Data analysis and business intelligence

What do we mean by data analysis and business intelligence?

The process of collecting, analysing, and presenting business data to support decision making.

Knowledge 

• Contextual awareness.
• Understanding of statistical concepts.
• Understanding of technical tools and languages to support data analysis.
• Understanding of data platforms and supporting technologies.
• Knowledge of data analysis best practice.
• Knowledge of the council’s service areas to ensure data can be interpreted meaningfully.
• Understanding of different project management approaches including Waterfall and Agile.

Skills 

Able to: 

• Think strategically: Ensuring analysis outputs align with the overall strategic objectives of the organisation and focus on the insights that matter most.
• Understand user needs / elicit requirements: Working with end users to determine the insights needed. 
• Analyse and prioritise requirements: Ensuring requirements are prioritised in support of business objectives, considering dependencies. 
• Manage stakeholders: Identifying and working with stakeholders to understand their needs, drivers, and priorities. 
• Collect and prepare data: Bringing data together from multiple sources and ensuring cleanliness ahead of data processing.
• Apply technical understanding: Including proficiency in BI tools (e.g. Tableau, PowerBI) and programming languages (e.g. Python, R, SQL) to query data and create reports / interactive dashboards. 
• Analyse data: Use tools and techniques to identify patterns and relationships, to forecast and analyse trends to extract meaningful insights.
• Present data accessibly: Ensuring user needs are reflected and that visualisations are accessible, unambiguous, and usable.
• Apply statistical concepts: To ensure that data is interpreted objectively and that any biases are clear. 
• Think critically: To ensure consideration of assumptions, and the robustness of the analysis.
• Communicate and collaborate effectively: With stakeholders at all levels, including technical and non-technical teams.
• Solve problems: Working as part of multi-functional teams to manage data issues and to find solutions.
• Manage risk: Understand the risks associated with data analysis and any risks created by data insights themselves, ensuring these are reflected in the risk register appropriately and effectively mitigated within risk appetite.