2.2 Tasks
2.2.5 Select Techniques for Source Data
Guide to Business Data Analytics
The following is a selection of some commonly used analysis and analytics techniques applicable to the Source Data domain. The list of techniques presented does not represent a comprehensive set of techniques used by an analyst in the Source Data domain but presents a small, but useful, set of techniques that can be used.
| Techniques | Usage Context for Business Data Analytics | BABOK® Guide v3.0 Reference |
| Acceptance and Evaluation Criteria | Used for determining the correct data and to validate data by understanding applicable criteria from both business and technology perspectives so that the right data can be sourced. | Chapter 10.1 |
| Data Dictionary | Used to create a dictionary of terminologies to describe the data labels that can be applied consistently to prepare datasets which can be used throughout the life cycle of an analytics initiative. | Chapter 10.12 |
| Data Flow Diagrams | Used to understand the conceptual or logical view of the data being collected and stored within data sources used for planning and data validation. | Chapter 10.13 |
| Data Modelling | Used to organize data elements and their interrelationships in conceptual, logical, and physical form in order to identify and validate the right data sources. | Chapter 10.15 |
| Document Analysis | Used to gather information about various internal source systems. | Chapter 10.18 |
| Interface Analysis | Used to understand how data is captured and stored in relevant data sources. Such analysis is useful in cases where an interface may request multiple data elements but stores information differently. | Chapter 10.24 |
| Non-Functional Requirements Analysis | Used for identifying and analyzing quality and governance attributes such as privacy, volume, frequency, retention, integrity, and constraints related to data sources to formulate a data collection plan. | Chapter 10.30 |
| Survey or Questionnaire | Used as a form of actively collecting data which may not be available readily but may be required for the analytics initiative. | Chapter 10.45 |
| Data Mapping | Used to develop traceability between data elements and data sources with the data owner, availability, frequency, constraints, assumptions, transformations, and extraction/ collection methods to build a reference for data collection. | N/A |
| ETL and Data Management Techniques | Used to extract and curate required data without compromising or changing data that is needed for ongoing business operations. | N/A |
