Data Cleaning and Preparation: This involves filtering the data, handling missing values, and preparing the dataset for analysis to ensure accuracy and relevance.
Data Exploration and Analysis: Analysts use statistical tools and techniques to explore and analyze data, identifying patterns, relationships, and trends.
Data Visualization: They create visual representations of data findings through charts, graphs, and dashboards to make the data understandable at a glance.
Reporting: Data analysts prepare reports and presentations to communicate the insights and findings from the data to stakeholders, which can influence policy and decision-making processes.
Collaboration: They often work with other departments to understand their data needs and help them make informed decisions based on data insights.
Gather data from primary and secondary sources, ensuring the upkeep of databases and data systems.
Detect, examine, and decode trends or patterns within intricate datasets.
Coordinate with management to align business and informational priorities.
Identify opportunities for process enhancements.
Employ statistical techniques to scrutinize data and produce actionable business insights.
Develop data dashboards, charts, and visual aids to support decision-making for different Products
Convey insights through both reports and visual presentations.
Engage with managers from various Products to specify data requirements for analysis projects tailored to their unique business processes.
Providing advice and decision support in every aspect of the process landscape his/her role covers.
Analyse new data analytics requirements and translate them into technical requirements, project plans and milestones.
Identify automation, simplification, standardization, and improvement opportunities across the process.
Collaborate with then Data Analytics network within Product and larger MAERSK organisations that harnesses subject matter expert knowledge of our data products and ensure best practices.
Propose and implement continuous improvement initiatives within the business analysis process, including process mining, automation, and data science.