WHAT DOES A DATA ANALYST DO?

Published: May 28, 2025 - The Data Analyst collaborates with stakeholders across departments to prioritize KPIs and develop comprehensive measurement plans. This position designs, builds, and maintains reports and dashboards using tools like Hadoop, Google Analytics, R, Python, and Tableau, ensuring metric alignment with Engineering, Product, and Marketing teams. This role analyzes data from multiple sources to support data-driven decisions, manages data warehouse requirements and documentation, and presents insights to various stakeholders including executives.

A Review of Professional Skills and Functions for Data Analyst

1. Data Analyst Duties

  • Data Interrogation: Interrogating the internal and external data to provide in-depth insight into the economy and business operations
  • Data Interrogation: Interrogating the internal and external data to uncover hidden insights and drive value
  • Market Analysis: Analysing market data and building a country market strategy
  • Trial Design: Designing and building controlled live trials to test business hypotheses
  • Trial Analysis: Analysing and communicating the results of controlled trials
  • Data Analysis: Analysing the internal data to understand drivers of customer churn, lead conversion and client lifetime value
  • Model Building: Designing and building predictive models and other machine learning solutions to increase client conversion rates, forecast revenue and fill rates
  • Stakeholder Liaison: Liaising with business stakeholders to clearly identify what challenges they are facing
  • Strategy Alignment: Ensuring work is always aligned to the regional and global data and business strategy
  • Cross-Function: Liaising with in-country IT and Business Intelligence and Business professionals to ensure all logical loopholes are closed

2. Data Analyst Details

  • Data Setup: Execute master data/hierarchy set-ups & maintenance.
  • Data Cleansing: Execute database cleansing activities.
  • Pricing Validation: Validate and process all client pricing requests from sales and all client pricing agreements.
  • Data Analysis: Perform required analyses to ensure the data extracted is accurate and meets business needs, and prioritize any change activity into a program of improvements.
  • ERP Support: Provide support on data & pricing elements for all ERP systems (e.g., SAP, Qorus, ET).
  • Data Modelling: Provide modelling based on dynamic data elements available in the database.
  • Trend Analysis: Analyse trends in the master data database.
  • Cross-Functional: Participate in cross-functional teams to complete projects initiated.
  • Reporting Prep: Prepare external and internal reporting for the standard SLA.
  • Pricing Strategy: Partner with cross-functional leaders to establish a product-level pricing strategy.
  • Reporting Collaboration: Collaborate with appropriate teams to prepare internal/external reporting and determine desired report content and parameters.
  • Pricing Analytics: Maintain pricing analytics, prepare business reports, and recommend changes to support long-term product growth.
  • Data Maintenance: Support the maintenance of material master data related to pricing attribute values.
  • Price Management: Work closely with partners and product management teammates to maintain list pricing.
  • Project Management: Establish and maintain timelines and priorities for assigned projects, systems, and applications, working across multiple initiatives with minimal supervision.
  • Business Support: Provide continuous business support to users, developing and maintaining strong relationships with stakeholders across all Operations teams & business partners

3. Data Analyst Responsibilities

  • Database Maintenance: Maintains databases and keeps information current and up to date.
  • Report Development: Develops daily, weekly, and monthly management reports.
  • Plan Management: Maintains Contractor and Sub-Contractor podding plans, verifies conformance and ensures periodic updates.
  • Information Tracking: Keeps track of Shelter in Accommodation information and verifies that testing and release protocols are followed.
  • Data Analysis: Provides data analysis to improve service operations and cost efficiency.
  • Action Facilitation: Facilitates discussions to drive actions to closure.
  • Expectation Setting: Defines and communicates expectations to Business Partners.
  • Issue Reporting: Keeps BOCO informed of areas of concern that require management support.
  • Medical Liaison: Liaises with the medical group and the Quick Response Team to ensure data entry is timely and accurate.
  • Partner Liaison: Liaises with Business Partners to ensure actions are carried out in a quick and responsive manner.
  • Contact Management: Consolidates a key contact list that allows for the timely acquisition of information from Business Partners and the Medical group.

4. Data Analyst Job Summary

  • Team Direction: Provides direction to QRT that supports targeted screening of accommodation blocks and pods.
  • Data Support: Supports 3GP Services team, FGP Services and BP (CCEP & SK) and COCR with consolidating data.
  • Data Oversight: Provides data analysis oversight and process improvement.
  • Trend Identification: Identifies and communicates trends and focus areas to Construction leadership.
  • Focus Analysis: Identifies focus areas based on 7- and 14-day trending by Pod and Accommodation blocks.
  • Early Detection: Early identification and communication of infection in Pods and Accommodations based on rolling averages.
  • Threshold Alert: Informs Business Partner and BOCO when accommodation block infection rates reach threshold limits that require Shelter in Accommodation.
  • Process Improvement: Suggests a process improvement to management.
  • Action Recommendation: Recommends actions to LT based on data analytics and trending.
  • Report Preparation: Prepares and submits reports to internal and external groups.
  • Meeting Participation: Participates in alignment meetings related to C19.

