What are the most effective data sources for problem-solving?

1. Surveys 2. Interviews 3. Observational data 4. Social media data 5. Customer feedback 6. Government databases 7. Academic research 8. Industry reports 9. Sensor data 10. Financial records

Always start with the business problem, never start with the data. Data is not always designed to answer your question, so if you use the data as your boundary, you'll never be truly innovative. Start with the problem, then unpack the ideal pieces of analysis/information/insight to understand that problem... then look to see what data you have and work to resolve gaps and workarounds from there.

Define Your Problem: Clearly articulate the problem you aim to solve. Similar to outlining manufacturing issues, a well-defined problem guides data collection effectively.

The effectiveness of data sources for problem-solving depends on the nature of the problem and the specific information needed. Common data sources include internal data, external data, surveys, interviews, case studies, historical data, financial data, operational data, social media data, customer feedback, competitor data, sensor and IoT data, academic and research publications, economic indicators, health records, supply chain data, and collaborative platforms and knowledge repositories. Combining multiple sources and employing diverse data collection methods can enhance the robustness of problem-solving efforts. Choosing the most effective data sources depends on the problem's context, information needed, and reliability.

For effective problem-solving, tap into diverse data sources like real-world experiences, expert opinions, reliable articles, and trustworthy databases. Combine your personal insights with information from credible sources to form a well-rounded understanding of the problem. Have a mix of qualitative and quantitative data for a more comprehensive solution.

Identify Your Data Needs: Specify the type of data required. Much like selecting appropriate materials in manufacturing, this ensures relevance to the problem.

Evaluating your data sources for accuracy, reliability and coverage is key. You will never have full coverage for the problem but must look for every source that could add value and then evaluate. It is better to have a lower value data set with reliability and accuracy than one where this can be questioned - this will lead to constant doubts when the data is used to make decisions which is after all the goal!

Evaluate Your Data Sources: Assess potential sources for reliability and accuracy. Similar to vetting suppliers in manufacturing, trustworthy data sources are crucial.

La importancia de evaluar las fuentes: - Siempre se debe verificar de dónde provienen los datos y si es una fuente confiable. - El artículo científico es la mejor fuente de información por sobre cualquier otra, debido a que es empíricamente comprobable y cuenta con validez teórica.

Compare and Contrast Your Data Sources: Analyze strengths and limitations of each source. Like choosing the right machinery in manufacturing, this ensures optimal data selection.

Use Your Data Sources Wisely: Apply data strategically to address the problem. Similar to utilizing resources efficiently in manufacturing, effective use of data enhances problem-solving.

When selecting data sources for problem-solving, consider the following: ? Acquiring data can involve costs like purchasing from providers or conducting primary research. Assess cost-effectiveness and potential insights gained. ? Technical expertise which is crucial for data analysis, including statistical analysis and data modelling. Evaluate if the organization has the skills or consider outsourcing. ? Ethical considerations which involve privacy regulations, customer confidentiality, and data sharing transparency. Comply with regulations like GDPR or CCPA. Adhering to ethical guidelines builds trust, mitigates risks, and protects reputation.