: Identify new product opportunities and trends. Metrics to Track : ROI : Return on investment from analytics projects. Adoption Rates : Usage of analytics tools and solutions. Project Success Rates : Success and impact of analytics projects. Customer Satisfaction : Improvements in customer satisfaction.

4. Phased Approach to Building the CoE

5. Data Analytics Center of Excellence Benefits of a Center of Excellence - Accelerate Execution - Maximize Resources - Enhance Quality - Elevate Expertise - Standardize Best Practices - Optimize Operations - Reduce Costs - Foster Innovation - Promote Collaboration Key Responsibilities of Center of Excellence Ensure Strategic Alignment and Business Results. Foster Innovation and Advanced Analytics. Facilitate Collaboration and Customer-Centric Insights. Centralize Tools, Standards, and Best Practices. Coordinate Training and Continuous Improvement. Ensure Data Governance, Security, and Compliance. Be a Central Point of Contact and Promote Agility.

6. Data analytics coe strategy: 2024 and beyond Data Analytics CoE Strategy and Value Proposition: The Analytics Center of Excellence at Monster Energy focuses on optimizing analytics across the enterprise by enhancing people, processes, tools, and metrics. It centralizes the analytics function and fosters best practices, communication, and training to drive strategic value and efficiency. Current State Top Characteristics Inconsistent Analytics: Varied quality & efficiency across departments. Underutilized Talent: Limited flexibility across departments. Data and Governance Issues: Restrictive sharing and insufficient tools, resources hinder innovation and costs. Future State Top Characteristics Centralized Analytics: Data management and expertise centralized by vertical, reporting to CDO. Collaborative Insight: Fosters shared knowledge and sets standards. Strategic Oversight: Manage data centralization, tech, and boosts IT value. Short Term Deliverables Current State: Assess tools, skills, training, and policies. Future State: Design services, structure, and governance. Roadmap: Create phased plan, budget Long Term Deliverables Centralize Data Hub: Implement with the relevant team. Train Talent: Manage cross-functional and department-specific data projects. Governance & Support Business Cases

7. IT Solutions and Tailored Managed Services 7 Pain Area & PoC Enablement Pain Area Identification Analyze key pain points and challenges to uncover data issues. Proof of Concept Demo Utilize analytics to deliver Impactful PoCs addressing pain areas. Custom Solutions: Design tailored solutions aligned with client needs. Delivery Excellence Standardized Methodologies: Implement best practices for project delivery. Reusable Assets: Develop and utilize templates and tools. Data Quality: Ensure high standards for data used in delivery. Practice Development and Skill Building Training Programs: Provide training on analytics tools and techniques. Knowledge Sharing: Foster a culture of continuous learning. Certifications: Support certification and career development. Customer Investment and Partnership Collaborative Solutions Co-develop data-driven solutions with customers. Investment Programs: Joint investment in data initiatives. Rapid Deployment: Quick deployment of analytics solutions. Strategy and Roadmap Short-term (0-6 Mos): Establish governance, launch foundational projects. Mid-term (6-18 Mos): Expand capabilities, implement advanced tools. Long-term (18+ Mos): Innovate, refine strategy, explore new technologies Data Analytics COE: Enterprise BI | Modernize Data Warehouse | Big Data-as-a-Service | Advanced Analytics | Automation Strategy and Advisory Engagement Models Data strategy workshops + define roadmap + resource models Extended Team Skillset based resource model Managed Services SLA based multiyear - engagement

8. Tools & Technology – Data analytics DATA DISCOVERY PYTHON BUSINESS INTELLIGENCE AND REPORTING BIG DATA SERVICES MACHINE LEARNING & DEEP LEARNING DATA TRANSFORMATION ANALYTICS AUTOMATION SCRIPTING SQL, NodeJS, HTML, CSS, regex, shell PYTHON 8 To be Updated, As per Client Assets

9. Questions and Discussion Conclusion and Next Steps Conclusion : Recap the value and impact of the CoE for Monster Energy. Next Steps : Formalize the CoE , gather feedback, and begin implementation.

