A Data Analytics Manager is a professional who oversees and manages the data analytics function within an organization. They are responsible for leading a team of data analysts, defining data strategies, and driving data-driven decision-making. In this blog post, we will describe the primary responsibilities and the most in-demand hard and soft skills for Data Analytics Managers.

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Main Responsibilities of a Data Analytics Manager

The following list describes the typical responsibilities of a Data Analytics Manager:

Apply

Apply statistics and AI to diverse data types to support business goals.

Build

• Build and maintain the Data Analytics & Monitoring platforms across various focus areas.

• Build reports, dashboard and data products to support day to day operations.

• Build statistical models to segment our customers and predict future revenue.

Collaborate with

• Collaborate with business units to identify opportunities for leveraging data analytics.

• Collaborate with cross-functional teams to integrate data analytics into business processes.

• Collaborate with functional leads across the company to understand their data and reporting needs.

• Collaborate with GMG team, BU and RU stakeholders to identify outcomes strategy.

• Collaborate with IT teams to ensure data security and compliance.

• Collaborate with stakeholders to understand their data requirements and challenges.

• Collaborate with the data science team on projects where data science can have a large impact.

Communicate

• Communicate confidently, effectively and clearly in both written and verbal forms.

• Communicate findings to the rest of the company and make actionable recommendations.

Conduct

• Conduct data analysis and provide insights to support decision-making.

• Conduct regular performance evaluations and provide feedback to team members.

• Conduct training and development programs for the data analytics team.

Contribute to

• Contribute and helps to manage our Precision and Digital Marketing Learning Agenda.

• Contribute to and execute the data strategy vision set by the leadership from the team.

Coordinate

• Coordinate and manage the business intelligence team's daily activities and operating budget.

• Coordinate and prioritize competing initiatives while meeting deadlines.

• Coordinate bi-weekly team meetings.

Define

• Define and implement data governance policies and procedures.

• Define and track key performance indicators (KPIs) for measuring data analytics success.

Design

• Design and build technical processes to address program issues.

• Design and implement data visualization techniques to communicate insights effectively.

• Design and implement technical processes to address business issues.

Develop

• Develop and execute the organization's data analytics strategy.

• Develop and maintain data models, algorithms, and statistical models.

• Develop and maintain strong relationships with internal and external stakeholders.

• Develop reports using generalizable themes and areas of interest.

Direct

Direct activities of junior analysts toward program goals.

Drive

• Drive and initiate data analytics projects that are cross-functional, conduct quantitative research.

• Drive the adoption of data analytics tools and technologies.

Ensure

• Ensure compliance with all regulatory and audience composition requirements.

• Ensure compliance with data privacy and protection regulations.

• Ensure data accuracy, consistency, and integrity across various data sources.

• Ensure deadlines are met by team members and vendors.

• Ensure that content is refreshed and links active and current.

• Ensure that project / department milestones / goals are met and adhere to approved budgets.

• Ensure that the highest level of the Code of Conduct is displayed in personal behavior.

Establish

• Establish consistent, objective program performance standards of accountability.

• Establish data analytics best practices and standard operating procedures.

Evaluate

Evaluate and review precision marketing strategies and optimization strategies.

Experiment

Experiment, Evaluate, Modify, and Implement.

Explore

Explore and evaluate new data sources and technologies for data analysis.

Foster

Foster a data-driven culture within the organization.

Frame

Frame and carry out statistical experiments to test impact of our new initiatives.

Identify

• Identify and prioritize data analytics projects based on business needs.

• Identify, model, and forecast key performance indicators that drive company growth.

Improve

Improve our existing data infrastructure and build out a solid dashboard stack.

Innovate

Innovate novel algorithms and tools to support research objectives.

Interact with

Interact with ad tech and IT regularly to ensure best in class results.

Lead

• Lead ad-hoc projects to optimize operational processes.

• Lead a team of data analysts, providing guidance and mentorship.

• Lead creation and roll out of new Programs data management systems.

• Lead data-driven initiatives and projects across different functional areas.

• Lead on designing and developing the data infrastructure for I& A reporting & analysis.

• Lead the strategic design and maintenance of enterprise business intelligence applications.

Liaise with

Liaise with outside data vendors when needed.

Maintain

• Maintain relevant databases.

• Maintain the production pace of other team members.

Manage

• Manage all aspects of internal analytics and reporting.

• Manage analytics team to support enterprise-wide research.

• Manage and maintain data analytics infrastructure and tools.

• Manage a pipeline of Analytics and Automation requests.

• Manage Compliance Monitoring platforms and deliver results on an ongoing basis.

• Manage cross-BU / RU outcomes strategies.

• Manage data analytics projects, including resource allocation and timelines.

• Manage data inventory and metadata library to support Compliance efforts globally.

• Manage implementation of advanced reporting tools.

Meet

Meet regularly with business and IT leaders, and maintain proactive communication with customers.

Monitor

• Monitor and manage scorecard and program dashboards.

• Monitor and optimize data analytics processes and workflows.

