How to Collect and Analyze Data Quantitatively and Qualitatively.Practical Lessons for Business Analysts on Not Missing What Matters
- Inna Prohorenko
- 5 дней назад
- 5 мин. чтения
Обновлено: 2 дня назад
Introduction
Today, analysts often face the same challenge: how to collect, structure, and interpret data effectively when information comes from fragmented sources and stakeholder expectations vary.
This article presents a practical case study that demonstrates how these challenges were addressed, while analyzing international donor support for Ukraine.
1. Data Collection Approach
1.1 Data Sources
- The primary data source was the official International Technical Assistance (ITA) Register maintained by the Cabinet of Ministers of Ukraine: https://www.kmu.gov.ua/diyalnist/mizhnarodna-dopomoga . The register provides structured information on registered projects, including budgets, donors, beneficiaries, and implementation timelines
- To complement the core dataset, additional sources were used:
official press releases;
reports from donor and international organizations;
open data dashboards and thematic publications;
stakeholder-provided materials;
expert interviews and survey insights.
1.2 Data Collection Methodology
A dual-source approach was applied:
1) The ITA register served as the foundation for quantitative analysis, enabling structured comparison across projects, donors, and beneficiaries.
2) Public and expert sources supported qualitative analysis by providing context — helping to understand project objectives, strategic priorities, emerging programs, and informal commitments not fully reflected in structured datasets.
Initial verification included cross-checking data across sources and validating critical information with stakeholders.
2. Data Processing and Normalization
After consolidating data into a unified Excel dataset, additional preparation was required to ensure analytical consistency.
Key steps included:
a) Currency Standardization
- Budgets reported in multiple currencies were converted into USD using a fixed reference-date exchange rate.
- Both original values and converted amounts were retained for transparency.
b) Sector Classification
- Projects were manually categorized into sectors such as infrastructure, energy, education, and healthcare.
- Classification decisions were based on project descriptions, objectives, beneficiary information, and expert consultations to ensure consistency.
c) Data Aggregation
- Projects were grouped by donors, sectors, and assistance types (grants, loans, and technical assistance).
- Additional analytical views were created for major contributors, including TOP-N donor rankings and the largest projects.
d) Structuring Unstructured Data
- Information from press releases and news sources was standardized by extracting key attributes such as date, funding amount, sector, and project status.
- Excel templates ensured uniform formatting across datasets.
3. Analytical Approach
3.1 Quantitative vs Qualitative Analysis.
Two complementary analytical perspectives were applied:
a) Quantitative analysis focused on measurable indicators - totals, shares, distributions, and concentration patterns — supported by visualizations such as bar charts, pie charts, histograms, heatmaps, and analytical tables.
It enabled:
- calculation of donor and sector shares;
- identification of funding concentration patterns (e.g., 80% of funding concentrated in 20% of projects).
b) Qualitative analysis added interpretation and context by examining donor strategies, policy decisions, and drivers behind funding changes.
Outputs included analytical summaries, diagrams, and executive-level insights.
3.2 Standard Visualization Techniques for Business Analysts
Recommended Visualization usage:
1) Bar charts — comparing values and ranking categories.
2) Pie charts — showing proportional structure with a limited number of categories.
3) TreeMaps — representing complex structures with many categories.
4) Pareto charts — identifying concentration and key drivers.
5) Histograms — analyzing distributions and detecting outliers.
6) Heatmaps — comparing intensity across multiple metrics simultaneously.
3.2.1 ТTop-N Analysis
Top-N analysis focuses on identifying the most significant elements (donors, sectors, or projects) based on selected criteria such as funding volume, number of initiatives, or overall impact.
Application in the project:
- Building tables and visualizations highlighting the TOP-10 donors by funding volume and number of projects.
- Visualizing the share of TOP contributors within the total dataset (for example, the TOP-6 donors accounted for approximately 80% of total funding).
