What are the best ways to ensure data-driven and evidence-based problem solving?

1. Clearly Define the Problem 2. Data Collection and Quality 3. Establish Key Metrics 4. Data Analysis Techniques 5. Visualizations 6. Cross-disciplinary Collaboration: Encourage collaboration between data experts and domain specialists. Combining data expertise with domain knowledge enhances the depth and relevance of problem-solving approaches. 7. Continuous Learning 8. Challenge Assumptions 9. Validation and Testing 10. Ethical Considerations 11. Documentation 12. Feedback Mechanisms By incorporating these practices, organizations can foster a culture of evidence-based decision-making, leading to more effective and informed problem-solving processes.

DMAIC (Define, Measure, Analyse, Improve and Control) is a proven methodology for data-driven improvements. The tool is part of a Six Sigma continual process improvement cycle, evidence is statistical based and solutions can be modelled.

There are a lot of great frameworks out there for problem solving, however, the starting point is culture. For example, if the organization currently does not trust data or only looks at data when something goes wrong then any effort or framework to do data-driven problem solving will fail. An organization must first evaluation where it is currently at with the collection and use of data and build from there. This article assumes that an organization/department is already adept at collecting and analyzing data.

In my experience, understanding the business problems to be solved. Identify the steps required for solving the problem. Review and research the available data to solve the problem. In case there are data gaps how and where to source the data from. Look at the infrastructure and architecture make sure it will support the solution. Understand the business impact of solving the problem like revenue generation, cost reduction, risk reduction.

Start by articulating the problem you want to solve in a clear and concise manner. Ensure that the problem is well-defined and specific.

While generating multiple solutions is often encouraged, I believe in the "less is more" approach. Sometimes, focusing on fewer, well-thought-out solutions leads to more efficient problem-solving and reduces decision fatigue.

Embrace Technology: Utilize data analytics and visualization tools, as well as data management systems, to streamline the data-driven decision-making process.

Stay Ethical and Compliant: Ensure that your data collection and analysis processes comply with relevant laws, regulations, and ethical standards, particularly when dealing with sensitive or personal data. Document Your Process: Keep records of your data sources, analysis techniques, decisions, and outcomes. This documentation can help you learn from past experiences and improve your problem-solving process. Foster a Data-Driven Culture: Encourage a culture of data-driven decision-making within your organization or team. This includes providing training and resources to enable others to use data effectively.