10. Strategy and Advisory Data Strategy and Roadmap 10 Technology Assessment Align Technology Define Pilots Business Alignment Future State Definition Strategy Roadmap Strategy and transformation roadmap with best fit governance program and agile delivery milestones Perform up-front data platform needs assessment and requirements definition Apply design thinking principles for identifying and prioritizing high-value use cases that can be enabled by Advanced Analytics Help leadership teams effectively align business and technology objectives Data Strategy Governance Program Compliance Management Value Realization Phased Roadmap Pilot Use Case Assessment diagnostics Summary Key Requirements Data Management Maturity Assessment Gap Analysis Future State Use Cases Recommendations & Roadmap Final Recommendation Appendix

11. Data Analytics CoE Team Structure - Monster Energy Core Analytics Team (Centralized & Cross-Functional) This central hub drives company-wide analytics, with key roles focusing on governance, advanced analytics, and data infrastructure, designed to support global and regional operations. Chief Data Officer (CDO): Leads the CoE , ensuring alignment with Monster Energy’s global and regional strategies, focusing on optimizing the energy drink business through data-driven insights. Data Governance Lead: Oversees global data governance, ensuring quality, security, compliance (especially important in regulated markets), and proper data usage across all functions. Data Scientists: Responsible for building predictive models, customer behavior analytics, and machine learning solutions to optimize operations, marketing, and product development. Data Engineers: Build and maintain scalable data pipelines that integrate data from global supply chains, sales, and manufacturing operations to power advanced analytics across multiple regions. Business Intelligence Analysts: Focus on building user-friendly dashboards that provide real-time performance metrics, helping executives and business unit leaders make informed decisions.

12. Functional Analytics Pods (By Key Business Functions) Each pod works directly within core business functions (SCM, finance, operations, commercial, sales, marketing) while remaining connected to the central CoE to leverage common standards, tools, and infrastructure. Supply Chain Management (SCM) Analytics Pod Supply Chain Data Analyst: Tracks inventory, shipment lead times, and distribution efficiency across global markets.Logistics Optimization Specialist: Focuses on predictive analytics to optimize transportation, warehousing, and production planning across regions. Demand Forecasting Analyst: Works closely with sales and marketing to align supply with demand forecasts, minimizing stockouts or excess inventory. Finance Analytics Pod Financial Data Analyst: Provides insights on profitability, pricing strategies, and cost efficiency across markets, helping to align financial goals with business strategies. Risk & Compliance Analyst: Monitors financial risks, ensuring compliance with international regulations, especially when expanding into new markets. Operations (Manufacturing) Analytics Pod Production Efficiency Analyst: Tracks key manufacturing performance indicators like equipment efficiency, yield, and production quality across plants worldwide. Lean Manufacturing Data Specialist: Utilizes data to streamline production processes, reduce waste, and improve overall output quality. Commercial & Sales Analytics Pod Sales Data Analyst: Analyzes sales performance in different markets, tracking KPIs like market share, sales growth, and customer behavior to inform sales strategies. Trade and Channel Analyst: Monitors channel performance and evaluates promotions to optimize sales execution across retail and distribution channels. Regional Market Analyst: Focuses on localizing insights, helping regional teams adjust their strategies based on consumer preferences and market dynamics in different countries. Marketing Analytics Pod Marketing Data Analyst: Monitors the effectiveness of global marketing campaigns, media spend, and ROI, tailoring insights for each region. Customer Insights Specialist: Analyzes customer sentiment, preferences, and brand interactions across various channels, driving personalized marketing strategies. Brand Performance Analyst: Tracks brand equity and competitor positioning, ensuring Monster Energy’s brand remains strong in key markets. Regional & Global Integration Regional Data Leads Global Analytics Steering Committee Training and Development Analytics Trainer/Coach Change Management Lead