5. Data Analyst Functions

  • Data Assurance: Contribute to internal quality data assurance and accuracy improvements in terminals as well as, in conjunction with the business analyst, drive automation of Maintenance & Repair (M&R) and Warehouse and Inventory reporting to enable real-time transparency of business and faster/better informed decision making.
  • Performance Analysis: Reporting directly to the Global Inventory and Warehouse Manager, analyze performance of terminals in scope, run data governance and Master Data Management checks, and facilitate decision-making based on analysis.
  • Data Improvement: Driving improvement of data accuracy and quality as well as compliance with reporting standards across terminals.
  • Team Collaboration: Work closely with the Inventory and Warehouse team leads who support implementing a consistent way of working and continuous improvement in reliability and performance.
  • Trend Analysis: Analyze performance of terminals and processes in scope to establish and understand trends for better monitoring and decision-making, ensuring process enhancement and cost reduction.
  • Reporting Accuracy: Ensure high-quality data analysis and accurate performance reporting across the portfolio.
  • Template Preparation: Prepare standard templates and ensure they are implemented and used across the team.
  • OPEX Support: Support OPEX investment business cases and assist in creating baseline and ambitious levels for Warehouse and Inventory performance.

6. Data Analyst Job Description

  • Report Generation: Generation of medical utilization and demographic reports.
  • Provider Coordination: Coordinate with insurance providers in the provision of accurate and on-time data.
  • Data Validation: Clean up and validate data and address and resolve issues.
  • Trend Analysis: Generate reports and come up with an analysis highlighting trends and cost drivers in terms of top availers, top claim types, top illnesses, and top hospitals, at the least.
  • Client Coordination: Coordinate with Client Relationship managers in the proper interpretation of findings and how best to convey results to clients.
  • Process Automation: Improve and automate report generation processes.
  • Data Compliance: Comply with data security policies as required by both internal policies and the Data Privacy Act of the Philippines.
  • Consulting Support: Support consulting projects by analysing utilization reports and correlating this data with other data points such as death claims, onsite clinic, pre-employment tests, at least.
  • Market Research: Conduct research on identified markets and industries to support business objectives.
  • Benchmark Studies: Conduct studies on insured benefits in the market to provide benchmarks.
  • Business Reporting: Generate reports to support business operations such as, but not limited to, sales pipeline reports, revenue reviews, and renewal accounts reviews.

7. Data Analyst Overview

  • Market Research: Research the market and brand. A key part of this role will be analysis and reporting.
  • Backlog Management: Work with the Product Owner to plan and manage product backlogs.
  • Team Collaboration: Work closely with the UX designers, developers and QC, helping ensure the vision and requirements are constantly delivered.
  • Process Mapping: Understand business processes to translate business needs and requirements into documents, process diagrams and wireframes to support product development.
  • Process Analysis: Analyze, create, and validate end-to-end business processes and detail out functional specifications for the required applications.
  • Stakeholder Engagement: Working with business stakeholders to understand the business decisions/challenges that could be solved by data.
  • Data Sourcing: Working with other teams to identify data sources and extraction processes.
  • ETL Development: Developing ETL processes to generate a required outcome.
  • Process Improvement: Continuous improvement of processes and reporting.
  • Third-Party: Engagement with 3rd parties to support the agreement of external reporting requirements.
  • Solution Assurance: Ensure that all solutions are fit for operational purposes and, in particular, that the specific demands and requirements of distributed & mobile environments are fully taken into account.

8. Data Analyst Tasks

  • Report Delivery: Deliver timely, accurate commercial reporting to the business, including Sales reports.
  • Model Development: Assist with building and developing operational models to support the business.
  • Customer Insight: Have a clear understanding of the customer’s journey from end to end to identify opportunities to ensure data is traveling between all necessary agents.
  • Business Support: Support the manager in the day-to-day and strategic direction of the business.
  • Process Improvement: Identify, communicate and endeavour to resolve gaps in Industry Operations’ processes.
  • Documentation Management: Further ensuring that processes are documented, including detailed working documents for staff to follow.
  • Data Analysis: Analyse large data sets to identify gaps, mismatches in data.
  • Team Support: Supporting other team members and the Manager in the day-to-day operations of the business.
  • Compliance Adherence: Ensure that all processes adhere to all necessary compliance, including GDPR.
  • Data Visualization: Manipulate and display data to assist client insight.
  • Tool Development: Analyze, develop, and recommend tools and processes.
  • Algorithm Design: Using/creating algorithms and creating/running simulations.
  • Meeting Participation: Participate in meetings and conferences to improve business outcomes.

