Analysis
In digital business engineering, analysis is the bridge between a strategic idea and a viable digital solution that actually creates value. It helps you understand
- what kind of digital product, service, or process change is needed,
- and how it should behave within the wider digital ecosystem before significant time and budget are committed.
- and how it should behave within the wider digital ecosystem before significant time and budget are committed.
For a digital business engineer, analysis is primarily a way to steer innovation and control risk. Without solid analysis, organizations risk investing in the wrong digital initiatives (because underlying needs and value drivers are unclear), facing scope creep and shifting priorities, overshooting time and budget, or discovering technical, data, or compliance issues too late. A thorough analysis process uncovers the real business and stakeholder needs, translates them into clear and shared requirements, and makes constraints, dependencies, and potential conflicts visible early on.
Analysis for a digital business engineer spans multiple dimensions:
- Business and value analysis: identifying stakeholders, objectives, and value propositions; understanding how the solution contributes to the business model, customer journey, and organizational strategy.
- Requirements analysis: translating business and user needs into clear, testable functional and non-functional requirements (e.g. performance, security, privacy, scalability, usability).
- Process and data analysis: mapping current and future processes, customer journeys, data flows, and data structures to ensure that information is accurate, consistent, and usable for decision-making and automation.
In agile digital environments, analysis is an ongoing activity: ideas are refined into epics and user stories, acceptance criteria evolve, and feedback from experiments and increments shapes new insights. In more plan-driven or regulated contexts, more analysis is done up front to define clear business cases and specifications. In all cases, effective analysis by a digital business engineer relies on intensive stakeholder collaboration, clear and visual communication, and the ability to translate complex business–technology problems into understandable models, decisions, and actionable roadmaps.
Starting Points
- Problem Analysis
- Requirements Analysis
- Literature-research
- Business Case
- Organizational Structure
- Return On Investment (ROI)
- Data Analysis
- DESTEP Analysis
Key Points
- You start by translating the business or digital innovation challenge into a clear, open research question that reflects the underlying needs of stakeholders.
- You choose method(s) to answer the question with a solid rationale.
- You collect qualitative and/or quantitative data in a methodological way, using clear inclusion and exclusion criteria that match the business domain and target groups.
- You preserve the raw data you collected so that your analysis steps can be retraced and validated if needed.
- You analyze the data to extract insights that are relevant for digital business decisions, such as value potential, risks, feasibility, and impact on processes, customers, and technology.
- You answer the research question using the results from your analysis, translating them into clear, actionable recommendations for digital solutions, interventions, or next steps.