Our Methodology: Phase II – Research & Analyse
Transforming Questions into Actionable Intelligence
Following the strategic foundation laid in “Compose & Assess,” the “Research & Analyse” phase is where we activate our interdisciplinary toolkit to generate the decisive intelligence that powers evidence-based decision-making. This is the engine of discovery, moving from a well-defined problem to a deep, data-driven understanding of its mechanics, drivers, and potential solutions.
Why “Research & Analyse” is the Engine of Discovery
A map of the landscape is useless without the means to navigate it. The “Research & Analyse” phase provides the compass and the propulsion. It is designed to move beyond assumptions and anecdotes, replacing them with empirical evidence and validated insights. This phase ensures that our strategies are:
Empirically Grounded: Built on data collected through rigorous, methodologically sound research.
Predictively Powerful: Leveraging analytical models to forecast outcomes and simulate scenarios.
Objectively Verified: Testing hypotheses against reality to confirm or refute their validity.
This phase answers the critical questions: What is actually happening and why? What does the data prove? What are the most impactful levers for change?
Deconstructing the Phase: Our Analytical Process
The “Research & Analyse” phase is a dynamic process of targeted data collection and sophisticated interpretation, executed through three interconnected streams of activity.
- Targeted Data Generation & Collection
We deploy a suite of methods to gather the specific data required to test our hypotheses. This is not about collecting data for its own sake, but about building a bespoke evidence base.
For Societal & Behavioural Insights: We design and conduct surveys, focus groups, in-depth interviews, and deliberative workshops. This allows us to quantify public perception, measure societal readiness, and understand the underlying values, barriers, and motivations of key stakeholder groups.
For Technical & Environmental Systems: We collect data from sensors, laboratory experiments, public databases (e.g., LCA inventories, climate data), and industrial processes.
For Economic & Market Dynamics: We gather market data, cost structures, regulatory filings, and trade flows.
- Advanced Interdisciplinary Analysis
This is where raw data is transformed into intelligence. We apply a powerful, integrated analytical framework:
AI-Driven Data Analysis: We use machine learning and natural language processing to uncover hidden patterns in large, complex datasets—from social media sentiment to sensor networks. This helps identify non-obvious correlations and predict trends, such as forecasting technology adoption curves or mapping the diffusion of policy ideas.
Economic & Techno-Economic Modelling: We build models to assess the financial viability, cost-benefit trade-offs, and scalability of new technologies or policies. This answers questions about long-term economic impact and return on investment.
Statistical & Qualitative Analysis: We employ robust statistical techniques (e.g., regression analysis, factor analysis) to validate survey results and test hypotheses. Simultaneously, we use qualitative coding and thematic analysis to derive rich, contextual insights from interviews and open-ended responses.
- Synthesis for Decision-Making
The final step is the synthesis of all analytical threads into a coherent narrative of understanding. We integrate quantitative findings with qualitative context to avoid a one-dimensional view.
Our Analytical Goal: To produce a clear understanding of causal relationships, market dynamics, and societal impacts. We don’t just report that “60% of respondents are concerned about climate change”; we explain why they are concerned, how it influences their behaviour, and what policy interventions would be most effective based on their demographic and psychographic profile.
The Balancevo Difference: Bridging the Quantitative-Qualitative Divide
Our unique advantage in this phase is the seamless integration of social science rigor with data science power. We never see them as separate.
In an EU project on “Social Acceptance of AI in Public Services,” we wouldn’t just model adoption rates (quantitative). We would use NLP to analyze policy documents and public discourse (quantitative), and then conduct focus groups to explain the reasons behind the distrust uncovered by the model (qualitative).
For a Techno-Economic Assessment of a new green hydrogen technology, we model the Levelized Cost of Hydrogen (LCOH) but also integrate a social readiness assessment to forecast potential supply chain or skills-gap barriers that could impact the economic model.
This interdisciplinary synthesis ensures the “decisive intelligence” we generate is not just statistically significant, but also contextually rich and strategically invaluable.
Outcome & Deliverables
The conclusion of the “Research & Analyse” phase is a comprehensive Evidence and Intelligence Report. This deliverable goes beyond raw data to provide:
Validated insights into user needs and market/societal dynamics.
Forecasted impacts and scenario analyses.
Statistically tested relationships between key variables.
A clear, evidence-based justification for the strategic choices to be made in the next phase.
This report becomes the undeniable factual core upon which successful strategies, compelling EU proposals, and impactful innovations are built.
Ready to move from assumptions to evidence?
[Contact us to leverage our “Research & Analyse” capabilities for your project’s success.]