• Monitor industry trends and competitive landscape in data analytics.

Oversee

Oversee data collection, cleansing, integration, and analysis processes.

Own

Own and manage logistics data science projects.

Participate

Participate existing research and evaluation of key assumption used in business model.

Partner with

Partner with a cross-functional team to meet (or exceed) pre-determined KPIs.

Perform

• Perform basic visual quality inspections on products.

• Perform or effectively accommodate relevant ad-hoc analysis.

• Perform root-cause analyses on key problem areas.

Popularize

Popularize testing as a strategic learning tool and promote data-oriented culture throughout company.

Present

Present findings and recommendations to senior management and stakeholders.

Propose

Propose and maintain Resiliency indicators ensuring completeness, accurateness and relevance.

Provide

• Provide administrative support for Resiliency related processes and projects.

• Provide documentation and training for stakeholders to promote understanding and adoption.

• Provide technical assistance to stakeholders / consumers of GMG outcomes data and information.

• Provide technical guidance to the team on analytics technologies and methodologies.

Publish

Publish results and present findings at meetings.

Quantify

Quantify and challenge our business hypotheses and assumptions with data.

Recruit

Recruit, train, manage, and mentor a team of high-performing analysts.

Run

Run analyses and reports results.

Set-up

Set-up Marketing data tracking mechanism.

Stay updated with

Stay updated with emerging trends and technologies in the field of data analytics.

Support

• Support experiments for continuous improvement.

• Support for Ali Marketing Operation.

• Support internal category management for out-site & in-site traffic planning，.

Teach

Teach, nurture, and lead members within the team.

Understand

Understand and follow basic verbal and written instructions.

Use

• Use current, real-world data to test and improve our life-altering diabetes products.

• Use predictive modeling to increase and optimize value of the Analytics Solutions.

• Use statistical techniques to help people throughout the company make sense of data.

Utilize

Utilize power tools and various equipment as needed to perform duties.

Work with

• Work closely with other GMG colleagues to interpret outcomes data.

• Work with Compliance Officers to understand their top problem areas.

• Work with data engineers in a devops environment to build production-grade custom analytical tools.

• Work with diverse teams to achieve understanding of product performance in a real-world context.

• Work with your team and key stakeholders to plan, prioritize, and execute analytical projects.

Write

Write compelling abstracts and articles to report findings.

Most In-demand Hard Skills

The following list describes the most required technical skills of a Data Analytics Manager:

1. Proficiency in data analysis tools, such as SQL, Python, or R.

2. Experience with data visualization tools, such as Tableau or Power BI.

3. Knowledge of statistical analysis techniques and methodologies.

4. Familiarity with data mining and machine learning algorithms.

5. Understanding of database management systems and data warehousing.

6. Competency in data manipulation and transformation techniques.

7. Proficiency in data querying and data manipulation languages.

8. Experience with big data technologies, such as Hadoop or Spark.

9. Knowledge of data governance and data quality management.

10. Familiarity with cloud-based data platforms, such as AWS or Azure.

11. Understanding of data integration and ETL (Extract, Transform, Load) processes.

12. Proficiency in data modeling and database design principles.

13. Experience with data analytics project management methodologies.

14. Knowledge of data privacy and protection regulations, such as GDPR.

15. Competency in data storytelling and effective communication of insights.

16. Understanding of data analytics frameworks, such as CRISP-DM or TDSP.

17. Knowledge of data visualization best practices and design principles.

18. Experience with data analytics tools and platforms, such as SAS or IBM Watson.

19. Proficiency in data-driven decision-making and data-driven problem-solving.

20. Understanding of data security and encryption techniques.

Most In-demand Soft Skills

The following list describes the most required soft skills of a Data Analytics Manager:

1. Strong leadership and team management skills.

2. Excellent communication and presentation skills to effectively convey complex data insights to non-technical stakeholders.

3. Critical thinking and problem-solving abilities to identify patterns and trends in data and provide actionable recommendations.

4. Strong analytical and quantitative skills for data analysis and interpretation.

5. Collaboration and teamwork skills to work effectively with cross-functional teams and stakeholders.

6. Project management skills to plan, execute, and monitor data analytics projects.

7. Adaptability and flexibility to work in a fast-paced and evolving data environment.

8. Attention to detail to ensure data accuracy and quality.

9. Strategic thinking to align data analytics initiatives with business objectives.

10. Continuous learning mindset to stay updated with the latest data analytics techniques and tools.

Conclusion

A Data Analytics Manager plays a crucial role in driving data-driven decision-making within an organization. They are responsible for leading a team of data analysts, defining data strategies, and ensuring the effective use of data to achieve business goals. To excel in this role, Data Analytics Managers require a combination of hard skills, including proficiency in data analysis tools, statistical techniques, and data visualization, as well as soft skills, such as leadership, communication, critical thinking, and collaboration. By possessing these skills, Data Analytics Managers can harness the power of data to gain valuable insights, make informed decisions, and drive business success.