PPDAC Model

A data problem solving framework, which is used where data forms a large part of a project, for responding to RFIs from customers, or for statistical analysis. It can help to reinforce the importance of data in making effective decisions.

The conclusion will often provide insight and solutions for the initial problem or aim but will also involve the posing of new questions for other problems to solve. The model can be thought of as a continuous development cycle.

• Problem: This needs to be clearly defined and explained how it is to be solved.
• Planning: What is the design plan? Methodology for design of the project? Methods (techniques) for data gathering? How is the problem to be measured? What sampling process is to take place? Are there any ethical considerations regard data collection? What is the timescale of the project? Is it a realistic proposition or does it need reframing?
• Data: How is data to be obtained (collected)? How is it to be managed once received? What is to be done about poorly completed data returns (i.e. data cleansing).
• Analysis: What type of analyses are to be carried out? Identify trends, correlations, relationships etc. between data. Validate findings from analysis. Has the problem been answered - fully or partially?
• Conclusion: Evaluate and discuss your findings and produce a conclusion, along with recommendations if appropriate. Create a new question for problem solving if findings have identified another area for exploration. The conclusion, which will often be a report or short document, should then be communicated to others. How will this be presented? How is it to be communicated?

(References:
Statistical Analysis Handbook 2018 edition: 2.1 The Statistical Method
https://www.statsref.com/HTML/index.html?statistics__statistical_analys.html

Imed Bouchrika, 'How to Write Research Methodology: Overview, Tips, and Techniques', posted October 16, 2020. https://www.guide2research.com/research/how-to-write-research-methodology)