The following is a selection of some commonly used analysis and analytics techniques applicable to the Identify the Research Questions domain. The list of techniques does not represent a comprehensive set of techniques used by an analyst in the Identify the Research Questions 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
Business Model Canvas	Used to understand how the enterprise produces value by analyzing the key component and business eco-system. It can be modified to include a data view on how data is used by the enterprise at a high level.	Chapter 10.8
Concept Modelling	Used to organize business vocabulary and their interrelationships. This can be used as a starting point to identify and validate data requirements as well as to correlate a business need for research questions.	Chapter 10.11
Decision Modelling and Analysis	Used to establish a decision hierarchy by capturing the flow of decisions that are commonly undertaken in an organization using various tools such as decision trees, tables, decision networks.	Chapters 10.16 and 10.17
Document Analysis	Used to understand the context through minutes of business meetings, internal reports, reports from other organizations, academic literature, previous analytics project reports, the data and methodology employed, the statistical results, and the subsequent business decisions.	Chapter 10.18
Interviews and Workshops	Used to understand the business problem or opportunity from the perspective of different stakeholders, or the organization as a whole, and to elicit more concrete research questions that contribute to the business problem or opportunity.	Chapters 10.25 and 10.50
Metrics and Key Performance Indicators (KPIs)	Used to measure the performance of the organization in different functional area or business goals. Many analytics engagements are undertaken to optimize or explain the KPIs and metrics.	Chapter 10.28
Organizational Modelling	Used to understand the organization’s capabilities, resources, and group structures to discover levels of data insights and research questions posed by different groups in the context of their roles and capabilities.	Chapter 10.32
Prioritization	Used throughout the business data analytics effort to focus attention on the most urgent items. For example, when determining what is important to resolve, formulating research questions, sharing insights, and recommending actions.	Chapter 10.33
Process Modelling and Analysis	Used to understand the organization’s processes where data is generated, and consumed, and to discover ways to identify improvement opportunities where analytics solutions may improve the business value.	Chapters 10.34 and 10.35
Risk Analysis and Management	Used to identify and manage any risks originating from a specific course of action, decision, or assumption that affect analytics engagement.	Chapter 10.38
Root Cause Analysis	Used to understand the business problem by systematically examining the probable causes to develop an intuition for the nature of research questions.	Chapter 10.40
Scope Modelling	Used to understand the scope and boundaries of the analytics engagement and the external context within which a future analytical solution may operate.	Chapter 10.41
Stakeholder List, Map, or Personas	Used to understand stakeholders and their characteristics with additional focus on how they generate and consume data and insights.	Chapter 10.43
Exploratory Data Analysis	Used to quickly understand the readily available data and insights to build intuition about the analytics problem and any contributing factors.	N/A
Hypotheses Formulation and Testing	Used to develop a premise for a particular result based on business stakeholder or SME’s opinion which is statistically justified through the data to formulate research questions accurately.	N/A
Problem Reframing and Shaping	Used to facilitate deliberate thinking about a specific business or research problem from multiple perspectives such as stakeholder, market, or customer. Problems are restated for an analytics initiative. For example, if the analytics problem relates to identifying high-value customers of an organization for a custom marketing campaign, understanding what high-value means and reframing the problem. In this case, it may mean lifetime value, highest spending, or customers interested in high- value purchases.	N/A