Example visualizations
a) TOP-10 donors by funding volume (bar chart)
b) TOP-10 donors by number of projects (bar chart)
c) Table showing TOP-6 donors contributing 83% of total funding
d) Table showing TOP-6 donors covering 80% of total projects:
Benefits:
- Quickly illustrates resource concentration.
- Helps stakeholders focus on the most influential contributors.
3.2.2 Pareto Principle (80/20 Rule)
The Pareto principle suggests that roughly 80% of outcomes are driven by 20% of contributing factors. In financial analysis, this often translates into the majority of funding being provided by a limited number of donors or projects.
Application in the project:
- Analysis showed that approximately 80% of all projects were initiated by five major donors.
- The effect was visualized using cumulative charts (Pareto charts).
Example visualizations. Pareto chart illustrating cumulative contribution distribution:
Benefits:
- Identifies key leverage points for optimization.
- Supports evidence-based prioritization and monitoring decisions.
3.2.3 Gaussian (Normal) Distribution Analysis
Gaussian distribution analysis evaluates how data is distributed around an average value, helping determine whether datasets contain outliers or follow consistent patterns.
Application in the project:
- Histograms were built to analyze project budget distribution.
- Results demonstrated that most projects had relatively small budgets, while the majority of funding was concentrated in a small number of large initiatives (“long-tail” effect).
Example visualization. Histogram showing project budget distribution.
Benefits:
- Helps identify anomalies and atypical projects.
- Explains why average values alone may not accurately represent the dataset.
3.2.4 Funding Structure vs Project Quantity Analysis
This approach compares two analytical dimensions simultaneously:
- total funding volume,
- number of projects across sectors.
The goal is to identify imbalances between financial allocation and sector activity.
Application in the project:
- A TreeMap visualized funding distribution across sectors (block size representing budget volume).
- A Pie Chart illustrated the number of projects per sector (in absolute numbers and percentages).
- Comparing both visualizations helped identify sectors with high funding but relatively few projects — and vice versa.
Example visualizations
a) TreeMap showing sector funding distribution:
b) Pie chart showing project distribution by sector:
Benefits:
- Provides visibility into both financial concentration and sector relevance.
- Improves decision-making by combining two analytical perspectives.
- Helps communicate funding balance clearly to stakeholders.
Testing alternative formats was part of the analytical process; final selections prioritized clarity and stakeholder interpretability.
4. Stakeholder Engagement
Stakeholder collaboration was integrated throughout the analysis process.
- Expert consultations provided domain context.
- Cross-source validation improved data reliability.
- Review sessions with client and internal teams refined analytical focus.
- Full documentation ensured transparency and reproducibility.
Executive summaries were prepared to enable quick decision-maker onboarding
Challenges and Lessons Learned
- Fragmented data required combining multiple sources and manual structuring.
- Lack of standardization led to the development of a custom classification framework.
- Rapidly changing conditions required updating datasets immediately before final reporting.
6. Key Takeaways for Business Analysts
a) A hybrid data approach — combining structured registries with public and expert sources — enables comprehensive analysis even in complex environments.
b) Data normalization and classification remain critical analyst responsibilities, even when automation tools are available.
c) Continuous stakeholder engagement improves both data quality and trust in analytical outcomes.
d) Documented methodology ensures transparency and future reuse.
e) Equally important is choosing the right visualization. Charts are not decoration — they shape interpretation and decision-making. The right format helps stakeholders quickly identify patterns, imbalances, and strategic priorities.
f) Ultimately, the role of a business analyst is not simply to present data, but to transform it into actionable insight: clarifying what matters, why it matters, and what should happen next.
7. A Practical Tip for Business Analysts
Clients rarely see the effort behind data collection and preparation — they see conclusions.
Start with the executive summary.
Decision-makers often lack time to read detailed reports but need clear insights to act. A strong summary should answer three questions:
What matters?
Why does it matter?
What should be done next?
Art of Business Analysis training schedule
News and articles on business analysis:
Комментарии