9. Data Analyst Roles

  • Data Execution: Responsible for the day-to-day execution of data needs from CS departments.
  • Report Generation: Generate daily operational-related data reports, analyze quantitative data, and investigate variances.
  • Tool Development: Involved in design, development, implementation, support and maintenance of reports, visualization tools, workflow automation and any other tools to facilitate business needs.
  • Task Management: Ensure assigned tasks or projects are completed in a timely manner and with high level of quality and accuracy.
  • Issue Feedback: Provide regular feedback on data operation concerns and issues.
  • Project Support: Participate and support ad hoc projects requiring additional analysis and data-heavy reporting.
  • Process Improvement: Propose improvements to operational processes.
  • Report Design: Design, develop, maintain, and publish CMS operational reports.
  • Operational Analysis: Provide operational analysis utilized for decision making.
  • Recommendation Making: Make recommendations based on the analysis.
  • Documentation Management: Develop and maintain documentation that chronicles processes related to previous analytic activities and associated results.
  • Communication Skills: Communicate with various functional areas and translate technical concepts in ways that can be understood by a variety of audiences, both verbally and in writing.

10. Data Analyst Additional Details

  • Integration Support: Support, guide and align the integration of all Programme-wide information systems and datasets with relevant stakeholders.
  • Team Leadership: Lead a team of internal and external Business Intelligence Reporting developers by providing functional support and technical guidance.
  • Capability Development: Develop individuals and the team by identifying capability, competency gaps, and finding specialist resources to deliver.
  • Requirement Specification: Specify requirements for (and improvements to) all Business Intelligence Reporting and Programme-wide Performance Management systems.
  • Stakeholder Collaboration: Work closely with other stakeholders of business systems and functional heads to incorporate end-user requirements.
  • Project Management: Build an IMS and manage on-time delivery of all BI Pipeline milestones, system operating performance, system availability, and continuous improvement that enhances all existing outputs, processes, and systems.
  • Innovation Leadership: Lead innovation and technical excellence, driving continuous improvement and industry-leading best practices.
  • Data Transformation: Collect, assess, and transform data to support the project.
  • Predictive Modeling: Derive predictive models using appropriate statistical techniques.
  • Data Verification: Ensure data supporting project recommendations is verified, analysed, and modelled effectively in collaboration with other team members.
  • Reporting Presentation: Report and present outputs to ensure clients fully understand project processes and deliverables.

11. Data Analyst Essential Functions

  • Meeting Support: Support all internal and external conference calls with Amazon by providing MI insight and minute management as directed by the Amazon Contract Manager and Senior Business Development Manager.
  • Insight Reporting: Provide proactive insight via the dashboard to Amazon on daily exception reporting.
  • Report Management: Manage and produce the proactive daily exception reporting for Amazon and distribute to all relevant key stakeholders, providing proactive insight about Amazon volume for the day (internal disruption log).
  • CS Support: Support the weekly Customer Service (CS) call between Amazon and the Amazon Contract Manager by providing MI content relating to any project actions with feedback.
  • Communication Management: Manage the online communication portal between Amazon and Hermes (Relay) and take ownership of ad-hoc collection requests from start to completion, including gaining approval with the planning, transport, and hub teams and the communication between Hermes and Amazon.
  • Escalation Handling: Ensure end-to-end management of higher-level escalations relating to transport issues, ensuring that relevant Hermes departments respond accordingly and timely to protect the client/customer experience.
  • Freight Management: Manage the online Freight Management Console between Amazon and Hermes and ensure the truck schedule is extracted weekly and provided timely to the Contract Manager by manipulating relevant data through various reports via Excel.
  • Report Maintenance: Manage and maintain daily/weekly reports as directed and required by the Senior Business Development Manager to support weekly, monthly, and quarterly business reviews.
  • Service Improvement: Make proactive recommendations to improve the client and customer experience, sometimes taking the lead on service improvements as directed by the Senior Business Development Manager.
  • Operational Planning: Deliver reports and necessary analysis to support planning and optimize the process.
  • Data Analysis: Use a data-driven approach to alert issues, highlight key points, and deliver operational analysis to leadership.
  • Team Insight: Provide the team with insights and data for daily execution.
  • Process Automation: Deep dive into the operation database and define where potential exists to suggest applying automated processes.

