Conduct Business Intelligence Data Collection and Management

This standard defines the competencies required to effectively collect, store, and manage data for Business Intelligence (BI) purposes. It covers sourcing, cleansing, and organising data, ensuring its integrity and security, and implementing robust data governance practices. It includes integrating real-time data sources and their critical role in modern BI practices. These competencies are vital for maintaining reliable, high-quality data that underpins accurate, actionable BI analyses.

This standard is designed for professionals responsible for collecting, organising, and managing data to maximise the effectiveness of BI tools and technologies in supporting informed decision-making as part of their workplace responsibilities.

1. Identify, assess, and collect relevant data from internal and external sources in line with organisational BI objectives and data needs.
2. Transform, clean, and pre-process data in line with organisational data quality standards in readiness for BI analysis.
3. Store data securely in appropriate data environments, to maintain accessibility, scalability, and performance in line with organisational procedures.
4. Implement data governance policies to maintain data integrity and compliance with ethical handling, organisational, and regulatory standards.
5. Monitor and review data sources to verify that data remains accurate, relevant and up to date, in line with evolving organisational needs.
6. Maintain data documentation, including data sources, lineage, and collection methods, to support transparency, traceability, and efficient data reuse across BI projects.
7. Collaborate with stakeholders to define data requirements and validate that collected data meets organisational BI objectives and priorities.

1. Data sourcing methods, including APIs (Application Programming Interface), databases, flat files, and third-party data providers.
2. Data storage solutions (including spreadsheets, databases, data warehouses, and data lakes).
3. Data pre-processing techniques, including data cleaning, transformation, and normalisation methods, to promote high standards of data quality, consistency, and formatting across datasets.
4. How to perform data extraction and transformation activities to prepare data for analysis, ensuring accuracy, consistency, and format compatibility.
5. Steps and techniques for data cleaning and validation, including handling missing values, duplicates, and outliers, in maintaining data quality.
6. Data monitoring methods and techniques to maintain data integrity.
7. Data governance and regulatory requirements, including data privacy laws, and their implications on BI data management.
8. Best practices for data security in handling, sharing, and storage, including access controls, encryption, secure transfer protocols, and adherence to organisational and regulatory standards.
9. industry-standard tools for data collection and management, including ETL (Extract, Transform, Load) tools, data integration platforms, and data validation software.
10. Principles of metadata management and data lineage to support BI transparency and traceability.
11. Effective stakeholder collaboration and requirement gathering for BI data collection and management.