How do you deal with incomplete or unreliable data in your competitive analysis?

1 Assess the data quality

In order to assess the quality of data you have or can access, you can use various criteria such as relevance, accuracy, timeliness, completeness, consistency, and validity. Ask yourself if the data is relevant to your research question and objectives, if it is accurate and free from errors or biases, if it is current and updated regularly, if it is complete and covers all the aspects you need to analyze, if it is consistent and comparable across different sources and methods, and if it is valid and based on reliable sources and methodologies. By assessing the data quality, you can identify any gaps, limitations, or risks in your data collection and analysis process.

2 Fill in the gaps

The next step is to fill in the gaps in your data as much as possible. You can use secondary research to look for alternative sources of data, such as industry reports, market surveys, customer reviews, or academic studies. Additionally, you can conduct primary research with tools like SurveyMonkey or Qualtrics to collect data directly from your target market. Estimation techniques, such as benchmarks, ratios, averages, or projections, can also be used to infer data from available information. By filling in the gaps, you can increase the completeness and accuracy of your data and reduce the uncertainty in your analysis.

3 Validate the data

The third step is to validate the data you have or have collected. You can use various methods to check the reliability and consistency of your data, such as triangulation, corroboration, and sensitivity analysis. Triangulation involves comparing and contrasting data from different sources, methods, or perspectives. Corroboration looks for evidence or confirmation that supports or contradicts your data. Sensitivity analysis tests how your data or results change when you vary certain assumptions, parameters, or scenarios. By validating the data, you can increase its credibility and reduce errors or biases in your analysis.

4 Communicate the data

The final step is to communicate the data you have analyzed in a clear and concise way. You can use tables to organize and display your data in rows and columns, making it easy to compare and contrast different values or categories. Charts can illustrate and highlight your data in graphical forms, making it easy to see patterns, trends, or relationships. Frameworks can also be used to structure and simplify your data in logical or strategic ways, making it easy to understand and apply. For example, SWOT, Porter's Five Forces, or Value Proposition Canvas can be used to analyze strengths, weaknesses, opportunities, threats, competitive forces, or value propositions. By communicating the data effectively, you can increase its clarity and impact for your audience.