2.1.6 Plan Business Data Analytics Approach

Planning the Business Data Analytics Approach defines how analytics work will be performed. When planning a business data analytics approach, analysts:

• determine the capabilities and capacity of the organization to perform analytics so the team understands what is realistically possible,
• identify “quick wins” versus longer-term efforts,
• determine the type of analytics questions being asked for (descriptive, diagnostic, predictive, or prescriptive), and
• maintain traceability of business needs, objectives, research questions, and their sources (for example, stakeholders who asked the research questions or analysis that pointed to specific research questions).

Planning is an iterative process and changes to the approach are made as new knowledge is gained. Each of the six business data analytics domains includes an element of planning which may influence the overall approach to analytics.

There is no right or wrong answer as to the degree of formality of the business data analytics approach. Some organizations may choose to formally document the decisions made when defining their approach by using a business data analytics planning template. Other teams may choose to build more visual models to capture the decisions and include the information on shared wikis and within the team's workspace.

When planning the business data analytics approach, analysts use techniques such as brainstorming to quickly identify a list of activities needed to be performed, functional decomposition to break down high-level concepts into lower-level tasks, and estimation to assess how long it may take to complete various activities. Analysts planning the business data analytics approach use facilitation, leadership skills, and negotiation skills to obtain stakeholder consensus.

Planning Business Data Analytics Approach at Various Stages When research questions or the solution approaches involve more complexity than anticipated, analytics initiatives are typically implemented in multiple stages of maturity: A proof of concept stage which focuses primarily on feasibility of the analytics approach. A pilot stage which focuses on limited scale solution to discover integration and quality issues. A production stage which focuses on business value for customers or internal stakeholders. Deployment Stage Level of Formality Assessment of Organizational Capabilities and Resources Planning Outlook Data Characteristics Proof of Concept Low Qualitative assessment of capabilities Independent and agile teams Low governance Only key stakeholder engagement Flexible solution and data architecture Near-term Static and limited data without data pipelines from different sources Noisy but consciously curated Pilot Medium Both qualitative and measurable assessment Larger teams with cross- functional capabilities Some governance structure defined Most stakeholders are identified and engaged Solution and data architecture are defined Mid- to long-term Dynamic data integrated to most of the known data sources Usable data with defined transformation procedures Production High Both qualitative and measurable assessment Larger teams with cross- functional capabilities Governance structure deployed Most stakeholders are identified and engaged Solution and data architecture are implemented and integrated to enterprise architecture and data strategy and governance Long-term Dynamic data integrated to most of the known data sources Usable data with defined transformation procedures