PRINCIPAL ACCOUNTABILITIES
- To lead business analytics initiatives in line with the business objectives
- Develop business cases and proposals for analytics initiatives
- Collaborate with stakeholders to identify business needs and develop analytics solutions
- Create and communicate reports through data visualizations and present findings and recommendations to executives and stakeholders
- Identify Challenges for different Projects using insights shared by Research Analytics team and propose potential solutions for business impacts.
- Lead and Mentor a team of Analytics professionals
- Develop team goals and objectives to meet organizational goals
MAJOR CHALLENGES
- Data Quality Accessibility: Data silos, Data Quality issues, Data privacy and security
- Evolving business needs: Rapid changes and Shifting priorities
- Technical complexity: Advanced Analytics and Tool Proficiency
- Integration with businesses processes: Resistance to change and alignment with strategic objectives
- Bias in data and Privacy concerns
DECISIONS
- Decisions by Analyst: Data Analysis and Visualization, Ad-hoc analysis, dashboard design, data quality governance, Tool selection, Team management
- Decisions that require Superior approval or inputs: Strategic initiatives, Resource allocation, Major investments, Organizational changes, external partnerships, and high impact decisions.
- Areas of collaboration: Setting priorities, resource allocation, stakeholder management and decision making
INTERACTIONS
Internal Clients
- Leadership team: To understand their strategic priorities and provide data driven insights
- Functional heads: To identify data needs, share insights and ensure alignment with business objectives
- Data Engineers and Analysts: For data collection, cleaning and preparation
- DE/IT Vertical: For ensuring data infrastructure, security and access
- Subject Matter Experts: To gain valuable insights and context for data analysis
External Clients
- Industry experts: To gain valuable insights and best practices
- Data providers: To supplement internal data and broaden the scope of analysis
- Academia: To facilitate access to academic research and expertise
- Regulatory bodies: To understand industry trends, regulations and compliance requirements
- Competitor analysis: To gain insights for competitive intelligence by gathering information on competitor s strategies, products and market share
DIMENSIONS
Financial Dimensions
- Budget: Allocated budget for analytics projects and initiatives
- ROI: Return on Investment from data-driven decision projects
- Cost Savings: Savings from Process optimization or cost reduction initiatives
- Revenue growth: Increase in revenue attributed to data-driven insights and strategies
- Profit Margin: Improvement in profit margin due to data-driven decisions
Other Dimensions
- Team size and Expertise: Number of team members, skill sets and experience levels
- Data Volume: Volume of data processed and analyzed
- Data sources: Number and variety of data sources used
- Data Quality: Data accuracy, completeness, and consistency metrics
- Analytics projects: Number of Analytics projects completed per year
- Decision impact: Number of strategic or operational decisions influenced by data-driven insights
- Technology adoption: Adoption rate of new analytics tools and techniques
- Industry benchmarks: Comparison of performance metrics against industry benchmarks
Educational Qualifications
Essential: B.Tech / B.E. / BA-Economics, BA-Statistics / MBA/Post Graduation preferably (Statistics / Analytics)
Desirable/ pref.: Any professional Diploma/ Certification in Statistics / Analytics (CBAP/ CCBA)