12. Data Analyst Role Purpose

  • KPI Planning: Work with stakeholders across departments to understand what KPIs are important from a priorities perspective and come up with a measurement plan.
  • Metric Alignment: Work with the Engineering, Product, and Marketing teams to ensure metrics are appropriately defined, implemented, and tested across digital assets.
  • Report Development: Design, build, and maintain key periodic reports, dashboards, and analyses utilizing Hadoop, Google Analytics, R, Python, Tableau, and other reporting and visualization tools.
  • Best Practices: Develop best practices and approaches to answer ad hoc questions on online user behavior, customer journeys, engagement, conversion data, and multi-channel attribution.
  • Data Analysis: Assist various testing and optimization efforts and special initiatives by performing in-depth and ad-hoc analysis of information from multiple data sources to assist data-informed decision making at all levels.
  • Report Presentation: Prepare and present reports, analysis, and presentations to various stakeholders including executives.
  • Data Architecture: Analyze existing data structures and data flow in the systems and define requirements for DWH development.
  • Requirements Gathering: Gather and analyze business needs for data and prepare requirements for DWH development.
  • Documentation Management: Maintain DWH related documentation.
  • Financial Understanding: Understand the financial process and related business operations to assure the quality of deliverables.
  • Cross-Team: Work closely with various teams (DWH, Finance, Risk, Sales, Analytics, IT) across the organization.

13. Data Analyst General Responsibilities

  • Develop and maintain automated reports from different data sources and business areas. 
  • Extract and analyze data based on ad hoc business requirements.
  • Automate data comparison between the accounting system, sales system, public data, and third-party providers.
  • Identify and remediate data differences and errors.
  • Develop and maintain ETLs from different data sources (relational databases, ERP, REST API) to DWH.
  • Maintain and release changes on AWS Aurora MySQL production database for the Financial Application Development team.
  • Participate in business-driven and BI projects.
  • Curate terabyte-scale data sets.
  • Derive insights from deep-dive analyses, bringing analytical modelling to bear on data sets which span sale- and supply-side, marketing, logistics and shopper journeys.
  • Design and build data visualisations, including reports, dashboards, and analyses to empower multi-team stakeholders at Fy! to make data-influenced decisions.
  • Collaborate in the design and analysis of rigorous statistical tests.
  • Communicate quantitative findings to teams in actionable ways.

14. Data Analyst Roles and Details

  • Report Automation: Develop and maintain automated reports from different data sources and business areas.
  • Data Extraction: Extract and analyze data based on ad hoc business requirements.
  • Data Automation: Automate data comparison between the accounting system, the sales system, public data, and third-party providers.
  • Error Remediation: Identify and remediate data differences and errors.
  • ETL Development: Develop and maintain ETLs from different data sources (relational databases, ERP, REST API) to DWH.
  • Database Maintenance: Maintain and release changes on AWS Aurora MySQL production database for Financial Application Development team.
  • Project Participation: Participate in business-driven and BI projects.
  • Data Curation: Curate terabyte-scale data sets.
  • Insight Derivation: Derive insights from deep-dive analyses, bringing analytical modelling to bear on data sets spanning sales, supply, marketing, logistics, and shopper journeys.
  • Visualization Design: Design and build data visualizations, including reports, dashboards, and analyses to empower multi-team stakeholders at Fy! to make data-influenced decisions.
  • Statistical Collaboration: Collaborate in the design and analysis of rigorous statistical tests.
  • Findings Communication: Communicate quantitative findings to teams in actionable ways.

15. Data Analyst Responsibilities and Key Tasks

  • Data Interpretation: Interpret data, analyze results and provide ongoing reports, analysis, and/or dashboards that measure product health and product OKRs.
  • Trend Analysis: Identify, analyze, and interpret trends or patterns in complex data sets to inform CGI product roadmap.
  • Use Case: Work with product management to inform use case prioritization by using data to make decisions on size, scope, and expected business impact of use case implementation.
  • Test Measurement: Set up & measure results from A/B tests using enterprise testing tools.
  • Metric Development: Help determine foundational metrics to assess product success.
  • Dashboard Automation: Build/automate dashboards and reports for consistent tracking of product usage and performance.
  • Partner Interface: Interface with Business Partners on a regular basis to understand business needs and expectations.
  • Model Building: Build data models and analyses based on agreed-upon requirements.
  • Team Collaboration: Partner extensively with other teams within and outside the Analytics & Data group such as data visualization, data science, sales and marketing teams to deliver business solutions.
  • Problem Solving: Utilize proactive problem-solving and innovative thinking to address challenges.
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