strategic media analytics manager

EFFECTIVE MEDIA ANALYTICS MANAGEMENT

• Leads media data analytics and ensuring its integrity, security, and proper ingestion and usage across the organization. Supports media intelligence needs while ensuring compliance with data governance frameworks and protecting critical business information.
• Collaborative: Work closely with cross-functional teams to assess data quality and correct inconsistencies in datasets (Media agency, research groups, Omniquest, CIT, CDG, CIMDA, ISDP, etc.
• Data Validation: Review structured data for accuracy and validity, ensuring columns contain the correct data types and values.
• Completeness Check: Identify missing records or incomplete data and collaborate with relevant teams to complete report submissions
• Format Verification: Ensure data is properly formatted and structured, with rows and columns in the correct order as well as initiate or apply enhancements needed for the data
• Data Remediation: Clean and transform data as necessary to meet quality standards and requirements for database or data lake ingestion.
• Data Quality Analysis: Monitor and report data quality metrics to ensure compliance with internal standards and best practices.
• Identify trends and outliers. Identify data trends in large datasets and communicate findings through clear visualizations and presentations.
• Documentation: Maintain comprehensive documentation of data reports, issues, remediation steps, and validation processes.

STRATEGIC OMNI-CHANNEL PLANNING ENABLER

• Enable the planning team to maximize the tool and be more strategic in analyzing and using historical, competitive, correlation and predictive data so that omni-channel planning guidance is provided to key corporate stakeholders (such as Strategic Marketing and Area Business Teams, Corporate Digital Teams, Agency partners), apply best practices;  pilot media frameworks and models; benchmarking/ measurement principles.
• Support the planning team in diligently assessing planned vs. actuals so that the team will be more accountable to committed plans
• Proactive: Strategic approach to problem-solving on data needs and data dynamism.
• Spot opportunities for portfolio management or enhancements within and across Bus
• Foresight leadership on omni-channel data-driven recommendations for different brands, categories/portfolio and/or BUs with reference to different modules of Diamond

DATA ANALYSIS & PREDICTIVE PLANNING INTEGRATION

• Leads the integration and synthesis of various data and insights from media, brand health, sales and consumer trends that will lead to scenario planning and predictive modelling

MEDIA ANALYTICS, INNOVATIONS AND STRATEGIC PARTNERSHIPS

• Enables key partnerships for capability building and learning roadmap of CMG collaborating with different Analytics Team streams
• Leads the corporate adaption of successful pilots/experiments related to Data and AI to drive upskilling and lead to maximized business value attuned to national and regional levels

LEADERSHIP AND CAPABILITY BUILDING

• Trains all stakeholders (internal and external) on use of Diamond and how to maximize data learnings
• Monitors and enables users to fully utilize the tool

CMG OPERATIONS SYNERGY

• Co-leads with Media Operations in linking Diamond with MARRS Next Gen CMG Data Tools

 QUALIFICATIONS

1. 
   
2. Bachelor's Degree graduate at minimum. Desirable fields are Research, Computer Science, Statictics and other related studies.
3. At least seven (7) years experience in a media analytics, media dashboard management, media planning and media data and performance governance role
4. SQL: Proficiency in querying databases for data validation and extraction.
5. Microsoft Excel: Advanced skills in data manipulation, analysis, and reporting.
6. Python or R: Experience with data wrangling, cleaning, and analysis using pandas, NumPy, or similar libraries.
7. Data Visualization Tools: Experience with tools such as Tableau, Power BI, or similar for reporting data quality metrics.
8. Data Integration Platforms: Familiarity with data lakes, cloud platforms (AWS, Azure, GCP), and tools such as Apache Spark or Databricks is a plus.
9. Experience with Financial Data (for roles working with financial datasets): Familiarity with financial reporting standards, data taxonomy, or industry-specific data models is an advantage.