WHAT DOES A DATA ANALYTICS MANAGER DO?

Published: September 27, 2024 - The Data Analytics Manager spearheads initiatives enhancing Sales, Aftersales, and Marketing operations, nurturing internal and external relationships to streamline goals across departments. Orchestrates data-driven projects, guiding senior leadership in strategic decision-making, while fostering a culture of growth and mentorship within the team. Acts as a pivotal leader, overseeing full-spectrum data analytics programs and providing robust project management aligned with PMI standards.

A Review of Professional Skills and Functions for Data Analytics Manager

1. Data Science and Analytics Manager Duties

  • Thought Leadership: Be a thought leader on data systems, data mining, and analysis to scale capabilities, uncover trends, and develop insights.
  • Data Integrity Assurance: Ensure and continuously improve data integrity, data accuracy, and data quality.
  • Data Handling: Get hands-on with large disparate datasets, hack through complex and messy data, and analyze it leveraging the latest algorithms and state-of-the-art techniques and tools.
  • Data Translation: Translate vast amounts of data in raw formats (both structured and unstructured data) into digestible information for stakeholders to use for actionable strategies.
  • Performance Monitoring: Oversee the building and maintaining of reports, dashboards, and metrics to monitor the performance of products.
  • Strategic Collaboration: Work with executives and stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions and help determine the strategic direction of the company.
  • Opportunity Sizing: Size opportunities to ensure product teams are prioritizing the most impactful work.
  • Problem Solving Partnership: Partner with Product and Engineering teams to solve problems.
  • Data Product Adoption: Increase adoption of data products across business partners, to drive real business value.
  • Prototype and Model Development: Responsible for prototype solutions, mathematical models, algorithms, machine learning techniques, and robust analytics to provide insights and development of data products.
  • Machine Learning Leadership: Lead a team that applies state-of-the-art Machine Learning, Deep Learning, and Natural Language Processing techniques to understand and transform how users interact with core products.

2. Data Science and Analytics Manager Details

  • Algorithm Oversight: Oversees the algorithm development process, focusing on fast model iteration and testing to measure and evaluate success.
  • Machine Learning Implementation: Implements Machine Learning and Deep Learning models into production by collaborating with Product and Engineering teams.
  • Standardization Development: Develops standardized code and processes that can be easily used by the larger team.
  • Product Decision Support: Informs, influences, supports, and executes product decisions.
  • Research and Development: Researches new technologies and methods across data science, data engineering, and data visualization to improve the technical capabilities of the team.
  • Communication Excellence: Communicates research, findings, and model results clearly and effectively to a wide audience of relevant partners, and builds meaningful presentations and analyses that tell a story.
  • Client Material Preparation: Prepares client-facing material including PowerPoint slides and charts, distilling analytical insights effectively into stories for clients.
  • Scalable Model Production: Produces models that can be scaled by the software development teams which enable self-serve analytical capabilities.
  • Data Value Unlocking: Unlocks value from data using Data Science techniques including Machine Learning, Deep Learning, and Natural Language Processing to help guide the business – everything from tactical optimizations to broad-level strategic direction that is grounded in data evidence and heavy analytical rigor.
  • Big Data Infrastructure Ownership: Designs, builds, and owns the big data infrastructure, data pipeline, and analytics platform including Data Lake, Data Warehouse, and Business Intelligence (BI) tools.
  • Solution Building: Builds stable, automated, and scalable solutions to make data available, accessible, and usable by all teams in the company in order to help drive the direction of products.

3. Data and Analytics Manager Responsibilities

  • Data Management Effectiveness: Drives the effectiveness of the data management function at Dorel Juvenile.
  • Service Model Development: Builds a multi-modal service model that meets the non-homogeneous needs of functional groups.
  • Data Centralization: Ensures the company's data is centralized into a single data lake and modeled to support data analysis requirements from all functional groups of the Company.
  • Systems Integration: Ensures that all transactional systems can communicate with the data warehouse and that production data adheres to a unified data model.
  • Vendor Collaboration: Collaborates with technology vendors and development partners to build new data capabilities in support of the Data Strategy.
  • Architecture Oversight: Oversees architecture, governance, and standards for key data technologies/tools (InfoBright, Talend, Power BI, Azure, etc.).
  • Self-service Enhancement: Supports and enhances self-service reporting and visualization through published/governed data sources.
  • Cost Management: Manages and controls costs associated with collecting, managing, and sharing data while growing the value of the data to the organization.
  • Decision-support Monitoring: Identifies and monitors integrated decision-support applications and databases to provide Dorel management with accurate and efficient access to business data.
  • Data Compliance: Establishes controls over data and information in compliance with Dorel policies and external legal/regulatory requirements.
  • Project Management: Manages data-driven projects involving timelines, vendor relationships, and budgets to ensure projects are delivered with the highest quality and within specified timeframes.
  • Data Utilization Promotion: Promotes the enterprise-wide use of data to drive organizational transformation activities.

4. Data Analytics Manager Accountabilities

  • Team Development: Builds and maintains a strong functional team through effective recruiting, training, performance management, coaching, and team building.
  • Training Design: Develops training plans for new analytics staff to ensure they understand the various data sets available for exploration.
  • Requirement Analysis: Analyzes and defines requirements, works with staff to ensure proper analysis is done to create complete and understandable requirements definitions for all analytics projects.
  • Stakeholder Liaison: Liaises with other internal departments to provide updates on project status.
  • Data Analysis: Analyzes data, generates analytic reports to support project requirements and assists with problem resolution.
  • Reporting: Prepares and presents reports to internal departments.
  • Architecture Advocacy: Champions the “target state” architecture and drives toward its realization.
  • Vision and Strategy: Drives vision and strategy for the Analytics team.
  • Data Culture Leadership: Defines and builds a strong data culture at Spin by working cross-functionally and with data leadership.
  • Business Insight: Deeply understands the business and proactively spots risks and opportunities.
  • Leadership Development: Develops the next generation of leaders.

5. Data Analytics Manager Functions

  • Initiative Leadership: Builds and drives various initiatives to support the Sales, Aftersales, and Marketing operations.
  • Relationship Management: Builds and manages relationships both internally and externally, including Marketing, Sales, Legal, Agencies.
  • Team Collaboration: Collaborates across teams to support business goals.
  • Engagement Promotion: Encourages and supports engagement efforts.
  • Communication Strategy: Drives communication that supports the Business's marketing plans.
  • Talent Development: Mentors, manages, and grows talent.
  • Data Analytics Management: Manages the Data Analytics program.
  • Strategic Advising: Guides senior leadership in formulating questions about business operations and service delivery.
  • Information Handling: Receives, manages, and responds to requests for data analytics initiatives and information.
  • Project Management: Provides project management services utilizing standards and guidelines defined by the Project Management Institute.
  • Supervisory Role: Serves as a full line supervisor.
  • Professional Development: Participates in meetings and training sessions to stay abreast of advancements in data science and project management.